Abstract
Cervical cancer is the fourth most common malignant tumor in women, lacking specific and sensitive markers. TP53 induced glycolysis regulatory phosphatase (TIGAR) plays an important role in the initiation and progression of several solid tumors. This study aims to investigate the diagnostic and prognostic and immunotherapeutic role of TIGAR in cervical cancer. We observed that TIGAR expression was upregulated in cervical cancer cells and tissues using experiments and bioinformatics analyses. Moreover, elevated TIGAR expression was associated with poor prognosis using Kaplan–Meier survival analysis, and its diagnostic value was confirmed using the Receiver operating characteristic (ROC) curve. We also analyzed the correlation between TIGAR expression and immune infiltration and assessed its correlation with a clinical cohort of cervical cancer, and found that TIGAR expression was significantly associated with the infiltration levels of Foxp3 + Treg cells, CD11b + MDSC, CTLA4, and HAVCR2. TIGAR is a potential diagnostic and prognostic biomarker of cervical cancer and is expected to offer new possibilities for cervical cancer immunotherapy.
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Introduction
Cervical cancer, the 4th women’s malignant tumor, is a common cause of malignancy-related deaths in women, occurring mainly in low- and middle-income countries1,2. According to the clinical stage and pathological risk factors, the common treatments for cervical cancer are surgery or a combination of radiotherapy and chemotherapy. Due to the heterogeneity of tumors, personalized therapies, such as targeted therapy and immunotherapy, have also been applied in the treatment of patients with advanced, recurrent or specific biomarker-positive in cervical cancer3,4,5. Although many clinical trials and academic studies have been conducted on the use of immunotherapy to control cervical cancer, few drugs are currently approved for clinical treatment. Therefore, there is an urgent need to identify new biomarkers for cervical cancer detection, prognostic assessments, and promising targeted immunotherapy6.
Cervical cancer is an immunogenic tumor caused by persistent human papillomavirus (HPV) infection, smoking, long-term use of oral contraceptives, and immunosuppression. Tumor cells escape immune checkpoints through a series of mechanisms7. HPV can disrupt the recruitment of natural killer cells (NK), T cells, Langerhans cells, and dendritic cells (DC) to tumor cells by downregulating host cell adhesion molecules and Toll-like receptors8,9. HPV can also reduce the expression of MHC-I molecules and antigen processing-associated transporters (TAP), which interfere with antigen presentation10, increasing the risk of disease progression and malignant transformation. The tumor microenvironment (TME) refers to the interactive environment formed by tumor cells and various surrounding immune cells and plays an important role in the progression and metastasis of various tumors, including cervical cancer11,12. The interactions between tumor and immune cells are complex and variable, and some infiltrating immune cells and immunosuppressive molecules have potent tumor-promoting abilities, promoting immune escape, EMT, and lymph node metastasis13. However, the impact of the interaction between cancer and immune cells on the progression and metastasis of cervical cancer remains unclear.
Glucose metabolism is one of the main sources of energy for maintaining normal physiological functions in mammals, including three pathways in mammalian cells: oxidative phosphorylation under aerobic conditions, glycolysis under anaerobic conditions, and pentose phosphate pathway (PPP), which generates pentose phosphate. TIGAR is similar to fructose-2,6-bisphosphatase in structure and function; it hydrolyzes fructose-2,6-bisphosphate to fructose-6-phosphate, inhibits glycolysis, promotes PPP, increases the production of NADPH and ribose-5-phosphate, and decreases the level of reactive oxygen species (ROS) in the cell, which is conducive to the maintenance of cellular redox homeostasis and promotion of nucleotide synthesis and DNA repair14. TIGAR has been shown to be highly expressed in various solid tumors to promote cancer cell survival15,16,17,18,19,20,21,22,23, which may be related to the fact that TIGAR reduces ROS, decreases DNA damage and improves DNA repair24. However, the expression and prognosis in patients with cervical cancer remain unclear. One study found that inhibition of TIGAR by siRNA in HeLa cells increased cell sensitivity to common chemotherapeutic drugs25, suggesting that TIGAR could be a potential therapeutic target for cervical cancer; however, further clinical validation is not yet available. In addition, the role of TIGAR in the tumor microenvironment of cervical cancer remains unclear. Therefore, it is necessary to evaluate the diagnostic and prognostic value of TIGAR in cervical cancer and explore its immunological role in the tumor microenvironment.
In this study, we analyzed TIGAR expression levels in cervical cancer tissues and cells and investigated its importance in clinical diagnosis and prognosis through experiments and bioinformatics analysis based on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to explore the potential biological role of TIGAR in cervical cancer. Immune infiltration analysis was performed using the TISIDB and Xiantao platforms to assess the correlation between TIGAR levels and the infiltration of immune lymphocytes and immunomodulatory molecules in cervical cancer, and the correlation was validated in clinical specimens. These results suggest that TIGAR may be a tumor marker and a potential therapeutic target for cervical cancer. In addition, these results provide a new perspective on the role of TIGAR in the tumor microenvironment and reveal its potential function in the progression of cervical cancer, which is expected to provide new possibilities for immunotherapy in cervical cancer.
Results
Study workflow
The entire working processes are shown in Fig. 1.
Workflow of the study.
Expression, ROC curve and survival analysis of TIGAR in cervical cancer
We assessed the expression level of TIGAR mRNA in pan-cancers (33 cancer types) from TCGA and GTEx databases using the Xiantao platform and found that TIGAR was upregulated in 26 tumors (Fig. 2A). TIGAR mRNA levels were significantly higher in cervical cancer tissues than in normal tissues (P < 0.001) (Fig. 2B). The ROC curve showed an area under the curve (AUC) of 0.933 (Fig. 2C), suggesting that TIGAR has good diagnostic value for cervical cancer. Cervical cancer patients with low TIGAR expression had longer OS (overall survival) by the GEPIA2 database (Fig. 2D). OS (Overall Survival) refers to the time from the diagnosis of a patient with a disease until death from any cause.
Expression, ROC curve and survival analysis of TIGAR in cervical cancer in TCGA and GTEx. (A) TIGAR mRNA levels in pan-cancer using TCGA and GTEx datasets. ACC, Adrenocortical carcinoma; BLCA, Bladder Urothelial Carcinoma; BRCA, Breast invasive carcinoma; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; 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; KIRC, Kidney renal clear cell carcinoma; 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. Cervical cancer data is circled by a red box. (B) TIGAR mRNA levels in cervical cancer tissues and normal cervical tissues from TCGA and GTEx datasets. (C) Diagnostic value of TIGAR in cervical cancer. (D) Kaplan–Meier survival curves was estimated based on TIGAR high expression (red) and low expression (green) in cervical cancer tissues in TCGA datasets using GEPIA2. *P < 0.05; **P < 0.01; ***P < 0.001.
TIGAR protein levels were significantly higher in cervical cancer tissues than in paracancerous tissues (Fig. 3A), and the statistical results was shown in Fig. 3C (P < 0.05). The level of TIGAR protein in cervical cancer cells was higher than that in normal cervical epithelial cells (Fig. 3B), and the statistical results was shown in Fig. 3D(P < 0.05).
TIGAR was upregulated in cervical cancer tissues and cells. (A) TIGAR protein levels in human cervical cancer tissues and paracancerous tissues from 3 cervical cancer patients. (B) TIGAR protein levels in HcerEpic (human cervical normal epithelial cells) and cervical cancer cells (SiHa, HeLa, C33A). (C,D) are statistical plots for (A,B), respectively. P, Paracancerous tissues; C, Cervical cancer tissues. *P < 0.05, **P < 0.01, or ***P < 0.001 were considered significant differences.
Correlation between TIGAR expression and clinical parameters in patients with cervical cancer
To further determine the clinical significance of TIGAR expression in cervical cancer, we collected the gene expression levels of 306 cervical cancer tissues, three paracancerous tissue samples, and clinical information from the TCGA database. Clinical data included basic patient information, tumor-node-metastasis (TNM) stage, clinical stage, histologic grade, and primary therapy outcomes. The results showed that TIGAR expression in cervical cancer correlated with patient age and tumor T-stage, and no significant differences were observed in other clinical features (Table 1). We performed further validation in TCGA database using the Xiantao online tool, which showed that TIGAR expression in cervical cancer was higher in patients older than 50 years than in those younger than 50 years, and higher in T2&T3&T4 than in T1 (Fig. 4).
Correlation between TIGAR expression and clinical parameters in patients with cervical cancer: (A) Age, (B) T stage, (C) N stage, (D) M stage.
The PPI and functional enrichment of TIGAR in cervical cancer
To identify the relationship between TIGAR and other proteins in cervical cancer, we constructed a protein-protein associations (PPI) network using the online STRING tool to obtain 50 TIGAR-interacting proteins (Fig. 5A) and analyzed the correlation between TIGAR and these 50 proteins (Fig. 5B). To identify genes associated with TIGAR expression, we obtained the top 100 TIGAR-associated genes using the GEPIA2 tool and analyzed the correlation between TIGAR expression and the top 50 TIGAR-associated genes in cervical cancer using the Spearman’s correlation test (Fig. 5C). Among the 150 TIGAR-interacting and associated genes, we analyzed the correlation between TIGAR and the most strongly correlated genes, ERC1, C12orf4, and LPCAT3 (Fig. 5D–F). To predict TIGAR gene function, we performed enrichment analysis on 150 genes (Fig. 6). GO enrichment analysis showed that “regulation of intracellular oxidative stress,” “hypoxia,” “apoptosis,” and ‘autophagy’ was the most enriched GO term (Fig. 6A–C). KEGG pathway analysis showed that most genes were associated with “AMPK signaling pathway,” “HPV infection,” “P53 signaling pathway” and “platinum drug resistance” (Fig. 6D)26,27,28.
PPI networks and correlation analysis of TIGAR in cervical cancer. (A) PPI network of TIGAR-interacting proteins was created using STRING. (B) Correlation between TIGAR and the 50 TIGAR-interacting proteins in cervical cancer. (C) Correlation between TIGAR and the top 50 TIGAR-associated genes in cervical cancer. (D–F) Correlation between TIGAR and the top three TIGAR-associated genes ERC1, C12orf4, and LPCAT3. *P < 0.05; **P < 0.01; ***P < 0.001. TPM (Transcripts Per Million) is a method used to normalize RNA sequencing data, which measures the number of transcripts of a gene in per million transcripts.
Enrichment analysis of TIGAR. (A–D) 150 TIGAR-interacting and associated genes were used for GO and KEGG enrichment analyses. BP, biological process; CC, cellular component; MF, molecular function.
Correlation between TIGAR expression and drug sensitivity
As shown in Fig. 6D, TIGAR is associated with platinum drug resistance. Therefore, we analyzed the correlation between TIGAR expression and sensitivity to anticancer compounds. Based on the drug sensitivity data from the GDSC database, we used the R package “oncoPredict” to predict the chemotherapy sensitivity of cervical cancer samples in TCGA and further explored the sensitivity of TIGAR and common chemotherapy drugs. The results showed that TIGAR was negatively associated with estimated IC50 of 5-Fluorouracil, Bortezomib, Oxaliplatin, Podophylotoxin bromide, Ruxolitinib, Tozasertib, Vinorelbine (Fig. 7).
The sensitivity of TIGAR and common chemotherapy drugs. Based on the drug sensitivity data from the GDSC database, the predicted potential therapeutic agents were (A) 5-Fluorouracil, (B) Bortezomib, (C) GSK2606414, (D) MG-132, (E) Mirin, (F) Oxaliplatin, (G) P22077, (H) PFI3, (I) Podophyllotoxin bromide, (J) Ruxolitinib, (K) SB216763, (L) Tozasertib, (M) Vincristine, (N) Vinorelbine.
HPV E6 oncoprotein could promote upregulation of TIGAR
KEGG enrichment analysis showed that TIGAR was associated with “HPV infection.” The HPV E6 oncoprotein can induce oncogenic and malignant transformation of cervical epithelial cells, promoting uncontrolled cell proliferation and accumulation of genetic damage29. Yapindi et al. showed that when HT-1080 cells were transfected with HPV18 E6 or HPV16 E6 (HA-tagged) expression constructs, the level of TIGAR protein increased significantly and was mainly localized in the mitochondria30. This suggests that HPV E6 protein may be one of the reasons for TIGAR upregulation. We found that in cervical cancer HeLa (HPV18+) and SiHa (HPV16+) cells, inhibition of E6 decreased TIGAR levels (Fig. 8A–D), whereas inhibition of TIGAR did not affect E6 expression (Fig. 8E–H), suggesting that the HPV E6 oncoprotein acts upstream of TIGAR and promotes the upregulation.
HPV E6 protein promoted the upregulation of TIGAR. (A) Knockdown of E6 decreased TIGAR levels in HeLa cells. (B) Knockdown of E6 decreased TIGAR levels in SiHa cells. (C,D) are statistical graphs of (A,B), respectively. (E) Knockdown of TIGAR does not affect E6 levels in HeLa cells. (F) Knockdown of TIGAR does not affect E6 levels in SiHa cells. (G,H) are statistical graphs of (E,F), respectively.
Weighted gene co-expression network analysis (WGCNA) of TIGAR
Co-expression network analysis is an effective tool for elucidating complex and variable features of diseases. Based on cervical cancer data in GSE63514 from GEO database, we constructed WGCNA to explore the regulatory network associated with TIGAR in cervical cancer. The soft thresholding power (β = 9, cut-off = 0.9) was used to create a weighted gene network (Fig. 9A), resulting in 17 distinct modules of different colors (Fig. 9B). Initial visualization of the TOM of the proteins was performed using a heatmap plot with various module assignments and protein dendrograms (Fig. 9C). Further analysis of the modules and traits revealed that the salmon module had the highest correlation with TIGAR (cor = 0.76, p = 3e−06) (Fig. 9D). We further performed gene enrichment analysis on the salmon modules, and the results showed that the genes were mainly enriched in the cell cycle, nuclear division, ATP hydrolysis activity, and other pathways (Fig. 9E)26,27,28.
WGCNA analysis of TIGAR. (A) The scale-free index and average connectivity of each soft threshold for cervical cancer. (B) Dendrogram of cervical cancer gene clusters, with different colors representing different modules. (C) Dendrogram and network heatmap of topological overlap between proteins. (D) Heatmap of the correlation between module-characterized genes and TIGAR, with negative correlations in blue and positive correlations in red. (E) Gene enrichment analysis of the salmon module.
Correlation between TIGAR expression and tumor infiltrating lymphocytes (TILs)
From the previous results (Fig. 2D), we found that high or low levels of TIGAR expression had an impact on the OS of patients with cervical cancer. Therefore, we analyzed TIGAR expression in immune subtypes (Fig. 10A) and found that TIGAR expression was present in three immune subtypes of cervical cancer: C1, C2, and C4. Furthermore, we evaluated the relationship between TIGAR expression and TILs in cervical cancer; the results showed that TIGAR was negatively correlated with the levels of Eosinophils and Th17 cells, and positively correlated with immunocytes, including T helper cells, macrophages, activated dendritic cells (aDC), etc. (Fig. 10B,D). Similarly, we validated the relationship between TIGAR and TILs in cervical cancer using the TIDISB database and found that TIGAR was significantly associated with the infiltration of immunocytes (Fig. S1A), particularly regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSC) (Fig. S2A,B). Subsequently, Spearman’s correlation and Wilcoxon signed-rank tests were used to calculate the relationship between TIGAR levels and the abundance of infiltrating macrophages, T cells, and CD8 + T cells (Fig. 10C,E–L).
Correlation between TIGAR expression and TILs. (A) Correlation between TIGAR expression and the immune subtypes of cervical cancer. (B) Correlation between TIGAR expression and TILs in pan-cancer. (C) Wilcoxon signed-rank test was used to detect the level of infiltrating immunocytes in the TIGAR high/low-expression group. (D) TIGAR expression was significantly associated with immunocyte infiltration in cervical cancer. (E–L) Spearman’s correlation test was used to calculate the correlation between TIGAR expression and the infiltration levels of immunocytes (aDC, CD8 + T cells, macrophages, T cells, T helper cells, Tgd, eosinophils, and Th17 cells).
Correlation between TIGAR expression and immunoregulation factors
To assess the relationship between TIGAR and immune regulation, we constructed heat maps of TIGAR and immune-related factors to analyze the correlation between TIGAR and immunoregulation-related genes. Figure S1B showed that TIGAR was negatively correlated with some immunostimulators, such as TNF receptor superfamily member 13 C (TNFRSF13C), TNFRSF14, and TNF superfamily member 13 (TNFSF13), and the correlation was examined in Figure S2C–E. TIGAR was also positively correlated with most immunoinhibitors, especially CD244, CD274, cytotoxic T-lymphocyte-associated protein 4 (CTLA4), interleukin 10 (IL-10), etc. (Fig. S1C), and the correlation was analyzed in Fig. S2F–L. In addition, TIGAR was also positively correlated with some MHC molecules (Fig. S1D), and the correlation was analyzed between TIGAR and β2-microglobulin (B2M), HLA-E (Fig. S2M,N). Figure S1E,F showed that TIGAR was positively correlated with chemokines and chemokine receptors, particularly CCL3, CCL8, CCR1, and CCR5 (Fig. S2O–T).
Elevated TIGAR expression in cervical cancer tissues is accompanied by an increase in tumor immunosuppressive cells and molecules
To assess the clinical importance of TIGAR in cervical cancer, we compared TIGAR levels in human cervical cancer tissues and normal cervical tissues using immunohistochemistry (n = 20), as well as the infiltration levels of immunocytes and immunoregulatory factors. Figure 11 shows that TIGAR levels were increased in cervical cancer tissues, and the levels of Foxp3 + Treg cells, CD11b + MDSC, CTLA4, and HAVCR2 were significantly higher than those in normal tissues, suggesting that elevated TIGAR expression in cervical cancer tissues may be related to the tumor immunosuppressive microenvironment.
Elevated TIGAR expression in cervical cancer tissues is accompanied by an increase in tumor immunosuppressive cells and molecules. Representative immunohistochemical (IHC) staining images of (A) TIGAR expression and (B) Foxp3 + Tregs, (C) CD11b + MDSC, (D) CTLA4, and (E) HAVCR2 in cervical cancer and normal cervical tissues. Scale bars: ×100: 500 μm, ×200: 200 μm.
Discussion
In high-income countries, cervical cancer incidence and mortality have more than halved over the past 30 years since the introduction of formal screening programs31, lacking specific and sensitive markers. Therefore, it is crucial to identify useful diagnostic and prognostic biomarkers of cervical cancer. Cancer cells require large amounts of nutrients and metabolites to meet the metabolic demands for rapid proliferation. Regardless of the availability of oxygen, cancer cells tend to use glycolysis to produce lactic acid for growth, a phenomenon known as the “Warburg effect”32. TIGAR reprograms cellular glucose metabolism and converts glycolysis to the PPP, which tends to enhance mitochondrial function in cancer cells33. TIGAR expression was elevated in many solid tumors. Le Zhang et al. found that PET/CT parameters could reflect the invasiveness and prognosis of early-stage cervical cancer, and PET/CT parameters were negatively correlated with TIGAR expression; however, the authors did not explore the relationship between the expression level of TIGAR and clinicopathological features of early-stage cervical cancer34. Thus, the role of TIGAR in the diagnosis and prognosis of cervical cancer needs to be further investigated. Based on bioinformatic analysis and experimental validation, our study showed that TIGAR was highly expressed in cervical cancer tissues and cells. Elevated TIGAR expression was associated with tumor progression and poor prognosis, and its diagnostic value was confirmed by ROC curves. Therefore, it can be inferred that TIGAR is a potential marker for cervical cancer.
In addition, our results showed that TIGAR was significantly upregulated in most cancers, which is consistent with previous reports15,16,17,18,19,20,21,22,23. These results suggest that TIGAR is a potential diagnostic marker for various cancers. The inhibitory effect of TIGAR on glycolysis contradicts the “Warburg effect,” which may be related to the “Reverse Warburg effect”35. According to “Reverse Warburg effect”, cancer cells secrete hydrogen peroxide (H2O2) into the TME to induce oxidative stress in neighboring stromal cells. Cancer-associated fibroblasts (CAFs) undergo aerobic glycolysis and produce high levels of intermediary metabolites (e.g., pyruvate and ketones), which are used by the mitochondria of cancer cells to produce large amounts of ATP, thereby promoting cancer proliferation36,37. Breast cancer MCF-7 and BT474 cells and glioma cells with high TIGAR expression have been reported to counteract aerobic glycolysis and promote mitochondrial generation and mitochondrial oxidative phosphorylation38,39,40. Therefore, redirecting the “Warburg effect” to the PPP favors the growth of cancer cells.
To elucidate the role of TIGAR in cervical carcinogenesis, we performed GO and KEGG enrichment analyses to identify the biological pathways associated with TIGAR. KEGG analysis showed that TIGAR was associated with “platinum drug resistance”. Gemcitabine- and cisplatin-resistant urothelial cancer cells show metabolic reprogramming, stimulating an increase in aerobic glycolysis and PPP, and an increase in the expression of TIGAR, with a high expression of TIGAR being associated with a worse prognosis for patients41. Yapindi et al. found that inhibiting TIGAR using siRNA increased the sensitivity of HeLa cells to apoptosis induced by common chemotherapy drugs that caused oxidative stress25. This suggests that combination therapy targeting TIGAR may be a powerful therapeutic strategy for HPV-related cancers. In addition, TIGAR was mainly enriched in the “AMPK signaling pathway,” which plays a key role in maintaining homeostasis of cellular energy metabolism, sensing ATP deficiency and generating energy through various metabolic modes, and its expression was elevated in 17 types of cancers, including cervical cancer42. TIGAR drives colorectal cancer cells to resist ferroptosis via the ROS/AMPK/ SCD1 pathway43. This suggests that TIGAR may play a pro-cancer role via the AMPK signaling pathway. Notably, the “P53 signaling pathways” were also associated with TIGAR expression. P53 regulates glycolysis by modulating the expression of TIGAR, which inhibits glycolysis while converting glucose metabolism to the pentose phosphate pathway (PPP), and TIGAR reduces ROS production and inhibits apoptosis and autophagy by promoting glutathione production44,45.
To prevent the recurrence and progression of cervical cancer, identification of TIGAR targets is necessary. Among the TIGAR-interacting and associated genes, ERC1, C12orf4, and LPCAT3 showed the strongest correlation with TIGAR. ERC1 is a protein-coding gene associated with thyroid cancer and studies confirming that ERC1 is involved in the progression of papillary thyroid cancer and non-small cell lung cancer46,47. ERC1 is also associated with breast cancer cell invasion and metastasis, and its inhibition further inhibits the activity of the oncogenic transcription factor NF-κB48. C12orf4 is located on the open reading frame 4 of chromosome 12 and is involved in mast cell degranulation, which plays an important role in asthma and inflammation-related diseases49. LPCAT3 is a member of the lysophosphatidylcholine acyl glycosyltransferase (LPCAT) family that regulates the abundance of different types of phospholipids in cells and tissues and plays an important role in lipid metabolism and homeostasis50. Increasing evidence suggests that LPCAT3 has tumorigenic activity in a variety of tumors51,52,53, and plays a central role in non-apoptotic cell death, particularly ferroptosis54,55. This implies that ERC1, C12orf4, and LPCAT3 may also have potential clinical translational value in cervical cancer and that combination therapy with TIGAR may be a useful anticancer strategy.
The level of immune infiltration in the tumor microenvironment determines the clinical outcome of cancer patients56. A previous study found an association between TIGAR and immune infiltration in hepatocellular carcinoma based on bioinformatics analysis57; however, this was not validated in clinical specimens and experiments. Further, the relationship between TIGAR expression and immune cell infiltration in cervical cancer has not been reported. In the present study, we found an association between TIGAR expression and the infiltration of several TILs and immunoregulation-related genes in cervical cancer. In addition, we confirmed in a clinical cohort from the Affiliated Hospital of Guizhou Medical University that TIGAR was highly expressed in cervical cancer tissues, and that the levels of Foxp3 + Treg cells, CD11b + MDSC, CTLA4, and HAVCR2 were also markedly elevated. Treg is a class of T cell functional subpopulations with important immunomodulatory functions, which inhibit T cell activity in a variety of ways in the tumor microenvironment to achieve tumor immune escape58. Treg can bind to the corresponding receptors on target cells via CTLA4 and TGF-β molecules, thereby reducing the expression of IL- 2Rα chain in effector T cells and leading to its apoptosis59. Tumor cells exert antitumor immunity by releasing chemokines such as CCL22/CCL17, which bind to the CCR4 of Tregs and attract many Tregs to move to the TME60. MDSCs are heterogeneous immature myeloid cells that accumulate during tumor progression61,62,63. MDSCs are one of the most potent immunosuppressive cells, which can promote tumor progression by inhibiting the anti-tumor functions of T cells and NK cells, and can also directly stimulate tumor progression by promoting neovascularization and tumor cell invasion, as well as creating a pre-metastatic milieu64. CTLA4, a classical immune checkpoint molecule, has strong affinity for the B7-1 (CD80)/B7-2 (CD86) receptor65. As the first available immune checkpoint inhibitor (ICI), the human CTLA4 antibody (ibritumomab) prolonged the survival of metastatic melanoma patients by 10 years66. HAVCR2, also known as TIM-3, is a marker of T cell depletion. Upregulation of TIM-3 has been associated with CD8 + T cell depletion in melanoma patients, whereas TIM-3 monoclonal antibody reversed tumor-induced T cell depletion67. In this study, we found that high TIGAR expression in cervical cancer tissues was accompanied by high infiltration of Foxp3 + Tregs, CD11b + MDSC, CTLA4, and HAVCR2, suggesting that TIGAR expression may be related to the immunosuppressive tumor microenvironment.
This study has some limitations, including the failure to explore the effect of TIGAR on the immunosuppressive microenvironment in vivo and the molecular mechanism by which TIGAR affects immunosuppression at the cellular level. Future experiments will also increase the functional experiments on upstream and downstream targets of TIGAR and the combined relationship between upstream and downstream targets.
In conclusion, TIGAR expression is upregulated in cervical cancer tissues and cells, and elevated TIGAR expression is associated with poor OS. Immune infiltration analysis showed that TIGAR expression was related to the tumor immunosuppressive microenvironment. This study demonstrates that TIGAR is a potential biomarker for the diagnosis and prognosis of cervical cancer and is expected to be a potential immunotherapy target.
Materials and methods
Data acquisition and subsequent analysis using bioinformatics
The clinical records and gene expression data of 306 patients with cervical cancer and 13 normal cervical tissues were obtained from TCGA68 (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and GTEx69 (https://gtexportal.org/home) datasets on the Xiantao platform ( https://www.xiantaozi.com/). Xiantao tool is a useful bioinformatics analysis web tool, and was used for visualization70.The RNA-seq data in TPM format were uniformly processed using the Toil process71. We analyzed the expression and prognosis of TIGAR in cervical cancer on “Xiantao platform,” and investigated its potential biological roles in “Functional clustering” panel using GO and KEGG analyses. In “immune infiltration” panel, we evaluated the correlation between infiltration of immune lymphocytes and immunomodulatory molecules and TIGAR levels.
ROC curves
Based on TIGAR mRNA expression levels in cervical cancer tissues and the corresponding paracancer and normal samples in the TCGA and GTEx databases, an ROC curve was constructed on the Xiantao Platform to evaluate the diagnostic value of TIGAR in cervical cancer. The ROC analysis was performed using the “pROC” (version 1.18.0) package72, and the results were visualized using the “ggplot2” (version 3.4.4) package73, and the area under the curve (AUC), cut-off value, sensitivity, specificity, positive predictive value, negative predictive value, and Youden index (YI) were calculated. The closer the AUC is to 1, the better the diagnostic performance, the lower the accuracy for AUC values between 0.5 and 0.7, the better the accuracy for values between 0.7 and 0.9, and the higher the accuracy for values above 0.9. The YI indicates the total ability of the screening method to detect real patients and non-patients; the higher the value, the more effective the screening method.
GEPIA2
We analyzed the correlation between TIGAR expression levels and the OS of patients with cervical cancer using GEPIA2 (https://gepia2.cancer-pku.cn/) and collected the top 100 TIGAR-associated genes for functional enrichment analysis. GEPIA2 is a web server for analyzing RNA sequence data from TCGA and GTEx databases74.
Protein–protein interaction (PPI) network analyses
We used the STRING (version 12.0) website (https://string-db.org/) to generate a network of TIGAR-binding proteins75 and obtained 50 proteins that interacted with TIGAR. First, we entered a single protein name (“TIGAR”) and organism (“Homo sapiens”) in the “Search” module. Subsequently, we set the following options: active interaction source (“Textmining and Experimentation”), minimum required interaction score (“low Confidence (0.150)”), maximum number of interactors to show (“no more than 50 interactors” in the first shell), and “default” for the other settings.
Function enrichment analysis
GO analysis is widely used in functional enrichment studies to generate data on biological processes, cellular components, and molecular functions76. KEGG is a database for systematically analyzing gene functions that connects genomic information to detailed information on biological functions26,27,28. Through GEPIA2 and STRING online analyses, we obtained 150 TIGAR-interacting and-associated genes that were analyzed using GO and KEGG enrichment.
WGCNA
Based on the cervical cancer data in GSE63514 in the GEO database77,78, we constructed a weighted gene co-expression network, searched for gene co-expression modules, and explored the correlations between the gene network and TIGAR, as well as the key genes in the network. The co-expression network of all genes in the cervical cancer dataset was constructed using the WGCNA-R package79, and genes with the top 5000 variograms were screened using this algorithm for further analysis. The soft threshold was set to nine. The weighted adjacency matrix was transformed into a topological overlap matrix (TOM) to estimate network connectivity, and the hierarchical clustering method was used to construct the cluster tree structure of the TOM. Different branches of the cluster tree represent different gene modules, and different colors represent different modules. Genes were classified according to their expression patterns based on their weighted correlation coefficients. Genes with similar expression patterns were grouped into one module, and all genes were divided into multiple modules according to their gene expression patterns.
Drug sensitivity analysis
Based on the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/) database, we used R software package “oncoPredict” to predict chemotherapy sensitivity of cervical cancer samples80. The IC50 estimates of each chemotherapeutic drug were obtained using the regression method, and the regression and prediction accuracy were checked using 10 cross-validations with the GDSC training set. Default values were selected for all parameters, including “combat” to remove batch effects and taking an average of repeated gene expression.
TIGAR expression in different immune subtypes of cervical cancer
We investigated the association between TIGAR expression and immune subtypes in cervical cancer through the “subtypes” module of the TISIDB (http://cis.hku.hk/TISIDB/) database. The different immune subtypes include C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-β dominant). The TISIDB database combines various data types to assess interactions between cancer and the immune system81.
Immune infiltration analysis
We evaluated the correlation between immune cells and TIGAR expression using “Immune Infiltration” panel of the Xiantao platform, and explored the association between TIGAR expression and tumor-infiltrating lymphocytes, immunostimulators, immunoinhibitors, MHC molecules, chemokines, and chemokine receptors using the TISIDB database, and analyzed the relationship between related immune cells, molecules and chemokines and TIGAR expression in cervical cancer using Spearman’s correlation test.
Tissue specimens
From January 2018 to December 2021, 10 cervical cancer specimens, three corresponding adjacent cancer specimens from patients with cervical cancer, and 10 normal cervical specimens from patients who underwent cervical resection for benign diseases were collected from the Affiliated Hospital of Guizhou Medical University. All tissue samples were independently analyzed by two pathologists at the hospital. Paracancerous tissue refers to tissue located 2 cm from the tumor lesion. All samples were approved by the Human Ethics Committee of the Affiliated Hospital of Guizhou Medical University and informed consent was obtained from all participants. The inclusion criteria were as follows: no history of chemotherapy, radiation therapy, hormone therapy, or other medications prior to admission. If the patient had infectious diseases, autoimmune disease, pelvic metastatic malignant tumor, or cancer of other organs such as the breast, liver, kidney, or spleen, or received immunosuppressive treatment within half a year before admission, the patient was excluded.
Cell culture
The normal human cervical epithelial cell line HCerEpic was purchased from Beijing Beina Chuanglian Biotechnology Institute. Human cervical cancer SiHa (CVCL_0032) and HeLa (CVCL_0030) cells were purchased from Nanjing Kebai Biotechnology Co., Ltd., and C33A (CVCL_1094) cells were purchased from Wuhan Pricella Biotechnology Co., Ltd. The cells were cultured in DMEM (Gibco, USA) supplemented with 10% fetal bovine serum (Beyotime Biotechnology Co., Ltd.) and 1% penicillin/streptomycin solution (Gibco, USA), and grown in an incubator at 37℃ in a 5% CO2 with 97% humidity. The cell culture medium was changed every 2–3 days, and when the degree of cell fusion reached 80–90%, 0.25% trypsin was used for passage.
Plasmid construction and lentiviral transfection
The human HPV16/18 E6 downregulation plasmid (LV: pLKO.1) and control plasmid were purchased from Guangzhou IGE Biotechnology Co., Ltd. The human TIGAR downregulation lentiviral plasmid (LV3: pGLVH1/GFP + Puro), control lentiviral plasmid, and corresponding interference lentivirus were obtained from Shanghai Genepharma Technology Co. The vector sequences are listed in Table 2. Cervical cancer cells were infected in the presence of polybrene (5 µg/mL), with a multiplicity of infection (MOI) of 10–20 for HeLa cells and 80 for SiHa cells. Following lentiviral infection, cervical cancer cells were screened with puromycin for 2–3 generations to obtain cervical cancer cells that stably knocked down TIGAR/HPV E6. The working concentration of puromycin was 1.5 µg/mL for HeLa and 2 µg/mL for SiHa.
Western blotting
Human cervical cancer and adjacent tissues were cut into small pieces and ground in liquid nitrogen, and proteins were extracted using a tissue lysis buffer containing protease inhibitors. After collecting HCerEpic, SiHa, HeLa, and C33A cells using trypsin, RIPA lysis buffer containing protease inhibitors was added to extract the proteins. The mixture was incubated on ice for 30 min, sonicated, and centrifuged at 12,000 g at 4 ℃ for 15 min, and the supernatant was collected. The protein concentration was determined using the BCA method (KeyGEN, China), and the protein samples were subjected to SDS-PAGE. The proteins were transferred onto a PVDF membrane and sealed with 5% skim milk powder for 2 h. The primary antibodies (TIGAR, E6, GAPDH) were incubated overnight at 4 ℃, washed with PBST three times, and the secondary antibodies were incubated at room temperature for 1 h. After washing three times with PBST, ECL chemiluminescence was used for color development. The process for each sample was repeated three times. The TIGAR antibody (22136-1-AP) was purchased from Wuhan Proteintech Co., Ltd. E6 antibody (C1P5) was purchased from Santa Cruz Biotechnology, and GAPDH (MB66349, Nanjing Bioworld Biotech Co., Ltd.) was used as an internal reference. Quantitative analysis was performed using the ImageJ software, and GAPDH grayscale values were normalized to calculate the relative abundance of the target protein.
IHC staining
Formalin-fixed, paraffin-embedded human cervical cancer tissue samples were sectioned and subjected to immunohistochemistry. Tissue sections were sequentially deaffinity, rehydrated, treated with citrate buffer for 3 min to extract antigens, and then treated with 3% hydrogen peroxide for 30 min to block endogenous peroxide activity. After blocking with goat serum for 30 min and washing three times with PBS, the sections were incubated with primary antibodies TIGAR (1:200, 22136-1-AP, Wuhan Proteintech Co., Ltd), CTLA4 (ZM-0035, Zhongshan Jinqiao, Beijing, China), CD11b (1:1500, ab133357), Foxp3 (1:500, ab20034), and HAVCR2 (1:1000, ab241332, Abcam, United States) at 4 °C overnight. Next, the sections were washed three times with PBS and incubated with horseradish peroxidase-conjugated goat anti-rabbit immunoglobulin (PV- 6001, Beijing Zhongshan Jinqiao Biotechnology Co., Ltd.) for 2 h at room temperature. After washing with PBS, the sections were incubated in 3′-3′-diaminobenzidine (DAB, Beijing Solarbio Science & Technology Co., Ltd., China) for 2–3 min, and then re-stained with hematoxylin, differentiated with hydrochloric acid, returned to the blue color, dehydrated, and finally observed under microscope. Slides were examined in five randomly selected high-resolution fields (200 × magnification), with triplicate counts for each field. The staining score method combining quantitative counting of the number of positively stained cells and qualitative assessment of color intensity were used82. For the color intensity score, slides were assigned a score of 0 (unstained), 1 (weak), 2 (moderate), and 3 (strong). The stained areas were scored for the percentage of positive cell count in the investigated areas: 0 (0%), 1 (1–25%), 2 (26–50%), 3 (50–75%), and 4 (> 75%). The final immunoreactivity score (IRS) was expressed as the staining intensity score × staining area score. All slides were examined by two independent pathologists who were blinded to the clinical outcomes.
Statistical analysis
All statistical analyses were performed using SPSS software (version 25.0; SPSS Corp., Armonk, NY, USA)83. For continuous variables, t-tests were used for two-group comparisons, ANOVA for multiple comparisons, and paired t-tests for paired tissues. Survival curves were plotted using the Kaplan-Meier method, and differences between groups were assessed using the log-rank test. To assess the correlation between TIGAR expression and clinicopathological features, we used the Kruskal-Wallis test, chi-squared test, and continuous corrected chi-squared test (Yates correction). To test the correlation between TIGAR expression and immune cells and molecules, we used Spearman’s correlation test. Statistical significance was reported at *P < 0.05, **P < 0.01, and ***P < 0.001.
We confirm that all methods were performed in accordance with the relevant guidelines and regulations.
Data availability
The data generated in the present study may be requested from the corresponding author on reasonable request.
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Acknowledgements
We would like to thank the staff of TCGA and GEO research network, as well as all the authors who made their precious work previously online. This study was funded by the National Natural Science Foundation of China (NSFC, Grant numbers 82360588), the project of Guizhou Medical University National Foundation Cultivation Project (Grant number 21NSFCP15), the project of Guizhou Provincial Science and Technology Projects (Grant number Qiankehe Fundamentals-ZK(2024)-General 202), and the project of Guizhou Provincial Health Commission (Grant number gzwkj 2024-204).
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The refined section is as follows: Y.L.: concept and design; Y.Z. (Yu Zhang), H.Z. and D.X.: collection of data; X.Z. (Xinzhu Zhou) , L.X. and H.X.: cell experiments; Y.L., Y.Z. (Yurong Zhu) and H.Y.: data analysis; X.Z. (Xiaoyu Zhu) and J.R. : data validation, and S.M. and Y.T. manuscript writing. All authors have read and agreed to the published version of the manuscript.
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The studies involving human participants were reviewed and approved by the Human Ethics Committee of the Affiliated Hospital of Guizhou Medical University. Informed consent was obtained from all subjects involved in the study.
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Liu, Y., Zhang, Y., Zhang, H. et al. TIGAR regulated by HPV E6 is correlated with disease stage, drug sensitivity, and immune microenvironment in cervical cancer. Sci Rep 15, 20618 (2025). https://doi.org/10.1038/s41598-025-07201-4
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DOI: https://doi.org/10.1038/s41598-025-07201-4
