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Article

Expression of HIF-1α and Genes Involved in Glucose Metabolism Is Increased in Cervical Cancer and HPV-16-Positive Cell Lines

by
Víctor D. Priego-Hernández
1,
Adán Arizmendi-Izazaga
1,
Diana G. Soto-Flores
1,
Norma Santiago-Ramón
1,
Milagros D. Feria-Valadez
1,
Napoleón Navarro-Tito
2,
Hilda Jiménez-Wences
3,4,
Dinorah N. Martínez-Carrillo
3,4,
Eric G. Salmerón-Bárcenas
5,
Marco A. Leyva-Vázquez
1,
Berenice Illades-Aguiar
1,
Luz del C. Alarcón-Romero
6 and
Julio Ortiz-Ortiz
1,4,*
1
Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico
2
Laboratorio de Biología Celular del Cáncer, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico
3
Laboratorio de Investigación Clínica, Facultad de Ciencias, Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico
4
Laboratorio de Investigación en Biomoléculas, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico
5
Departamento de Biomedicina Molecular, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Ciudad de Mexico 07360, Mexico
6
Laboratorio de Investigación en Citopatología e Histoquímica de la Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Chilpancingo C.P. 39090, Guerrero, Mexico
*
Author to whom correspondence should be addressed.
Pathogens 2023, 12(1), 33; https://doi.org/10.3390/pathogens12010033
Submission received: 14 November 2022 / Revised: 21 December 2022 / Accepted: 23 December 2022 / Published: 25 December 2022
(This article belongs to the Special Issue Latest Advances in Human Papillomavirus (HPV) Research)
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Abstract

:
Cervical cancer (CC) is the most common cancer in women in the lower genital tract. The main risk factor for developing CC is persistent infection with HPV 16. The E6 and E7 oncoproteins of HPV 16 have been related to metabolic reprogramming in cancer through the regulation of the expression and stability of HIF-1α and consequently of the expression of its target genes, such as HIF1A (HIF-1α), SLC2A1 (GLUT1), LDHA, CA9 (CAIX), SLC16A3 (MCT4), and BSG (Basigin or CD147), which are involved in glucose metabolism. This work aimed to evaluate the expression of HIF-1α, GLUT1, LDHA, CAIX, MCT4, and Basigin in patient samples and CC cell lines. To evaluate the expression level of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG genes in tissue from patients with CC and normal tissue, the TCGA dataset was used. To evaluate the expression level of these genes by RT-qPCR in CC cell lines, HPV-negative (C-33A) and HPV-16-positive (SiHa and Ca Ski) cell lines were used. Increased expression of HIF1A, SLC2A1, LDHA, SLC16A3, and BSG was found in Ca Ski and CA9 in SiHa compared to C-33A. Similar results were observed in CC tissues compared to normal tissue obtained by bioinformatics analysis. In conclusion, the expression of HIF-1α, GLUT1, LDHA, CAIX, MCT4, and BSG genes is increased in CC and HPV-16-positive cell lines.

1. Introduction

Cervical cancer (CC) is the fourth leading cause of death in women worldwide, with approximately 604,127 new cases and 341,831 deaths annually [1]. Human papillomavirus 16 (HPV 16) is present in more than 50% of cases of CC [2]. The oncogenicity of HPV results mainly from the action of E6 and E7 oncoproteins. E6 promotes carcinogenesis by inducing the degradation of the tumor suppressor protein p53 and activating the PI3K/AKT/mTOR [3]. In contrast, E7 contributes to carcinogenesis by inducing the retinoblastoma tumor suppressor protein (pRb) degradation by releasing the cell cycle transcription factor E2F1 [4]. Several studies have revealed the association between HIF-1α overexpression and worse prognosis in patients with this type of cancer [5]. In different HPV-16-positive cancers, other active HIF1-regulated genes are overexpressed, which encode proteins that play an essential role in immortalization [6,7], cell proliferation, metastasis, and metabolic reprogramming [8].
Metabolic reprogramming is an essential feature of cancer. Tumor cells reprogram their metabolism through a process known as the Warburg effect; this process is defined as the ability of cells to have high rates of glycolysis for the generation of ATP and precursors of different biomolecules independent of O2 [5,9]. The active HIF1 transcription factor is a master transcription factor of genes in response to hypoxia [10]. Active HIF1 is a heterodimer consisting of two subunits: HIF-α, which is induced by hypoxia and has three isoforms: HIF-1α, HIF-2α, and HIF-3α; and the HIF-1β subunit, which is constitutively expressed [10]. The transcription factors, such as the c-Myc-MAX heterodimer, the ISGF3 complex, composed of STAT1/STAT2/IRF9, STAT3, NF-κB, and even active HIF1 itself, bind to the promoter region of the HIF-1α gene to activate the initiation of its transcription [11]. Active HIF1 binds to the promoters of genes containing the 5′-RCGTG-3′ sequence, known as hypoxia response elements (HREs) [12]. Together with the coactivators CBP and p300 [13], they can regulate the expression of more than 70 genes, including some involved in glucose metabolism, such as HIF-1α, GLUT1, LDHA, CAIX, MCT4, and BSG (Basigin) [12,14]. HIF-1 upregulates the expression of GLUT1 and GLUT3 genes, both necessary for glucose uptake, and enhances lactate production by the enzyme lactate dehydrogenase A (LDHA), thereby decreasing intracellular pH. This mechanism regulates intracellular acidosis and is essential for maintaining homeostasis in the carbonic-anhydrase-IX (CAIX)-dependent mechanism [15]. Together with MCT4 and BSG, they facilitate the release of lactate and H+ to the extracellular milieu to neutralize intracellular acidosis in a HIF-1-dependent manner [16], which is associated with a hyperglycolytic and acid-fast phenotype in cancer [14,17,18] and the release of lactate into the extracellular milieu [19]. It has been shown in cancer in vitro that MCT4 is regulated by HIF1, unlike MCT1 and MCT2, because it has two HREs in its promoter [20].
It has been reported that under hypoxia conditions in CC, E7 is associated with increased expression of HIF-1α via ROS, ERK1/2, and NF-κB [2,11]. In turn, E6 positively regulates HIF-1α, preventing its ubiquitination by VHL and subsequent degradation via proteasome [21]. Thus, E7 and E6 could indirectly promote increased glycolysis and resistance to intracellular pH changes through increased expression of HIF-1α and its target genes, such as GLUT1, LDHA, CAIX, MCT4, and BSG [3,9,22]. Moreover, the high glucose levels in blood have been associated with bad prognosis in CC, suggesting a potential relationship between CC and the metabolism of glucose [23]. However, the role of HPV 16 oncoproteins E6 and E7 in metabolic reprogramming is not entirely clear.
Therefore, in this work, we evaluated the expression of active HIF1 target genes: HIF-1α, GLUT1, LDHA, CAIX, MCT4, and BSG in HPV-16-positive CC cell lines and biopsies of CC patients using data from The Cancer Genome Atlas (TCGA). Likewise, an overall survival analysis was performed using the Kaplan–Meier Plotter database. Increased expression of HIF1A, SLC2A1, LDHA, SLC16A3, and BSG was observed in cell lines and biopsies of patients with CC and was associated with poor survival prognosis. This information will contribute to a better understanding of the mechanisms that favor metabolic reprogramming in HPV-16-positive CC.

2. Materials and Methods

2.1. Gene Expression Analysis in CC Samples Using the TCGA and HPA Datasets

For gene expression analysis of mRNA level in CC patient samples, data were obtained from The Cancer Genome Atlas (TCGA) dataset and the GEPIA database [24]. The total was n = 306 biopsies from patients with CC and n = 13 with normal tissue. Graphs showed the expression levels of HIF-1α, GLUT1, LDHA, CAIX, MCT4, and BSG. Expression was log2 transformed (TPM+1), differences were calculated using a one-way ANOVA test, and a p-value < 0.05 was considered statistically significant. The expression of these six genes compared to protein level was analyzed in the Human Protein Atlas (HPA) database [25].

2.2. Correlation Analysis

Correlation analysis between HIF1A expression and SLC2A1, LDHA, SLC16A3, and BSG expression was performed on CC samples from the TCGA dataset using the GEPIA database [24]. The correlation was calculated using Spearman and R2 coefficients; a value of p < 0.05 was considered statistically significant.

2.3. Cell Culture

C-33A, SiHa, and Ca Ski cell lines were cultured in DMEM (Dulbecco’s Modified Eagle’s Medium) supplemented with 10% fetal bovine serum (Gibco, Life Technologies, Grand Island, NY, USA), and 100 U/mL penicillin and 100 µg/mL streptomycin (Gibco, Life Technologies, Grand Island, NY, USA) were added. The cells were maintained at a temperature of 37 °C with an atmosphere of 5% CO2.

2.4. RNA Extraction

Total RNA extraction was performed using TriZol® Reagent (Ambion® by Life Technologies, Carlsbad, CA, USA), following the manufacturer’s instructions. Total RNA integrity was verified by 1.5% agarose gel electrophoresis. The concentration and purity of the RNA obtained were determined by spectrophotometry using the Nanodrop 2000c (Thermo Fisher Scientific Waltham, MA, USA).

2.5. Determination of Gene Expression in C-33A, SiHa, and Ca Ski Cells by RT-qPCR

Determination of gene expression in C-33A, SiHa, and Ca Ski cells was performed by RT-qPCR using the TaqMan® RNA-to-Ct™ 1-Step Kit (4392938). Each 10 µL reaction contained 1 µL of total RNA (50 ng); 5.0 µL of TaqMan® RT-PCR Mix reaction mix (2✕), which contains AmpliTaq Gold® DNA Polymerase, UP (Ultra Pure), dNTPs (dATP, dCTP, dGTP, dTTP, and dUTP), ROX™ passive reference and optimized buffer components; 0.25 µL of TaqMan® RT Enzyme Mix (40×) containing: ArrayScript™ UP Reverse Transcriptase and RNase inhibitor; 0.5 µL of HIF1-α (ID: HS0015153153_M1), GLUT1 (ID: HS00892681_m1), LDHA (ID: HS01378790_g1), MCT4 (ID: HS00358829_m1), CAIX (ID: HS00154208_m1), and BSG (ID: HS00936295_m1) probes, respectively. The probe used as endogenous was GAPDH (ID: HS99999905_05), with 3.25 µL of nuclease-free H2O. Thermal cycling conditions were as follows: 48 °C for 15 min for retrotranscription, 95 °C for 10 min to activate DNA polymerase, 40 cycles of 95 °C for 15 s for cDNA denaturation, 60 °C for 1 min for alignment and extension on the 7500 Fast System real-time PCR system (Applied Biosystems, South San Francisco, CA 94080, USA). Gene expression was expressed as averages ± SD and determined using the 2−ΔΔCT method [26].

2.6. Overall and Relapse-Free Survival Analyses

Overall Survival (OS) analyses were performed from the TCGA dataset using the Kaplan–Meier Plotter database (https://kmplot.com/analysis/ accessed on 20 June 2022) [27]. Survival curves were estimated using the Kaplan–Meier estimator. Survival curves were compared with the log-rank test. Data were analyzed for CC using the pan-cancer expression option. 304 patients were analyzed from the database repository.

2.7. Statistical Analysis

The analysis of data obtained from the cell lines was performed by multivariate analysis using ANOVA. Post hoc Tukey tests considered a value p < 0.05 as statistically significant. Survival analyses with a p < 0.05 were considered statistically significant using the log-rank test. A p < 0.01 was considered statistically significant for expression analyses in patient samples.

3. Results

3.1. HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG Expression Is Increased in Samples from Patients with CC

Metabolic reprogramming is a key feature in cancer progression. For this reason, the expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG genes was analyzed in samples from patients with CC and normal tissue using the TCGA and GEPIA datasets [24]. The expression level of the genes of interest was obtained in 13 samples from normal tissue and 306 from patients with CC. Increased expression levels of SLC2A1, LDHA, CA9, and SLC16A3 were observed in CC. The differences were statistically significant compared to normal tissue samples. We also found that HIF-1α and BSG expression levels increased in CC compared to normal tissue (Figure 1). To confirm these results, we compared the expression of six genes to the protein level in tissue samples from the HPA dataset, and we found similar results (Figure S1).

3.2. High Expression of HIF1A Correlates with Increased Expression of SLC2A1, LDHA, CA9, SLC16A3, and BSG in CC

SLC2A1, LDHA, CA9, SLC16A3, and BSG are transcriptional targets of the active transcription factor HIF1. To evaluate whether increased expression levels of HIF-1α correlate with increased expression levels of the target genes of active HIF1, a correlation analysis was performed between expression levels of HIF-1α and SLC2A1, LDHA, CA9, SLC16A3, and BSG in the GEPIA database (Table 1). It was observed that expression of SLC2A1, LDHA, CA9, and SLC16A3 shows a positive correlation with HIF1A expression, i.e., when HIF1A expression levels increase, SLC2A1, LDHA, CA9, and SLC16A3 expression levels also increase; in all cases the data were statistically significant. However, in the case of BSG, when HIF1A levels are low, BSG expression levels increase, although the data were not statistically significant. These data suggest that in CC, the high expression of SLC2A1, LDHA, CA9, SLC16A3, and BSG is related to the high expression of the HIF-1α subunit of the active HIF1 complex.

3.3. mRNA Expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG Is Increased in CC Cell Lines

To determine whether, as in patient samples, the expression levels of HIF-1α and its target genes SLC2A1, LDHA, CA9, SLC16A3, and BSG are increased in CC cell lines, their expression was evaluated in the SiHa and Ca Ski (HPV-16-positive) and C-33A (HPV-negative) cell lines. The results obtained show that the mRNA expression level of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG increases in the Ca Ski cell line compared to C-33-A. However, a higher increase in CAIX was observed in SiHa compared to Ca Ski cells. Furthermore, in SiHa cells, an upregulation in the expression of HIF-1α and SLC16A3 was observed compared to C-33A. On the other hand, a slight decrease in LDHA and BSG expression was observed in SiHa compared to C-33A; however, this decrease was not statistically significant (Figure 2). These results suggest that HPV 16 is involved in the overexpression of HIF1A, which in turn induces the overexpression of SLC2A1, LDHA, CA9, SLC16A3, and BSG involved in metabolic reprogramming in CC.

3.4. High Expression of HIF1A, SLC2A1, LDHA, CA9, and SLC16A3 Correlates with Lower Survival in CC

Here, we found that the expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG genes is increased in CC samples (Figure 1) and in HPV-16-positive CC cell lines (Figure 2). Furthermore, high expression of HIF-1α was observed to correlate with increased expression of SLC2A1, LDHA, CA9, and SLC16A3 genes in CC patient samples (Table 1). To determine whether high expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG genes is involved in survival in patients with CC, OS analyses were performed using the Kaplan–Meier Plotter database (https://kmplot.com/analysis/ accessed on 20 June 2022). High expression of HIF1A, SLC2A1, LDHA, CA9, and SLC16A3 genes was associated with shorter OS in patients diagnosed with CC, and these differences were statistically significant (Figure 3).

4. Discussion

Under hypoxia conditions in CC, it has been observed that HPV 16 oncoproteins E6 and E7 positively regulate HIF-1α. On the one hand, E7 promotes its gene expression, and on the other hand, E6 prevents its ubiquitination by VHL and its subsequent degradation via proteasome [21]. Several studies show that active HIF1 regulates gene expression in different hallmarks of cancer. The deregulated genes are grouped into tumor suppressor genes and oncogenes. In CC, deregulation of the expression of several genes is a mechanism that promotes tumor development and progression. These genes are known to code for proteins involved in processes such as metabolic reprogramming [9,22], angiogenesis [28], cell migration, invasion, and metastasis [29,30]. High expression of GLUT1, LDHA, and MCT4 proteins has been observed in biopsies from patients with invasive cervical cancer [31]. Additionally, LDHA inhibition has resulted in cell cycle inhibition and apoptosis in nasopharyngeal carcinoma [32]. It also suppresses cell migration, increases chemo- and radiosensitivity in cancer cells [33], induces cell cycle arrest in the G2/M phase, and activates the mitochondrial apoptosis pathway in CC cells [34]. On the other hand, there is evidence that HIF1A mRNA is overexpressed in CC [35] and laryngeal squamous cell carcinoma [36], while GLUT1 is overexpressed in CC [37] and colorectal cancer [38]. However, overexpression of LDHA, CAIX, MCT4, and BSG has only been reported in other types of cancer, but not in CC. LDHA overexpression has been reported in lung adenocarcinoma [39], CAIX in breast cancer [40] and oral squamous cell carcinoma [41], MCT4 in bladder [42] and breast cancer [43], and BSG in acute myeloid leukemia [44]. In this work, the expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG was evaluated in tissue from patients with CC and normal tissue using the TCGA dataset and in HPV-negative (C-33A) and HPV-16-positive (SiHa and Ca Ski) cell lines.
The expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG was found to be increased in 306 samples from patients with CC compared with 13 samples of normal tissue. The increased expression of SLC2A1, LDHA, CA9, and SLC16A3 is statistically significant. However, the increase in HIF1A and BSG expression was not statistically significant. Increased expression was found for SLC2A1 [45], LDHA [46], CA9, SLC16A3, BSG [47] and HIF-1α [35,48]. Active HIF1 plays an important role in metabolic reprogramming in cancer by activating the transcription of genes encoding proteins involved in glucose metabolism, which promotes glucose uptake, conversion of pyruvate to lactate, pyruvate detour from mitochondria, and selective mitochondrial autophagy [49]. Active HIF1 regulates the expression of GLUT1, LDHA, CAIX [14], MCT4, and CAIX [12]. It can also bind to the promoter region of HIF-1α and promote its expression [11]. HIF-1α is the regulatory subunit in the formation of active HIF1, it is expressed under hypoxic conditions, and its expression is related to the Warburg effect in cancer [50]. These data suggest that the increased expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG could be related to HIF1 activation triggered by increased HIF1A expression. These data were confirmed in the correlation analysis, where a positive correlation between the expression of HIF-1α and SLC2A1, LDHA, CA9, and SLC16A3 was observed. Interestingly, when HIFA1 expression is increased, SLC2A1, LDHA, CA9, and SLC16A3 expression is also increased. A negative correlation was observed between HIF1A expression and BSG expression, although the data were not statistically significant.
On the other hand, in The Human Protein Atlas (https://www.proteinatlas.org/ accessed on 11 November 2022), gene expression data for HIF1A, SLC2A1, LDHA, SLC16A3, and BSG were found in 69 human cell lines. Interestingly, no data related to CA9 expression were found in SiHa, HeLa, and HaCaT cell lines (Figure S2). The data found show an increase in the expression of HIF1A, SLC2A1, and SLC16A3 transcripts, a slight increase in BSG expression, and a decrease in LDHA expression in the SiHa cell line, while in the HeLa cell line (HPV-18-positive), only increased expression of SLC16A3 was observed, compared to the immortalized human keratinocyte cell line (HaCaT). These data suggest that high-risk HPV could regulate the expression of HIF1A, SLC2A1, SLC16A3, and BSG genes in these BESEbases, which are required to carry out metabolic reprogramming. Importantly, no reports on the expression levels of these transcripts in the C33-A and Ca Ski cell lines were found on this platform.
This work evaluated the expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG transcripts in cervical cancer cell lines with and without HPV 16, compared to the HPV-negative C-33A tumor cell line, SiHa, with one to two integrated copies per cell of the HPV 16 genome, and Ca Ski with 600 integrated copies per cell of the HPV 16 genome. Increased expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG transcripts was observed in the Ca Ski cell line compared to C-33A, with statistically significant differences in the expression of all genes evaluated, except CA9. In the SiHa cell line, there were only statistically significant differences in the expression of CA9 compared to C-33A. There is also an increase in the expression of HIF1A and SLC16A3 compared to C-33A; however, a slight decrease in LDHA and BSG was observed. When comparing the data obtained in this study with the data from the Human Protein Atlas, it is observed that there is a similar behavior in the expression of LDHA; however, it has been shown that in the SiHa cell line, E6 increases indirectly the LDHA expression via downregulation of miR-34a, which is a target of p53 [51]. On the other hand, the expression observed in the Ca Ski and C-33A cell lines was similar to that observed in the 306 CC samples and the 13 normal tissue samples reported in the TCGA dataset, in which the expression of the six genes evaluated was found to be increased with statistically significant differences in SLC2A1, LDHA, CA9, and SLC16A3 (Figure 1), as in primary advanced uterine cervical carcinoma [35], human-papilloma-virus-type-16-positive and negative cervical cancer, [37] and cervical cancer [42].
These results support the theory that HPV 16 could be favoring the gene expression of HIFA1 through E7 and the formation of active HIF1 through E6 and that, in turn, active HIF1 may be inducing the expression of its target genes, such as HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG. This effect could be affected by viral load, E6, and E7 variants, or at the stage of tumor progression. Importantly, SiHa and Ca Ski cells, in addition to being HPV-16-positive, show two different stages of cancer progression, as they were derived from primary cervical carcinoma and metastatic tumor cells, respectively [52]. Ca Ski is a cervical cancer cell line established from cells from metastasis in the mesentery of the small intestine and containing the integrated HPV 16 genome of about 600 copies per cell and the E-G131/G350 variants of E6 and E7-Prototype [53]. On the other hand, the SiHa cell line was established from primary uterine squamous cell carcinoma tissue. It contained the integrated HPV 16 genome of one to two copies per cell and the E-G350/C442 variants of E6 and E7-C645 [54,55], given that the E6 variants of HPV 16 have an oncogenic potential difference and induce the differential expression of several genes [56].These particularities of the cell lines could explain why, although the two cell lines contain HPV 16, in Ca Ski, there is a high expression of HIF1A, SLC2A1, LDHA, SLC16A3, and BSG, whereas in SiHa, there is a higher expression of CA9.
Changes in mRNA expression of various genes are often used to establish associations between gene transcription and disease stage. Previous studies have shown that high expression of LDHA is involved in cell proliferation and survival, migration, invasion, angiogenesis, and immune evasion in cancer, indicating that LDHA may be a potential prognostic marker and therapeutic target in cancer [7,57]. High SLC2A1 expression and HPV 16 have been reported to be independent prognostic factors in patients with CC [37]. On the other hand, increased expression of MCT1 and MCT4 is generally associated with poor prognosis. MCT4 is overexpressed in different types of cancer, such as breast, bladder, colorectal, and CC cancers. Moreover, high expression of MCT4 is closely associated with increased expression of CAIX and BSG [16,22].
Regarding CAIX, its expression has been reported to regulate epithelial–mesenchymal transition and cell migration in CC [58]. In contrast, high expression of BSG has been correlated with radioresistance in the CC cell line SiHa [59]. Furthermore, it has been reported that shorter survival of patients in all types of breast cancer, especially in those with the triple-negative phenotype, is associated with high expression of HIF-1α [60], CA9 [61], and BSG [62]; LDHA in lung adenocarcinoma [39]; SLC16A3 in bladder cancer [42]; and SLC2A1 in colorectal cancer [38]. Likewise, in this study, the overall survival analysis with Kaplan–Meier curves shows that high expression of HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG is associated with worse survival in patients with CC (Figure 3) in which HPV 16 is the leading etiological agent. All these data support the theory that a higher expression of the transcripts evaluated here is associated with HPV 16 and a worse prognosis in CC.

5. Conclusions

In conclusion, these results suggest that HPV 16 increases the expression of active HIF1 target genes, HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG, in the Ca Ski cell line and in patients with CC. On the other hand, the high expression of these genes is related to lower survival in patients with CC, denoting the importance of studying these genes and their possible use as prognostic biomarkers. This poor survival could be related to viral load, HPV 16 E6 and E7 variants, or stage of tumor progression; however, further studies are needed in this regard.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pathogens12010033/s1, Figure S1: HIF-1α, GLUT1, LDHA, CA9, MCT4, and BSG protein expression increases in CC. Figure S2: HIF-1α, GLUT1, LDHA, MCT4, and BSG transcript expression in HaCaT, HeLa, and SiHa cervical cancer cell lines.

Author Contributions

Conceptualization, J.O.-O.; methodology, V.D.P.-H., A.A.-I., D.G.S.-F., N.S.-R. and M.D.F.-V.; validation, H.J.-W., D.N.M.-C., D.G.S.-F., E.G.S.-B. and J.O.-O.; formal analysis, N.N.-T., J.O.-O., M.A.L.-V., D.N.M.-C. and E.G.S.-B.; investigation, H.J.-W., B.I.-A. and L.d.C.A.-R.; resources, V.D.P.-H., A.A.-I. and J.O.-O.; writing—original draft preparation, V.D.P.-H., N.N.-T., J.O.-O. and L.d.C.A.-R.; writing—review and editing, J.O.-O., N.N.-T. and A.A.-I.; visualization, V.D.P.-H., A.A.-I. and J.O.-O.; supervision, J.O.-O., M.A.L.-V., B.I.-A. and L.d.C.A.-R.; project administration, J.O.-O.; funding acquisition, J.O.-O. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by SEP-CONACYT CB-2017-2018 A1-S-43704 and Universidad Autónoma de Guerrero funding for seed research projects 2018.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No applicable.

Acknowledgments

Victor Daniel Priego-Hernandez received a Master’s degree scholarship from CONACYT program, 2020-000026-02NACF-03120. Adan Arizmendi-Izazaga was supported by the CONACYT Predoctoral Training Grant. Norma Santiago-Ramón was supported by the CONACYT, 30264.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA. Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  2. Wang, X.; Huang, X.; Zhang, Y. Involvement of human papillomaviruses in cervical cancer. Front. Microbiol. 2018, 9, 1–14. [Google Scholar] [CrossRef] [PubMed]
  3. Martínez-Ramírez, I.; Carrillo-García, A.; Contreras-Paredes, A.; Ortiz-Sánchez, E.; Cruz-Gregorio, A.; Lizano, M. Regulation of cellular metabolism by high-risk human papillomaviruses. Int. J. Mol. Sci. 2018, 19, 1839. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Tommasino, M. The human papillomavirus family and its role in carcinogenesis. Semin. Cancer Biol. 2014, 26, 13–21. [Google Scholar] [CrossRef] [PubMed]
  5. Li, J.; Huang, Q.; Long, X.; Guo, X.; Sun, X.; Jin, X.; Li, Z.; Ren, T.; Yuan, P.; Huang, X.; et al. Mitochondrial elongation-mediated glucose metabolism reprogramming is essential for tumour cell survival during energy stress. Oncogene 2017, 36, 4901–4912. [Google Scholar] [CrossRef]
  6. Park, S.; Smith, C.; Wilbur, R.; Cain, C.; Kallu, S.; Valasapalli, S.; Sahoo, A.; Guda, M.; Tsung, A.; Velpula, K. An overview of MCT1 and MCT4 in GBM: Small molecule transporters with large implications. Am. J. Cancer Res. 2018, 8, 1967–1976. Available online: http://www.ncbi.nlm.nih.gov/pubmed/30416849 (accessed on 10 March 2021).
  7. Feng, Y.; Xiong, Y.; Qiao, T.; Li, X.; Jia, L.; Han, Y. Lactate dehydrogenase A: A key player in carcinogenesis and potential target in cancer therapy. Cancer Med. 2018, 7, 6124–6136. [Google Scholar] [CrossRef] [Green Version]
  8. Walenta, S.; Wetterling, M.; Lehrke, M.; Schwickert, G.; Sundfør, K.; Rofstad, E.; Mueller-Klieser, W. High lactate levels predict likelihood of metastases, tumor recurrence, and restricted patient survival in human cervical cancers. Cancer Res. 2000, 60, 916–921. [Google Scholar]
  9. Soga, T. Cancer metabolism: Key players in metabolic reprogramming. Cancer Sci. 2013, 104, 275–281. [Google Scholar] [CrossRef]
  10. Xu, S.; Ying, K. Association between HIF-1α gene polymorphisms and lung cancer: A meta-analysis. Medicine 2020, 99, e20610. [Google Scholar] [CrossRef]
  11. Koyasu, S.; Kobayashi, M.; Goto, Y.; Hiraoka, M.; Harada, H. Regulatory mechanisms of hypoxia-inducible factor 1 activity: Two decades of knowledge. Cancer Sci. 2018, 109, 560–571. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Wenger, R.; Stiehl, D.; Camenisch, G. Integration of oxygen signaling at the consensus HRE. Sci. STKE 2005, 2005, 1–14. [Google Scholar] [CrossRef] [PubMed]
  13. Albadari, N.; Deng, S.; Li, W. The transcriptional factors HIF-1 and HIF-2 and their novel inhibitors in cancer therapy. Expert Opin. Drug Discov. 2019, 14, 667–682. [Google Scholar] [CrossRef] [PubMed]
  14. Lee, J.-W.; Bae, S.-H.; Jeong, J.-W.; Kim, S.-H.; Kim, K.-W. Hypoxia-inducible factor (HIF-1)α: Its protein stability and biological functions. Exp. Mol. Med. 2004, 36, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Semenza, G. Targeting HIF-1 for cancer therapy. Nat. Rev. Cancer 2003, 3, 721–732. [Google Scholar] [CrossRef]
  16. Payen, V.; Mina, E.; Van Hée, V.; Porporato, P.; Sonveaux, P. Monocarboxylate transporters in cancer. Mol. Metab. 2019, 33, 1–19. [Google Scholar] [CrossRef]
  17. Mulukutla, B.C.; Yongky, A.; Le, T.; Mashek, D.; Hu, W. Regulation of Glucose Metabolism–A Perspective from Cell Bioprocessing. Trends Biotechnol. 2016, 34, 638–651. [Google Scholar] [CrossRef]
  18. Sowa, T.; Menju, T.; Chen-Yoshikawa, T.; Takahashi, K.; Nishikawa, S.; Nakanishi, T.; Shikuma, K.; Motoyama, H.; Hijiya, K.; Aoyama, A.; et al. Hypoxia-inducible factor 1 promotes chemoresistance of lung cancer by inducing carbonic anhydrase IX expression. Cancer Med. 2017, 6, 288–297. [Google Scholar] [CrossRef]
  19. Ward, C.; Meehan, J.; Gray, M.; Kunkler, I.; Langdon, S.; Argyle, D. Carbonic anhydrase IX (CAIX), cancer, and radiation responsiveness. Metabolites 2018, 8, 13. [Google Scholar] [CrossRef] [Green Version]
  20. Ullah, M.; Davies, A.; Halestrap, A. The plasma membrane lactate transporter MCT4, but not MCT1, is up-regulated by hypoxia through a HIF-1α-dependent mechanism. J. Biol. Chem. 2006, 281, 9030–9037. [Google Scholar] [CrossRef] [Green Version]
  21. Guo, Y.; Meng, X.; Ma, J.; Zheng, Y.; Wang, Q.; Wang, Y.; Shang, H. Human papillomavirus 16 E6 contributes HIF-1α induced warburg effect by attenuating the VHL-HIF-1α interaction. Int. J. Mol. Sci. 2014, 15, 7974–7986. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Pinheiro, C.; Garcia, E.; Morais-Santos, F.; Scapulatempo-Neto, C.; Mafra, A.; Steenbergen, R.; Boccardo, E.; Villa, L.; Baltazar, F.; Longatto-Filho, A. Lactate transporters and vascular factors in HPV-induced squamous cell carcinoma of the uterine cervix. BMC Cancer 2014, 14, 1–12. [Google Scholar] [CrossRef] [PubMed]
  23. Li, B.; Sui, L. Metabolic reprogramming in cervical cancer and metabolomics perspectives. Nutr. Metab. 2021, 18, 1–14. [Google Scholar] [CrossRef] [PubMed]
  24. Tang, Z.; Li, C.; Kang, B.; Gao, G.; Li, C.; Zhang, Z. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017, 45, W98–W102. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Uhlén, M.; Fagerberg, L.; Hallström, B.M.; Lindskog, C.; Oksvold, P.; Mardinoglu, A.; Sivertsson, Å.; Kampf, C.; Sjöstedt, E.; Asplund, A.; et al. Tissue-based map of the human proteome. Science 2015, 347, 1260419. [Google Scholar] [CrossRef] [PubMed]
  26. Livak, K.; Schmittgen, T. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef]
  27. Lánczky, A.; Győrffy, B. Web-based survival analysis tool tailored for medical research (KMplot): Development and implementation. J. Med. Internet Res. 2021, 23, 1–7. [Google Scholar] [CrossRef]
  28. Liao, D.; Johnson, R. Hypoxia: A key regulator of angiogenesis in cancer. Cancer Metastasis Rev. 2007, 26, 281–290. [Google Scholar] [CrossRef]
  29. Chan, D.; Giaccia, A. Hypoxia, gene expression, and metastasis. Cancer Metastasis Rev. 2007, 26, 333–339. [Google Scholar] [CrossRef]
  30. Sullivan, R.; Graham, C. Hypoxia-driven selection of the metastatic phenotype. Cancer Metastasis Rev. 2007, 26, 319–331. [Google Scholar] [CrossRef]
  31. Reyna-Hernández, M.A.; Alarcón-Romero, L.D.C.; Ortiz-Ortiz, J.; Illades-Aguiar, B.; Jiménez-López, M.A.; Ocampo-Bárcenas, A.; Morrugares-Ixtepan, M.O.; Torres-Rojas, F.I. GLUT1, LDHA, and MCT4 Expression Is Deregulated in Cervical Cancer and Precursor Lesions. J. Histochem. Cytochem. 2022, 70, 437–446. [Google Scholar] [CrossRef]
  32. Yang, Y.; Su, D.; Zhao, L.; Zhang, D.; Xu, J.; Wan, J.; Fan, S.; Chen, M. Different effects of LDH-A inhibition by oxamate in non-small cell lung cancer cells. Oncotarget 2014, 5, 11886–11896. [Google Scholar] [CrossRef] [PubMed]
  33. Maftouh, M.; Avan, A.; Sciarrillo, R.; Granchi, C.; Leon, L.G.; Rani, R.; Funel, N.; Smid, K.; Honeywell, R.; Boggi, U.; et al. Synergistic interaction of novel lactate dehydrogenase inhibitors with gemcitabine against pancreatic cancer cells in hypoxia. Br. J. Cancer 2014, 110, 172–182. [Google Scholar] [CrossRef] [PubMed]
  34. Zhang, W.; Wang, C.; Hu, X.; Lian, Y.; Ding, C.; Ming, L. Inhibition of LDHA suppresses cell proliferation and increases mitochondrial apoptosis via the JNK signaling pathway in cervical cancer cells. Oncol. Rep. 2022, 47, 1–11. [Google Scholar] [CrossRef] [PubMed]
  35. Łuczak, M.; Roszak, A.; Pawlik, P.; Kedzia, H.; Lianeri, M.; Jagodziński, P.P. Increased expression of HIF-1A and its implication in the hypoxia pathway in primary advanced uterine cervical carcinoma. Oncol. Rep. 2011, 26, 1259–1264. [Google Scholar] [CrossRef] [PubMed]
  36. Woś, J.; Bryś, M.; Lewy-Trenda, I.; Stasikowska, O.; Papie, P.; Papierz, W.; Starska, K. Analiza ekspresji HIF-1α i COX-2 w utkaniu guza oraz korelacja ze stopniem inwazyjności zmian nowotworowych u chorych z rakiem krtani-Badania wstpne. Otolaryngol. Pol. 2011, 65, 102–108. [Google Scholar] [CrossRef]
  37. Kim, B.; Chang, J. Differential effect of GLUT1 overexpression on survival and tumor immune microenvironment of human papilloma virus type 16-positive and -negative cervical cancer. Sci. Rep. 2019, 9, 1–7. [Google Scholar] [CrossRef] [Green Version]
  38. Feng, W.; Cui, G.; Tang, C.; Zhang, X.; Dai, C.; Xu, Y.; Gong, H.; Xue, T.; Guo, H.; Bao, Y. Role of glucose metabolism related gene GLUT1 in the occurrence and prognosis of colorectal cancer. Oncotarget 2017, 8, 56850–56857. [Google Scholar] [CrossRef] [Green Version]
  39. Yu, C.; Hou, L.; Cui, H.; Zhang, L.; Tan, X.; Leng, X.; Li, Y. LDHA upregulation independently predicts poor survival in lung adenocarcinoma, but not in lung squamous cell carcinoma. Futur. Oncol. 2018, 14, 2483–2492. [Google Scholar] [CrossRef]
  40. Tafreshi, N.; Lloyd, M.; Proemsey, J.; Bui, M.; Kim, J.; Gillies, R.; Morse, D. Evaluation of CAIX and CAXII Expression in Breast Cancer at Varied O2 Levels: CAIX is the Superior Surrogate Imaging Biomarker of Tumor Hypoxia. Mol. Imaging Biol. 2016, 18, 219–231. [Google Scholar] [CrossRef]
  41. Eckert, A.; Horter, S.; Bethmann, D.; Kotrba, J.; Kaune, T.; Rot, S.; Bache, M.; Bilkenroth, U.; Reich, W.; Greither, T.; et al. Investigation of the prognostic role of carbonic anhydrase 9 (CAIX) of the cellular mRNA/protein level or soluble CAIX protein in patients with oral squamous cell carcinoma. Int. J. Mol. Sci. 2019, 20, 375. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Zhao, Y.; Zhao, B.; Yan, W.; Xia, Y.; Wang, Z.; Zheng, G.; Da Wang, W.; Zhang, Y. Integrative Analysis Identified MCT4 as an Independent Prognostic Factor for Bladder Cancer. Front. Oncol. 2021, 11, 1–10. [Google Scholar] [CrossRef] [PubMed]
  43. Yuan, C.; Zhang, J.; Lou, J.; Wang, S.; Jiang, Y.; Wu, F.; Wang, S. Comprehensive Analysis of Monocarboxylate Transporter 4 (MCT4) expression in breast cancer prognosis and immune infiltration via integrated bioinformatics analysis. Bioengineered 2021, 12, 3850–3863. [Google Scholar] [CrossRef] [PubMed]
  44. Łacina, P.; Butrym, A.; Turlej, E.; Stachowicz-Suhs, M.; Wietrzyk, J.; Mazur, G.; Bogunia-Kubik, K. BSG (CD147) Serum Level and Genetic Variants Are Associated with Overall Survival in Acute Myeloid Leukaemia. J. Clin. Med. 2022, 11, 332. [Google Scholar] [CrossRef] [PubMed]
  45. Zhang, X.; Pang, X.; Zhang, Z.; Liu, Q.; Zhang, H.; Xiang, Q.; Cui, Y. Co-expression and prognosis analyses of GLUT1–4 and RB1 in breast cancer. BMC Cancer 2021, 21, 1–10. [Google Scholar] [CrossRef] [PubMed]
  46. Guddeti, R.; Bali, P.; Karyala, P.; Pakala, S. MTA1 coregulator regulates LDHA expression and function in breast cancer. Biochem. Biophys. Res. Commun. 2019, 520, 54–59. [Google Scholar] [CrossRef]
  47. Bonatelli, M.; Fornari, I.; Bernécule, P.; Pinheiro, L.; Costa, R.; Longatto-Filho, A.; Junior, J.; Silva, E.; Cárcano, F.; Pinheiro, C. Expression of Glycolysis-Related Proteins in Cancer of Unknown Primary Origin. Front. Oncol. 2021, 11, 1–10. [Google Scholar] [CrossRef]
  48. Mansour, R.; Enderami, S.; Ardeshirylajimi, A.; Fooladsaz, K.; Fathi, M.; Ganji, S. Evaluation of hypoxia inducible factor-1 alpha gene expression in colorectal cancer stages of Iranian patients. J. Cancer Res. Ther. 2016, 12, 1313–1317. [Google Scholar] [CrossRef]
  49. Semenza, G. HIF-1: Upstream and downstream of cancer metabolism. Curr. Opin. Genet. Dev. 2010, 20, 51–56. [Google Scholar] [CrossRef] [Green Version]
  50. Ke, Q.; Costa, M. Hypoxia-Inducible Factor-1 (HIF-1). Mol. Pharmacol. 2006, 70, 1469–1480. [Google Scholar] [CrossRef]
  51. Zhang, R.; Su, J.; Xue, S.; Yang, H.; Ju, L.; Ji, Y.; Wu, K.; Zhang, Y. Original Article inhibits Warburg effect through targeting LDHA in cervical cancer. Am. J. Cancer Res. 2016, 6, 312–320. [Google Scholar] [PubMed]
  52. Shagieva, G.; Domnina, L.; Makarevich, O.; Chernyak, B.; Skulachev, V.; Dugina, V. Depletion of mitochondrial reactive oxygen species downregulates epithelial-to-mesenchymal transition in cervical cancer cells. Oncotarget 2017, 8, 4901–4913. [Google Scholar] [CrossRef] [PubMed]
  53. Pattillo, R.; Hussa, R.; Story, M.; Ruckert, A.; Shalaby, M.; Mattingly, R. Tumor Antigen and Human Chorionic Gonadotropin in CaSki Cells: A New Epidermoid Cervical Cancer Cell Line Abstract. Obstet. Gynecol. Surv. 1978, 33, 56–57. [Google Scholar] [CrossRef]
  54. Meissner, J. Nucleotide sequences and further characterization of human papillomavirus DNA present in the CaSki, SiHa and HeLa cervical carcinoma cell lines. J. Gen. Virol. 1999, 80, 1725–1733. [Google Scholar] [CrossRef] [PubMed]
  55. Baker, C.; Phelps, W.; Lindgren, V.; Braun, M.; Gonda, M.; Howley, P. Structural and transcriptional analysis of human papillomavirus type 16 sequences in cervical carcinoma cell lines. J. Virol. 1987, 61, 962–971. [Google Scholar] [CrossRef] [Green Version]
  56. Zacapala-Gómez, A.E.; Del Moral-Hernández, O.; Villegas-Sepúlveda, N.; Hidalgo-Miranda, A.; Romero-Córdoba, S.L.; Beltrán-Anaya, F.O.; Leyva-Vázquez, M.A.; Alarcón-Romero, L.D.C.; Illades-Aguiar, B. Changes in global gene expression profiles induced by HPV 16 E6 oncoprotein variants in cervical carcinoma C33-A cells. Virology 2016, 488, 187–195. [Google Scholar] [CrossRef] [Green Version]
  57. Cui, X.; Han, Z.; He, S.; Da Wu, X.; Chen, T.; Shao, C.; Chen, D.; Su, N.; Chen, Y.; Wang, T.; et al. HIF1/2α mediates hypoxia-induced LDHA expression in human pancreatic cancer cells. Oncotarget 2017, 8, 24840–24852. [Google Scholar] [CrossRef]
  58. Hsin, M.; Hsieh, Y.; Hsiao, Y.; Chen, P.; Wang, P.; Yang, S. Carbonic anhydrase ix promotes human cervical cancer cell motility by regulating pfkfb4 expression. Cancers 2021, 13, 1174. [Google Scholar] [CrossRef]
  59. Ju, X.; Liang, S.; Zhu, J.; Ke, G.; Wen, H.; Wu, X. Extracellular matrix metalloproteinase inducer (CD147/ BSG/EMMPRIN)-induced radioresistance in cervical cancer by regulating the percentage of the cells in the G2/m phase of the cell cycle and the repair of DNA Double-strand Breaks (DSBs). Am. J. Transl. Res. 2016, 8, 2498–2511. [Google Scholar]
  60. Shamis, S.; McMillan, D.; Edwards, J. The relationship between hypoxia-inducible factor 1α (HIF-1α) and patient survival in breast cancer: Systematic review and meta-analysis. Crit. Rev. Oncol. Hematol. 2021, 159, 103231. [Google Scholar] [CrossRef]
  61. Chen, Z.; Ai, L.; Mboge, M.; Tu, C.; McKenna, R.; Brown, K.; Heldermon, C.; Frost, S. Differential expression and function of CAIX and CAXII in breast cancer: A comparison between tumorgraft models and cells. PLoS ONE 2018, 13, 1–25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Liu, M.; Tsang, J.; Lee, M.; Ni, Y.; Chan, S.; Cheung, S.; Hu, J.; Hu, H.; Tse, G. CD147 expression is associated with poor overall survival in chemotherapy treated triple-negative breast cancer. J. Clin. Pathol. 2018, 71, 1007–1014. [Google Scholar] [CrossRef] [PubMed]
Pathogens 12 00033 g001a 550Pathogens 12 00033 g001b 550
Figure 1. Expression of HIF1A and its target genes SLC2A1, LDHA, CA9, SLC16A3, and BSG in biopsies of patients with CC (T) (red box) and normal tissue (N) (grey box), obtained from the TCGA dataset, is shown. * p < 0.01. Expression was log2 transformed (TPM+1).
Figure 1. Expression of HIF1A and its target genes SLC2A1, LDHA, CA9, SLC16A3, and BSG in biopsies of patients with CC (T) (red box) and normal tissue (N) (grey box), obtained from the TCGA dataset, is shown. * p < 0.01. Expression was log2 transformed (TPM+1).
Pathogens 12 00033 g001aPathogens 12 00033 g001b
Pathogens 12 00033 g002a 550Pathogens 12 00033 g002b 550
Figure 2. Relative expression of genes involved in metabolic reprogramming. The relative expression level of HIFA1 (a), SLC2A1 (b), LDHA (c), CA9 (d), SLC16A3 (e), and BSG (f) in the SiHa and Ca SKi cell compared to C33-A. A value of p < 0.05 was considered statistically significant through a one-way ANOVA test and using mean and standard error. Data were measured in three independent experiments in triplicate in RT-qPCR and calculated by the 2−ΔΔCT method. Expression of the six transcripts was normalized to endogenous GAPDH. Relative expression levels were analyzed in GraphPad Prism software. * p < 0.05; ** p < 0.001.
Figure 2. Relative expression of genes involved in metabolic reprogramming. The relative expression level of HIFA1 (a), SLC2A1 (b), LDHA (c), CA9 (d), SLC16A3 (e), and BSG (f) in the SiHa and Ca SKi cell compared to C33-A. A value of p < 0.05 was considered statistically significant through a one-way ANOVA test and using mean and standard error. Data were measured in three independent experiments in triplicate in RT-qPCR and calculated by the 2−ΔΔCT method. Expression of the six transcripts was normalized to endogenous GAPDH. Relative expression levels were analyzed in GraphPad Prism software. * p < 0.05; ** p < 0.001.
Pathogens 12 00033 g002aPathogens 12 00033 g002b
Pathogens 12 00033 g003 550
Figure 3. Overall survival analysis. Kaplan–Meier curve of overall survival by expression of HIF1A (a), SLC2A1 (b), LDHA (c), CA9 (d), SLC16A3 (e), and BSG (f) in patients with CC. The data show the probability of survival for 200 months, the time during which the levels of the transcripts were studied. The lines in red show high levels, and the gray color shows low levels of gene expression. Numbers below the plots indicate the number of patients during baseline, 50, 100, 100, 150, and 200 months of expression analysis. p < 0.05 were considered statistically significant.
Figure 3. Overall survival analysis. Kaplan–Meier curve of overall survival by expression of HIF1A (a), SLC2A1 (b), LDHA (c), CA9 (d), SLC16A3 (e), and BSG (f) in patients with CC. The data show the probability of survival for 200 months, the time during which the levels of the transcripts were studied. The lines in red show high levels, and the gray color shows low levels of gene expression. Numbers below the plots indicate the number of patients during baseline, 50, 100, 100, 150, and 200 months of expression analysis. p < 0.05 were considered statistically significant.
Pathogens 12 00033 g003
Table
Table 1. Correlation analysis between HIF-1α expression levels and HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG expression levels.
Table 1. Correlation analysis between HIF-1α expression levels and HIF1A, SLC2A1, LDHA, CA9, SLC16A3, and BSG expression levels.
HIF-1α Target GenesCESC (Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma)
Tumor
Rp
SLC2A10.27***
LDHA0.41***
CA90.14**
SLC16A30.31***
BSG−0.260.66
Tumor, tissue correlation analysis TCGA. (**) p < 0.01, (***) p < 0.001. R (correlation) and p (p-value).
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Priego-Hernández, V.D.; Arizmendi-Izazaga, A.; Soto-Flores, D.G.; Santiago-Ramón, N.; Feria-Valadez, M.D.; Navarro-Tito, N.; Jiménez-Wences, H.; Martínez-Carrillo, D.N.; Salmerón-Bárcenas, E.G.; Leyva-Vázquez, M.A.; et al. Expression of HIF-1α and Genes Involved in Glucose Metabolism Is Increased in Cervical Cancer and HPV-16-Positive Cell Lines. Pathogens 2023, 12, 33. https://doi.org/10.3390/pathogens12010033

AMA Style

Priego-Hernández VD, Arizmendi-Izazaga A, Soto-Flores DG, Santiago-Ramón N, Feria-Valadez MD, Navarro-Tito N, Jiménez-Wences H, Martínez-Carrillo DN, Salmerón-Bárcenas EG, Leyva-Vázquez MA, et al. Expression of HIF-1α and Genes Involved in Glucose Metabolism Is Increased in Cervical Cancer and HPV-16-Positive Cell Lines. Pathogens. 2023; 12(1):33. https://doi.org/10.3390/pathogens12010033

Chicago/Turabian Style

Priego-Hernández, Víctor D., Adán Arizmendi-Izazaga, Diana G. Soto-Flores, Norma Santiago-Ramón, Milagros D. Feria-Valadez, Napoleón Navarro-Tito, Hilda Jiménez-Wences, Dinorah N. Martínez-Carrillo, Eric G. Salmerón-Bárcenas, Marco A. Leyva-Vázquez, and et al. 2023. "Expression of HIF-1α and Genes Involved in Glucose Metabolism Is Increased in Cervical Cancer and HPV-16-Positive Cell Lines" Pathogens 12, no. 1: 33. https://doi.org/10.3390/pathogens12010033

APA Style

Priego-Hernández, V. D., Arizmendi-Izazaga, A., Soto-Flores, D. G., Santiago-Ramón, N., Feria-Valadez, M. D., Navarro-Tito, N., Jiménez-Wences, H., Martínez-Carrillo, D. N., Salmerón-Bárcenas, E. G., Leyva-Vázquez, M. A., Illades-Aguiar, B., Alarcón-Romero, L. d. C., & Ortiz-Ortiz, J. (2023). Expression of HIF-1α and Genes Involved in Glucose Metabolism Is Increased in Cervical Cancer and HPV-16-Positive Cell Lines. Pathogens, 12(1), 33. https://doi.org/10.3390/pathogens12010033

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