Abstract
Purpose
This study aimed to analyze the prognostic and diagnostic value of Lactate dehydrogenase A (LDHA) and solute carrier family 16 member 1 (SLC16A1) in low-grade gliomas (LGG).
Methods
Gene expression datasets for LGG were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases. The prognostic value of LDHA and SLC16A1 in LGG was analyzed using the survival package. Receiver operating characteristic (ROC) curves were drawn to evaluate the ability of the model to distinguish between patients with LGG and controls. Gene set enrichment analysis (GSEA) of single gene was utilized to explore the potential biological function of the two genes. The protein levels of LDHA and SLC16A1 were analyzed using the Human Protein Atlas database. LDHA and SLC16A1 expression was verified using real-time reverse transcription polymerase chain reaction. Finally, the effects of low SLC16A1 expression on the proliferation, migration, and invasion of LGG cells were investigated using CCK-8 and Transwell assays.
Results
LDHA was downregulated, and SLC16A1 was upregulated in LGG tissues compared to normal tissues in TCGA dataset. Kaplan–Meier (K–M) survival and ROC curves revealed that these two genes have potential prognostic and diagnostic performances. LDHA positively correlated with SLC16A1 in TCGA and CGGA cohorts. GSEA demonstrated that LDHA is involved in the chemokine and NOD-like receptor signaling pathways, whereas SLC16A1 is involved in the JAK-STAT and NOD-like receptor signaling pathways. Immunohistochemical profiles of LDHA and SLC16A1 were consistent with their mRNA expression levels. SLC16A1 overexpression and downregulation of LDHA have been validated in glioma cell lines. Additionally, low SLC16A1 expression inhibited the proliferation, migration, and invasion of glioma cells.
Conclusion
LDHA and SLC16A1 have potential prognostic and diagnostic values for LGG. Therefore, SLC16A1 may serve as a potential biomarker for the diagnosis and treatment of LGG.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12672-025-03297-2.
Keywords: Lower-grade glioma, LDHA, SLC16A1, Prognosis, Diagnosis
Introduction
Gliomas are the most common malignant neural epithelial tumors with high morbidity and recurrence rates. According to the tumor histopathology, gliomas are divided into four World Health Organization (WHO) grades, ranging from benign grade I tumors to highly malignant grade IV gliomas [1]. Low-grade glioma (LGG), including grades I and II as classified by the WHO, is usually slow-growing, intermittently progressive, and invasive, accounting for approximately 22% of all adult brain tumors [2]. However, recent developments have shown that classification is unnecessary and have led to the term “LGG” to designate both grades II and III gliomas [2, 3]. Although epileptic seizures is the most common manifestation in grade II gliomas, some patients with LGG is asymptomatic, which affects patient prognosis [4]. Thus, it is necessary to identify specific biomarkers for the individualized treatment and prognosis of LGG.
In recent years, molecular markers for the diagnosis, prediction, and prognosis of gliomas have been extensively explored. Ali et al. found that miR-29a, miR-106a, and miR-200a can serve as biomarkers for monitoring therapeutic efficacy of patients with glioblastoma (GBM) [5]. Another study confirmed that patients with GBM with IDH1 mutations and MGMT methylation have better survival patterns, and when the two were used in combination, the treatment effect and survival rate improved [6]. Swellam et al. found that NDRG2 methylation in patients with GBM significantly increased with the deterioration of overall survival and progression-free survival, and that NDRG2 methylation levels can serve as an effective biomarker for diagnosis and prognosis [7]. Metabolic reprogramming is a hallmark of cancer [8, 9]. Lactate dehydrogenase A (LDHA) is a key glycolytic enzyme that converts pyruvate to lactate in the final step of glycolysis [9, 10]. Lactate has been suggested to promote the survival, invasiveness, resistance, and activation of oncogenic signaling pathways in cancer cells [11]. Lactate is a hydrophilic and weak acid; thus, its transport across membranes requires transporters belonging to the monocarboxylate transporter (MCT) family [12]. It has been reported that lactate uptake is mediated by MCT1 (encoded by SLC16A1). MCT1 upregulates the lactic acid exchange [13]. A recent study demonstrated that the growth and proliferation of intestinal and intraductal papillary mucinous neoplasms and hepatocellular carcinoma subtype tumors mainly rely on lactate dehydrogenases (such as LDHA) and SLC16A1 gene expression [14]. However, the roles of LDHA and SLC16A1 in LGG development have not been reported to our best knowledge.
In the present study, we investigated the roles of LDHA and SLC16A1 in LGG. The expression of these two genes in tumor and normal tissues was investigated. Furthermore, the prognostic value of these two genes in LGG was evaluated. We performed gene set enrichment analysis (GSEA) of LDHA and SLC16A1 to investigate the potential mechanisms of the two genes in LGG progression. The protein levels of LDHA and SLC16A1 were analyzed using the Human Protein Atlas (HPA) database, and the differential expression and function of LDHA and SLC16A1 were validated in glioma cell lines.
Methods
Data sources
Gene expression data for LGG were obtained from the Cancer Genome Atlas (TCGA) database [15]. Moreover, 105 normal cerebral cortex samples from the Genotype-Tissue Expression (GTEx) database were used as controls. According to the clinical information of the LGG samples downloaded simultaneously, disease samples with prognostic information were retained, and 628 samples were obtained, including 523 LGG samples and 105 controls. Among the 523 LGG samples, 518 had complete survival information and were used for prognostic analyses. Grade I gliomas are rare, and grade IV gliomas present with superior malignancy. The TCGA database classifies grades II and III gliomas as LGG [16]. Thus, LGG was defined as a grade II or III glioma in the following investigation. The inclusion and exclusion criteria are as follows: (1) Samples from patients with grades II and III in TCGA were included, with grades I and IV excluded from the analysis; (2) Standardized patient follow-up data, patients with complete survival information were used for follow-up prognostic analysis; (3) Data sets that include the largest possible sample size; (4) All expression values are log2 logization. The RNA-seq dataset, including 172 LGG samples, was downloaded from the Chinese Glioma Genome Atlas (CGGA) database [17], and the survival information of the patients was downloaded to analyze the prognosis of LDHA and SLC16A1. Two expression datasets, GSE15824 and GSE16011, were downloaded from the Gene Expression Omnibus (GEO) database. GSE15824 included 45 samples (five control samples and 40 tumor samples from 26 GBM and 14 LGG), which were sequenced on a GPL570 ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. GSE16011contained 284 cases (eight control samples, 117 LGG, and 159 GBM samples), and the sequencing platform used was GPL8542 (Affymetrix GeneChip Human Genome U133 Plus 2.0 array [CDF: Hs133P_Hs_ENTREZG.cdf]). Owing to the lack of survival information, the GSE15824 and GSE16011 datasets were only used for expression pattern and diagnostic value analysis of LDHA and SLC16A1. The clinicopathological features of the patients were shown in Table 1. Details of the sample size are shown in Table 2.
Table 1.
The clinicopathological features of the patients
| Cohort | TCGA-LGG(n = 518) | CGGA-LGG(n = 172) |
|---|---|---|
| Gender | ||
| Male | 287 | 106 |
| Female | 231 | 66 |
| Age(years) | ||
| < 60 | 448 | 160 |
| ≥ 60 | 70 | 12 |
| Race | – | |
| White | 477 | |
| Black or African American | 22 | |
| Asian | 8 | |
| American Indian or Alaska native | 1 | |
| Not reported | 10 | |
| Vital_status | ||
| Alive | 388 | 82 |
| Dead | 130 | 90 |
| Primary_diagnosis.diagnoses | – | |
| Mixed glioma | 130 | |
| Oligodendroglioma, NOS | 114 | |
| Astrocytoma, NOS | 66 | |
| Astrocytoma, anaplastic | 129 | |
| Oligodendroglioma, anaplastic | 79 | |
| IDH | ||
| Mutant | 407 | 127 |
| Wildtype | 111 | 44 |
| NA | – | 1 |
Table 2.
Details of sample size
| Database | Samples |
|---|---|
| TCGA | 523 LGG samples (518 samples had survival information for prognostic analysis) |
| GTEx | 105 control samples |
| CGGA (CGGA-325) | 172 LGG samples (Prognostic analysis) |
| GEO (GSE15824) | 5 control samples, 26 GBM samples, 14 LGG samples (Gene expression and diagnostic analysis) |
| GEO (GSE16011) | 8 control samples, 159 GBM samples, 117 LGG samples (Gene expression and diagnostic analysis) |
Download the mRNA probe expression matrix file corresponding to each dataset, and download the annotation file corresponding to the sequencing platform. Converts probes to gene symbol one by one, removing probes that do not match the gene symbol. For different probes mapped to the same gene, the average value was taken as the expression value of the gene, that is, the gene expression matrix was obtained for subsequent analysis.
Expression profiles of LDHA/SLC16A1 in LGG and controls
The expression levels of LDHA and SLC16A1 in the TCGA + GTEx, GSE15824, and GSE16011 cohorts were analyzed. Differences between LDHA and SLC16A1 in the tumor vs. normal groups were analyzed using the Wilcoxon test in the R language.
Prognostic value of target genes in LGG
Based on the TCGA-LGG and CGGA-325 datasets, the tumor samples were classified into high- and low-expression groups according to the median values of LDHA and SLC16A1 gene expression [18, 19]. Kaplan–Meier (K–M) survival analysis was conducted using survival 2.41-1 [20] to analyze the relationship between gene expression and LGG patient survival. The combined effects of LDHA and SLC16A1 on LGG cell survival of LGGs was also investigated.
Receiver operating characteristic (ROC) curve of LDHA and SLC16A1
To analyze the diagnostic value of LDHA and SLC16A1 in LGG, ROC curves were plotted for the TCGA-LGG, GSE15824-LGG, and GSE16011-LGG cohorts using the R package pROC 1.18.0 [21]. The area under the ROC curve (AUC) was calculated to evaluate the ability of the model to distinguish between patients with LGG and controls. The AUC is between 0 and 1, and the greater the AUC value (the closer it is to 1), the better are the diagnostic results.
Gene set enrichment analysis (GSEA) of LDHA and SLC16A1
To explore the correlation between LDHA and SLC16A1 in LGG, Spearman’s correlation analysis was employed based on TCGA and CGGA datasets using R software. With MSigDB v7.1 [22] database c2.cp.kegg.v7.4. symbols.gmt as the enrichment background, the expression profile of TCGA-LGG patients was analyzed using GSEA. The significant Kyoto Encyclopedia of Genes and Genomes pathways enriched by LDHA and SLC16A1 were analyzed, and pathways with P < 0.05 were presented.
Protein expression of LDHA and SLC16A1 based on the HPA database
The HPA database is a collection of protein expression in 48 human normal tissues, 20 tumor tissues, and 64 cell lines through immunoassays [23]. Immunochemistry images of LDHA and SLC16A1 in the normal cerebral cortex and glioma tissues were retrieved from the HPA online database.
Cell culture and processing
Normal human glial cells were purchased from Shanghai Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The LGG cell lines of SW1088 and HS683 cell lines were obtained from the American Type Culture Collection (ATCC, USA). Cells were cultured in Dulbecco’s modified Eagle’s medium (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, Grand Island, NY, USA) and maintained at 37 °C under 5% CO2. SLC16A1 small interference RNA (si - SLC16A1) and the negative control (si - NC) sequence (Gemma, Shanghai) was as follows: si-SLC16A1:5’- GAGGAAGAGACCAGTATAGATGTTGCTGG-3; si-NC: 5’- CCGCCCTTTTTTGGGCCTAAAACCCCTGAATAGTCCG − 3. ’ si-SLC16A1 and si-NC were transfected into LGG cells lines by Lipofectamin3000 and continued cultured for 48 h.
Real time PCR assay
Real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) was used to validate the relative mRNA levels of LDHA and SLC16A1. Total RNA was extracted using the TRIzol reagent (Invitrogen, USA). cDNA was generated using MMLV reverse transcriptase (Promega, USA). RT-qPCR was performed using a LightCycler 480II qRT-PCR instrument (Roche, Switzerland). The sequences of primer pairs: LDHA: F: 5′-CTCCTGTGCAAAATGG-CAAC-3′, R: 5′-CCTAGAGCTCACTAGT-CACAG-3′; SLC16A1: F: 5′-CCATTGTGGAATGCGTCCT-3’, R: 5′-CCTACTTCTTTCCCCCATCC-3′. Actin was used as an internal reference. The relative expression of each gene was quantitatively analyzed using the ΔΔCt method.
Cell proliferation analysis
Glioma cells in the logarithmic growth phase were selected and seeded in 96-well plates at a density of 4 × 103 cells/well. Three wells were used for each group. After cultivation, 10-µL CCK-8 solution was added to each well and continue incubating for 4 h, and the absorbance (A) value at 450 nm was detected on the microplate reader (Thermo Fisher, USA).
Transwell migration and invasion assays
Uniform Matrigel (BD, USA) was added to the upper layer of the basement membrane of the Transwell chamber (Millipore, Billerica, MA, USA), and 100-µL cell suspension was spread to the upper chamber of the Transwell (5 × 104 cells/chamber). A total of 600 µL of medium containing 10% FBS was aspirated and added to the lower Transwell chamber. After 24 h of cell culture, the chambers were removed and fixed in 4% paraformaldehyde for 30 min, followed by crystal violet staining (Beyotime, Hangzhou, China) for 30 min. Migration results were observed under a microscope. For the invasion experiment, the BD Matrigel™ Matrix was frozen thawed and diluted with serum-free medium at a ratio of 1:2 and then coated onto the bottom membrane of the chamber and incubated at room temperature for 1 h. After rinsing with serum-free medium, 70 µL of serum-free medium was added and incubated at 37℃ for 30 min, and the remaining experimental procedures were similar to the migration assay.
Western blot
48 h after transfection, cells were collected and lysed with RIPA cell lysate to extract total cell protein. The protein concentration was determined by BCA method, then 30 µg protein per well was deposited on PVDF membrane by electrophoresis, and enclosed in 5% skim milk at room temperature for 1 h. Mouse anti-human SLC16A1 monoclonal antibody diluted at 1:500 and mouse anti-human GAPDH monoclonal antibody diluted at 1:1000 were added (as internal reference), respectively, and the reaction was overnight at 4 ℃. After TBST rinsing, secondary antibody (1:2000) was added and reacted at 37 ℃ for 1.5 h. And finally visualized using an enhanced chemiluminescence system (BioRad, Hercules, CA, USA).
Statistical analysis
Data analysis was conducted using GraphPad Prism Software and R packages. The list of R packages implemented in this study is shown in Table 3. Data are presented as mean ± standard deviation, and statistical analysis was conducted using a two-tailed Student’s t-test. Differences were considered statistically significant at P < 0.05.
Table 3.
R packages and versions implemented in this study
| R package | Version |
|---|---|
| Survival | 2.41-1 |
| pROC | 1.18.0 |
| ggplot2 | 3.5.1 |
| clusterProfiler | 4.12.6 |
Results
Expression difference of transporters (LDHA/SLC16A1) in LGG and normal tissue
The expression levels of LDHA and SLC16A1 in LGG and control samples were extracted from TCGA and GTEx expression profile datasets, and the expression differences of LDHA and SLC16A1 in the tumor vs. normal groups were analyzed by R Wilcoxon test according to grading. Based on the TCGA dataset, LDHA was significantly downregulated and SLC16A1 was significantly up-regulated in the LGG (grade II and grade III) groups compared to the normal controls (P < 0.001, Fig. 1A and B). The expression levels of LDHA and SLC16A1 in GSE15824 and GSE16011 of all tumor samples (grades I–IV) were also extracted. The expression of LDHA and SLC16A1 in all grade tumor samples (grades I to IV) in the GSE15824 dataset was not significantly different from that in the control group (P > 0.05, Fig. 1C and D), and the expression of LDHA in grade I to grade IV in the GSE16011 dataset was significantly lower than that in the control group (all P < 0.05, Fig. 1E). SLC16A1 was not annotated based on the GPL8542 platform, and its expression profile of SLC16A1 was obtained from the GSE16011 dataset. Furthermore, we analyzed the expression profiles of LDHA and SLC16A1 in LGG samples from the GSE15824 (14 LGG and 5 control) and GSE16011 (117 LGG and 8 control) datasets. LDHA expression in LGG was relatively low in the GSE15824 (P > 0.05, Fig. 1F) and GSE16011 datasets (P < 0.05, Fig. 1G), which was consistent with the results of the LDHA expression trend based on the TCGA dataset. However, there was no significant change in the expression of SLC16A1 in the GSE15824 dataset (P > 0.05, Fig. 1F). We observed diverse LDHA expression in LGG, possibly due to the heterogeneity of the tumor samples and differences in tumor purity.
Fig. 1.
Differential expression of LDHA/SLC16A1 in LGG and normal tissue. Box plot of LDHA (A) and SLC16A1 expression (B) between tumor and normal groups in TCGA-LGG dataset. Box plot of LDHA (C) and SLC16A1 expression (D) according to the grading based on GSE15824 dataset. Box plot of LDHA expression (E) according to the grading based on GSE16011. (F) The box plot of LDHA and SLC16A1 expression in GSE15824 LGG cohort. (G) Box plot of LDHA expression of GSE16011 LGG cohort. * indicated P < 0.05
Prognostic value of LDHA/SLC16A1 in glioma
In this study, we also analyzed the IDH status of the patients, as shown in Fig. 2A. It can be seen that the mutation frequency of IDH accounts for 77%. In addition, we also analyzed the correlation between the expression of LDHA and SLC16A1 and clinical features such as age, gender, primary diagnosis, and race demographic. The results showed that the expression of LDHA and SLC16A1 was significantly correlated with primary diagnosis, but not with other clinical features (Fig. 2B-J).
Fig. 2.
Correlation between gene expression and clinical features. Waterfall diagram of patient IDH status (A). Correlation of LDHA expression with age (B), vital status (D), gender (E), primary diagnosis (G), and race demographic (I). Correlation between the expression of SLC16A1 and age (C), gender (F), primary diagnosis (H), and race demographic (J)
The correlation between LDHA/SLC16A1 expression and prognosis was analyzed using survival analysis in TCGA and CGGA datasets. K–M survival curves demonstrated that high expression of LDHA and SLC16A1 was significantly correlated with poor outcomes of LGG patients in TCGA cohort (n = 518, P < 0.001, Fig. 3A and B). We observed similar results in the CGGA cohort, where high expression of LDHA and SLC16A1 was significantly associated with poor outcomes in patients with LGG (n = 172, P < 0.001, Fig. 3C and D). Furthermore, the prognostic value of the combination of LDHA and SLC16A1 was analyzed. As depicted in Fig. 3E and F, patients with high expression of LDHA and SLC16A correlated with worse prognosis, while those with low LDHA and SLC16A expression displayed the best outcomes compared with the other groups (all P < 0.001).
Fig. 3.
Prognostic value of LDHA/SLC16A1 in LGG. K-M survival curves of LDHA (A) and SLC16A1 (B) in TCGA-LGG dataset. K-M survival curves of LDHA (C) and SLC16A1 (D) in CGGA LGG samples. The combined effect of LDHA and SLC16A1 expression on survival rate in TCGA (E) and CGGA cohort (F)
The diagnostic value of LDHA/SLC16A1 in glioma
To further illustrate the diagnostic value of LDHA/SLC16A1 in LGG, ROC curves were established. As shown in Supplemental Fig. 1A, in the TCGA-LGG, the AUC values of the two genes were > 0.65, indicating that LDHA and SLC16A1 have potential diagnostic value in LGG. Moreover, LGG samples from the GSE15824 and GSE16011 datasets were collected for ROC analysis. The results showed that the AUC values were 0.686 for LDHA and 0.629 for SLC16A1 in the GSE15824 LGG cohort and 0.751 for LDHA in the GSE16011 LGG cohort (Supplemental Fig. 1B and C). This suggests that these two genes have better diagnostic value for LGG.
Correlation and biological function of LDHA and SLC16A1
In the TCGA cohort, LDHA expression was positively correlated with SLC16A1 expression (R = 0.34, P < 0.05). Similarly, LDHA expression positively correlated with SLC16A1 expression in the CGGA dataset (R = 0.33, P < 0.05) (Fig. 4A and B). Furthermore, the GSEA algorithm was used to identify the differential regulatory pathways between the high- and low-expression groups of LDHA and SLC16A1 based on the TCGA dataset. The pathways closely related to the high-expression groups were considered activated pathways. The top five pathways ranked by enrichment score are revealed in Fig. 4C and D, and the top10 pathways ranked by P-value are presented in Fig. 4E and F. For both genes, allograft rejection, autoimmune thyroid disease, asthma, systemic lupus erythematosus, and graft-versus-host disease were activated in the high-expression groups. In the ridge maps, LDHA was closely related to the chemokine and NOD-like receptor signaling pathways. SLC16A1 is involved in JAK-STAT and NOD-like receptor signaling pathways.
Fig. 4.
Correlation between LDHA and SLC16A1 and their potential biological functions. Pearson correlation analysis between LDHA and SLC16A1 in TCGA (A) and CGGA (B). By GSEA analysis, the top5 pathways ranked by enrichmentScore for LDHA (C) and SLC16A1 (D). The top10 pathways ranked by P value for LDHA (E) and SLC16A1 (F)
Protein expression of LDHA and SLC16A1 based on the HPA database
The immunohistochemical images of LDHA and SLC16A1 in normal cerebral cortical and glioma tissues were retrieved from the HPA database. As shown in Fig. 5, the protein level of LDHA was relatively low and SLC16A1 was higher in tumor tissues than in controls. The patient’s organizational characteristics are shown in Table 4.
Fig. 5.
Immunohistochemical images of LDHA and SLC16A1 based on HPA database. Immunohistochemical results of (A) LDHA and (B) SLC16A1 in normal and tumor tissues. For LDHA (normal: CAB015336, Female, age 54, Cerebral cortex (T-X2020), Normal tissue, NOS (M-00100), Patient id: 2523, Intensity: Negative, Quantity: None, Staining: Low, Intensity: Moderate, Quantity:<25%; Tumor: Glioma, CAB015336, Female, age 36, Cerebral cortex (T-X2020), Glioma, malignant, High grade (M-938033), Patient id: 1587, Tumor cells, Staining: Low, Intensity: Moderate, Quantity:<25%, Location: Cytoplasmic/membranous); For SLC16A1 (normal: Cerebral cortex, HPA003324, Male, age 45, Cerebral cortex (T-X2020), Normal tissue, NOS (M-00100), Patient id: 2521, Endothelial cells, Staining: High, Intensity: Strong, Quantity:>75%, Location: Cytoplasmic/membranous; Tumor: Glioma, HPA003324, Male, age 56, Cerebral cortex (T-X2020), Glioma, malignant, High grade (M-938033), Normal tissue, NOS (M-00100), Patient id: 1578, Tumor cells, Staining: High, Intensity: Strong, Quantity: >75%, Location: Cytoplasmic/Membranous)
Table 4.
Organizational characteristics of patients
| LDHA | SLC16A1 | |||
|---|---|---|---|---|
| Patient ID | 2523 | 1587 | 2521 | 1578 |
| Age (years) | 54 | 36 | 45 | 56 |
| Gender | Female | Female | Male | Male |
| Sample type | Normal sample | Glioma sample | Normal sample | Glioma sample |
| Location | Cerebral cortex (T-X2020) | Cerebral cortex (T-X2020) | Cerebral cortex (T-X2020) | Cerebral cortex (T-X2020) |
| Stained area | Cytoplasmic/membranous | Cytoplasmic/membranous | Cytoplasmic/membranous | Cytoplasmic/membranous |
| Staining | Low | Low | High | High |
| Intensity | Moderate | Moderate | Strong | Strong |
| Quantity | < 25% | < 25% | > 75% | > 75% |
Low expression of SLC16A1 inhibits proliferation, migration, and invasion of glioma cells
The expressions of LDHA and SLC16A1 were validated in LGG cell lines. As illustrated in Fig. 6A, LDHA was aberrantly de-expressed and SLC16A1 was significantly upregulated in LGG cells, which was consistent with the results of the bioinformatics analysis. SLC16A1 was selected for subsequent experiments. si-SLC16A1 was transfected into SW1088 and HS683 cells, and the mRNA and protein expression levels of SLC16A1 in SW1088 and HS683 cells were significantly reduced (Fig. 6B and C), indicating successful transfection of si-SLC16A1. Proliferation analysis of CCK-8 demonstrated that the proliferative capacity of SW1088 and HS683 cells was reduced after transfection with si-SLC16A1 (Fig. 7A and B). Moreover, Transwell analysis revealed that low expression of SLC16A1 inhibited the migration and invasion abilities of SW1088 and HS683 cells compared to the negative control group (Fig. 7C and D).
Fig. 6.
Expression of SLC16A1 in SW1088 and HS683 after transfection with si-SLC16A1. A The expressions of LDHA and SLC16A1 in low-grade glioma cell lines. The X-axis represented different cells, while the Y-axis represented the relative expression of LDHA and SLC16A1. Control, normal human glial cells; Low-grade glioma cell lines SW1088 and HS683. **, P < 0.01, when compared with control. B The mRNA expression of SLC16A1 was detected by qRT-PCR. C The protein expression of SLC16A1 was detected by western blot. Protein concentrations were determined by BCA assay, and equal amounts (30 µg) were loaded in each lane.***, P < 0.001
Fig. 7.
Low expression of SLC16A1 inhibits glioma cell proliferation, migration, and invasion. A Proliferation curve of SW1088 cells. B Proliferation curve of HS683 cells. C Transwell assay was used to detect the migration ability of SW1088 and HS683 cells (×100). D Transwell assay was used to detect the invasion ability of SW1088 and HS683 cells (×100). *, P < 0.05, **, P < 0.01, ***, P < 0.001
Discussion
Lactate secreted by tumors, produced from aerobic glycolysis in cancer cells, is associated with an elevated incidence of metastasis, angiogenesis, and metabolic reprogramming in adjacent tissues. LDHA plays a critical role in human cancers owing to its role in promoting glycolysis and converting pyruvate to lactate [24]. Lactate transport across the membrane requires MCT1. Thus, the contribution of LDHA and MCT1 (SLC16A1) to tumor progression deserves attention. In this study, by downloading glioma-related datasets from TCGA, CGGA, and GEO, we analyzed the expression, prognosis, and diagnostic values of LDHA and SLC16A1 in LGG cohorts. The results showed that LDHA was downregulated and SLC16A1 was upregulated in LGG tumor tissues compared to normal tissues in TCGA dataset. The protein expression of LDHA and SLC16A1 based on the HPA database was consistent with the mRNA expression levels. QRT-PCR also showed a similar trend. Furthermore, low SLC16A1 expression inhibited the proliferation, migration, and invasion of SW1088 and HS683 cells. K–M survival curves revealed that high expression of LDHA and SLC16A1 predicted poor prognosis in patients with LGG. ROC curve showed that the two genes exerted potential diagnostic value. GSEA revealed that LDHA is involved in the chemokine and NOD-like receptor signaling pathways, whereas SLC16A1 is involved in the NOD-like receptor and JAK-STAT signaling pathways.
Lactate is the final metabolite of highly proliferative tumors, including GBM, which can be transported to the tumor microenvironment and mediate the survival of tumor cells via MCTs, members of the SLC16 family [25]. MCT1 encoded by SLC16A1 exerts functions in L-lactate transport to maintain energy balance [26]. In tumor cells, SLC16A1 is highly expressed during the transport of L-lactate derived from glycolysis. MCTs and L-lactate accumulate and contribute to tumor development and progression [27]. MCT1 is overexpressed in GBM and has been suggested as a potential therapeutic target. In the present study, SLC16A1 was upregulated in LGG. Although little is known regarding the role of MCT1 in LGG, we infer that SLC16A1 upregulation plays a key role in the LGG development and progression.
Furthermore, suppressing LDHA expression leads to diminished glycolysis, cell growth, and invasion, while increasing apoptosis [28]. As reported in an study in vivo, inhibition of MCT function considerably reduced glioma invasion [25]. Interestingly, in the present study, LDHA encoding lactate dehydrogenase A was downregulated in LGG tumor tissues, which may cause less lactate accumulation, leading to reduced growth and invasion of LGG, despite the MCT1 (SLC16A1) upregulation. Recently, enzymes associated with catabolism have emerged as prognostic biomarkers of tumors. Zhang et al. found that the cyclin-dependent kinase regulatory subunit 2 is highly expressed in various malignancies and is an independent prognostic factor in HCC [29]. Flap endonuclease 1 is overexpressed in HCC and has been implicated in the progression and metastasis of tumor cells [30]. Overall, LDHA and SLC16A1 are potential targets for LGG therapy.
We also explored the prognostic and diagnostic value of LDHA and SLC16A1 in patients with LGG. The high expression of LDHA and SLC16A1 indicated poor prognosis in LGG, which is in accordance with previous studies [31–34]. For instance, Girgis et al. [31] reported that LDHA is a potential prognostic biomarker of clear cell renal cell carcinoma, and the upregulation of LDHA indicates poor prognosis. A recent study by Dong et al. [32] also demonstrated that high expression levels of LDHA are implicated in the poor differentiation of pancreatic adenocarcinoma, leading to poor survival outcomes. Zhang et al. [33] indicated that high expression of SLC16A1 predicts poor prognosis in urological cancers. A recent study revealed that the mRNA level of SLC16A1 was significantly increased in high-grade gliomas compared to LGG and non-tumor controls, suggesting that SLC16A1 expression is positively correlated with WHO pathological grading and poor survival of gliomas [34]. To the best of our knowledge, this is the first study to demonstrate the diagnostic and prognostic value of LDHA and SLC16A1 in LGG.
To explore the potential mechanisms of these two genes, we conducted GSEA. LDHA is involved in chemokine and NOD-like receptor signaling pathways, whereas SLC16A1 is involved in JAK-STAT and NOD-like receptor signaling pathways. The chemokine system controls almost all types of leukocyte trafficking in the immune system. Chemokines and their receptors are upregulated in numerous human cancers, including gliomas [35, 36]. A pan-cancer analysis by Neapolitan et al. [37] reported that the chemokine signaling pathway is notable in LGG. The JAK-STAT signaling pathway is the response of the cell membrane to extracellular growth factors and cytokines that transmit signals from the cell membrane to the nucleus. Activation of this pathway is effective in predicting the clinical prognosis of glioma [38]. Interestingly, both LDHA and SLC16A1 are involved in the NOD-like receptor signaling pathway. NOD-like receptors are essential for sensing pathogens and risk-associated molecular patterns. NLR signaling dysregulation is critical for the pathogenesis of neurodegenerative and autoimmune diseases and cancers [39]. Notably, NOD-like receptors play a key regulatory role in angiogenesis [40], which is associated with the pathogenesis of gliomas [39]. Glioma growth is closely associated with the brain vascular structure. Once the diameter of the primary tumor reaches 1–2 mm, the cerebrovascular system is destroyed. Thus, tumor growth and invasion in gliomas are marked by angiogenesis [41]. Nevertheless, the exact mechanisms underlying angiogenesis and the expression of these two genes require further investigation. Altogether, we speculate that LDHA and SLC16A1 may be implicated in LGG pathogenesis via the signaling pathways described above.
Overall, this study suggests that SLC16A1 has a potential prognostic and diagnostic value for LGG. This study also shows that low expression of SLC16A1 inhibits the biological processes of SW1088 and HS683 cells and can be used as a potential diagnostic and therapeutic target for LGG. The current study used advanced analytical methods and molecular techniques (bioinformatic analysis combined with experimental validation) to identify new targets for the diagnosis and prognosis of LGG, laying the foundation for the development of personalized treatment for patients.
Conclusion
In summary, our study suggests that LDHA is abnormally downregulated, and SLC16A1 is upregulated in LGG. Furthermore, these two genes have potential prognostic and diagnostic values for LGG. Importantly, low SLC16A1 expression inhibited the proliferation, migration, and invasion of SW1088 and HS683 cells. SLC16A1 may serve as a potential diagnostic and therapeutic target for LGG.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
Not applicable.
Author contributions
JY H: Conceptualization, Methodology and Writing– original draft; QS W, ZJ L and XN H: Data curation and Formal analysis; W W and J L: Conceptualization, Methodology and Writing– review and editing.
Funding
This work was supported by [Weifang City health Commission scientific research project] (grant numbers WFWSJK-2023-314.
Data availability
The datasets used or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Weller M, Wen PY, Chang SM, Dirven L, Lim M, Monje M, et al. Glioma. Nat Rev Dis Primers. 2024;10(1):33. 10.1038/s41572-024-00516-y. [DOI] [PubMed] [Google Scholar]
- 2.Schiff D, Van den Bent M, Vogelbaum MA, Wick W, Miller CR, Taphoorn M, et al. Recent developments and future directions in adult lower-grade gliomas: society for Neuro-Oncology (SNO) and European association of Neuro-Oncology (EANO) consensus. Neuro Oncol. 2019;21(7):837–53. 10.1093/neuonc/noz033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.van den Bent MJ. Interobserver variation of the histopathological diagnosis in clinical trials on glioma: a clinician’s perspective. Acta Neuropathol. 2010;120(3):297–304. 10.1007/s00401-010-0725-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Roland G, Maximilian R, Martin K, Carolin Weiss L, Norbert G, Stefan G. The treatment of gliomas in adulthood. Deutsches Arzteblatt Int. 2018;115(0). 10.3238/arztebl.2018.0356.
- 5.Ali E, Ahmed MA, Shawki MA, El Arab LRE, Khalifa MK, Swellam M. Expression of some Circulating MicroRNAs as predictive biomarkers for prognosis and treatment response in glioblastoma. Sci Rep. 2025;15(1):1933. 10.1038/s41598-024-83800-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Mahmoud MS, Khalifa MK, Nageeb AM, El-Arab LE, El-Mahdy M, Ramadan A et al. Clinical impact of IDH1 mutations and MGMT methylation in adult glioblastoma multiforme. J Egypt Med Human Gen. 2024;25(1):42.
- 7.Swellam M, Khalifa MK, Nageeb AM, Ezz El-Arab L, El-Mahdy M, El-Bahy K, et al. Clinical role of NDRG2-based methylation status on survival pattern of glioblastoma. Int J ImmunoPathol Pharmacol. 2024;38:03946320241250294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. [DOI] [PubMed] [Google Scholar]
- 9.Zhu J, Thompson CB. Metabolic regulation of cell growth and proliferation. Nat Rev Mol Cell Biol. 2019;20(7):436–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Wolpaw AJ, Dang CV. Exploiting metabolic vulnerabilities of cancer with precision and accuracy. Trends Cell Biol. 2018;28(3):201–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Elia I, Rowe JH, Johnson S, Joshi S, Notarangelo G, Kurmi K, et al. Tumor cells dictate anti-tumor immune responses by altering pyruvate utilization and succinate signaling in CD8(+) T cells. Cell Metab. 2022;34(8):1137–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Payen VL, Mina E, Van Hée VF, Porporato PE, Sonveaux P. Monocarboxylate transporters in cancer. Mol Metab. 2020;33:48–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.San-Millán I, Brooks GA. Reexamining cancer metabolism: lactate production for carcinogenesis could be the purpose and explanation of the Warburg effect. Carcinogenesis. 2017;38(2):119–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Balakrishnan K. Lactate dehydrogenase isoform expressions differing impacts on gastrointestinal carcinogenesis. Hum Gene. 2023;39:201243.
- 15.Tomczak K, Czerwińska P, Wiznerowicz M. The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Poznan Poland). 2015;19(1a):A68–77. 10.5114/wo.2014.47136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Zhang N, Zhang H, Wu W, Zhou R, Li S, Wang Z, et al. Machine learning-based identification of tumor-infiltrating immune cell-associated LncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma. Theranostics. 2022;12(13):5931–48. 10.7150/thno.74281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Zhao Z, Zhang KN, Wang Q, Li G, Zeng F, Zhang Y, et al. Chinese glioma genome atlas (CGGA): A comprehensive resource with functional genomic data from Chinese glioma patients. Genom Proteom Bioinform. 2021;19(1):1–12. 10.1016/j.gpb.2020.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zhang H, Dai J, Mu Q, Zhao X, Lin Z, Wang K, et al. Macrophage heterogeneity and oncogenic mechanisms in lung adenocarcinoma: insights from scRNA-seq analysis and predictive modeling. Front Immunol. 2024;15:1491872. 10.3389/fimmu.2024.1491872. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Li S, Luo J, Liu J, He D. Pan-cancer single cell and Spatial transcriptomics analysis Deciphers the molecular landscapes of senescence related cancer-associated fibroblasts and reveals its predictive value in neuroblastoma via integrated multi-omics analysis and machine learning. Front Immunol. 2024;15:1506256. 10.3389/fimmu.2024.1506256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Therneau TM, editor. Extending the Cox model. New York, NY: Springer US; 1997. [Google Scholar]
- 21.Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. 10.1186/1471-2105-12-77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics (Oxford, England). 2011;27(12):1739-40. 10.1093/bioinformatics/btr260. [DOI] [PMC free article] [PubMed]
- 23.Thul PJ, Lindskog C. The human protein atlas: a Spatial map of the human proteome. Protein Sci. 2018;27(1):233–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Liu J, Zhang C, Zhang T, Chang CY, Wang J, Bazile L, et al. Metabolic enzyme LDHA activates Rac1 GTPase as a noncanonical mechanism to promote cancer. Nat Metab. 2022;4(12):1830–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Colen CB, Shen Y, Ghoddoussi F, Yu P, Francis TB, Koch BJ, et al. Metabolic targeting of lactate efflux by malignant glioma inhibits invasiveness and induces necrosis: an in vivo study. Neoplasia. 2011;13(7):620–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Halestrap AP. The SLC16 gene family–structure, role and regulation in health and disease. Mol Aspects Med. 2013;34(2–3):337–49. [DOI] [PubMed] [Google Scholar]
- 27.Contreras-Baeza Y, Sandoval PY, Alarcón R, Galaz A, Cortés-Molina F, Alegría K, et al. Monocarboxylate transporter 4 (MCT4) is a high affinity transporter capable of exporting lactate in high-lactate microenvironments. J Biol Chem. 2019;294(52):20135–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Li J, Zhu S, Tong J, Hao H, Yang J, Liu Z, et al. Suppression of lactate dehydrogenase A compromises tumor progression by downregulation of the Warburg effect in glioblastoma. NeuroReport. 2016;27(2):110–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Zhang J, Song Q, Liu J, Lu L, Xu Y, Zheng W. Cyclin-dependent kinase regulatory subunit 2 indicated poor prognosis and facilitated aggressive phenotype of hepatocellular carcinoma. 2019;2019:8964015. 10.1155/2019/8964015. [DOI] [PMC free article] [PubMed]
- 30.Bian S, Ni W, Zhu M, Zhang X, Qiang Y, Zhang J, et al. Flap endonuclease 1 facilitated hepatocellular carcinoma progression by enhancing USP7/MDM2-mediated P53 inactivation. Int J Biol Sci. 2022;18(3):1022–38. 10.7150/ijbs.68179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Girgis H, Masui O, White NM, Scorilas A, Rotondo F, Seivwright A, et al. Lactate dehydrogenase A is a potential prognostic marker in clear cell renal cell carcinoma. Mol Cancer. 2014;13:101. 10.1186/1476-4598-13-101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Dong M, Tang R, Wang W, Xu J, Liu J, Liang C, et al. Integrated analysis revealed hypoxia signatures and LDHA related to tumor cell dedifferentiation and unfavorable prognosis in pancreatic adenocarcinoma: hypoxia in PDAC. Transl Oncol. 2023;33:101692. 10.1016/j.tranon.2023.101692. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Zhang L, Song ZS, Wang ZS, Guo YL, Xu CG, Shen H. High expression of SLC16A1 as a biomarker to predict poor prognosis of urological cancers. Front Oncol. 2021;11:706883. 10.3389/fonc.2021.706883. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Lin HH, Tsai WC, Tsai CK, Chen SH, Huang LC, Hueng DY, et al. Overexpression of Cell-Surface marker SLC16A1 shortened survival in human High-Grade gliomas. J Mol Neurosci. 2021;71(8):1614–21. 10.1007/s12031-021-01806-w. [DOI] [PubMed] [Google Scholar]
- 35.Yang SX, Chen JH, Jiang XF, Wang QL, Chen ZQ, Zhao W, et al. Activation of chemokine receptor CXCR4 in malignant glioma cells promotes the production of vascular endothelial growth factor. Biochem Biophys Res Commun. 2005;335(2):523–8. 10.1016/j.bbrc.2005.07.113. [DOI] [PubMed] [Google Scholar]
- 36.Zhou Y, Larsen PH, Hao C, Yong VW. CXCR4 is a major chemokine receptor on glioma cells and mediates their survival. J Biol Chem. 2002;277(51):49481–7. 10.1074/jbc.M206222200. [DOI] [PubMed] [Google Scholar]
- 37.Neapolitan R, Horvath CM, Jiang X. Pan-cancer analysis of TCGA data reveals notable signaling pathways. BMC Cancer. 2015;15:516. 10.1186/s12885-015-1484-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Tu Y, Zhong Y, Fu J, Cao Y, Fu G, Tian X et al. Activation of JAK/STAT signal pathway predicts poor prognosis of patients with gliomas. Med Oncol (Northwood, London, England). 2011;28(1):15–23. 10.1007/s12032-010-9435-1. [DOI] [PubMed]
- 39.Saxena S, Jha S. Role of NOD- like receptors in glioma angiogenesis: insights into future therapeutic interventions. Cytokine Growth Factor Rev. 2017;34:15–26. [DOI] [PubMed] [Google Scholar]
- 40.Liu P, Lu Z, Liu L, Li R, Liang Z, Shen M, et al. NOD-like receptor signaling in inflammation-associated cancers: from functions to targeted therapies. Phytomedicine: Int J Phytotherapy Phytopharmacology. 2019;64:152925. 10.1016/j.phymed.2019.152925. [DOI] [PubMed] [Google Scholar]
- 41.Plate KH, Mennel HD. Vascular morphology and angiogenesis in glial tumors. Exp Toxicol Pathol. 1995;47(2–3):89–94. [DOI] [PubMed] [Google Scholar]
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Supplementary Materials
Data Availability Statement
The datasets used or analysed during the current study are available from the corresponding author on reasonable request.