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. 2025 Apr 28;42:102034. doi: 10.1016/j.bbrep.2025.102034

SLC16A3 (MCT4) expression in tumor immunity and Metabolism: Insights from pan-cancer analysis

Wenxing Du a,1, Bo Zang b,1, Yang Wo a, Shiwei Chen c,⁎
PMCID: PMC12415977  PMID: 40927315

Abstract

Background

SLC16A3, a highly expressed H + -coupled symporter, facilitates lactate transport via monocarboxylate transporters (MCTs), contributing to acidosis. Although SLC16A3 has been implicated in tumor development, its role in tumor immunity remains unclear.

Methods

A pan-cancer analysis was conducted using datasets from The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, and Genotype-Tissue Expression projects. SLC16A3 expression patterns and associations with tumor progression, prognosis, immune checkpoints, and immune neoantigens were evaluated across 30 cancer types. Immune infiltration scores were analyzed using the Tumor Immune Estimation Resource dataset.

Results

SLC16A3 expression is differentially regulated in cancer versus healthy tissues, with elevated levels associated with poor prognosis and reduced overall survival in glioblastoma multiforme (HR = 1.88), low-grade gliomas (HR = 1.51), and lung adenocarcinoma (HR = 1.33). Notably, significant associations between SLC16A3 expression and poor outcomes were observed in 33 cancers, except for rectum adenocarcinoma, testicular germ cell tumors, pheochromocytoma and paraganglioma, and adrenocortical carcinoma. SLC16A3 expression was also strongly linked to immune checkpoints and neoantigens. Correlations with tumor-infiltrating immune cells were pronounced in prostate adenocarcinoma but absent in uterine carcinosarcoma and cervical squamous cell carcinoma. Gene set enrichment analysis (GSEA) revealed a pivotal role of SLC16A3 in tumor growth, metabolism, and immunity.

Conclusion

SLC16A3, the transporter facilitating the efflux of lactic acid, shows differential expression across various cancer types and exerts a critical effect on tumor development and immunity. Thus, SLC16A3 has promising potential as a prognostic marker, and its targeted manipulation can offer therapeutic advantages.

Keywords: SLC16A3 (MCT4), Lactic acid, Glycolysis, Pan-cancer analysis, Immunity

Highlights

  • •

    SLC16A3 shows differential expression in cancer and is linked to poor prognosis.

  • •

    High SLC16A3 expression correlates with immune checkpoints and immune cells.

  • •

    SLC16A3 plays a key role in tumor growth, metabolism, and immunity via GSEA.

1. Introduction

Cancer, a prevalent illness that impacts millions of individuals, is a significant global contributor to mortality [1]. Although many treatments for cancers have been developed recently, the prognostic outcome for advanced cancers remains dismal [2,3]. An excellent advancement in the field of cancer treatment is the emergence of immunotherapy. Currently, the biomarkers used include Programmed death ligand 1 (PD-L1) expression in cancer cells and the burden of tumor mutational load (TML) [4]. Nevertheless, clinicians continue to face challenges in determining patients suitable for undergoing immunotherapy. Consequently, it is crucial to develop new prognostic biomarkers and therapeutic targets, particularly those associated with immunotherapy.

In the past few years, lactate has become more prominent in research because of its involvement in regulating various tumor processes. Lactate is not only a source of high-energy metabolism, but it also plays a role as a complex regulator in tumor cells [5]. Following the occurrence of H+ efflux as an additional outcome, tumor cells release lactate into the surrounding environment, which aids in developing acidic barriers that hinder cell proliferation and partially trigger apoptosis of cytotoxic T lymphocytes [[6], [7], [8]]. Moreover, the differentiation of dendritic cells (DCs) and the production of inflammatory factors such as IL-6 and TNF were suppressed by lactate through GPR81 activation [9,10]. A recent study reported that the promotion of M2 polarization in tumor-associated macrophages (TAMs) was observed when lactate modified histone through histone lactylation; thus, lactate probably has a critical effect on modulating immunity [11]. By upregulating PD-1 while facilitating T cell cytokine secretion, TAMs induce M2 polarization to attenuate the efficacy of immune response and facilitate immunosuppression [12,13]. Therefore, lactate exerts an essential effect on linking fundamental metabolic processes, tumor microenvironment, and immune responses [14,15].

Lactate transportation, which is achieved through the action of monocarboxylate transporters (MCTs), contributes to the mechanisms that enhance acidosis [16]. At the inflammatory site or in metastatic lesions affected by the Warburg effect or hypoxia, SLC16A3 is a highly expressed H+-coupled symporter [17]. In these conditions, lactate outside the cells can facilitate immune escape and enhance the malignancy grade of tumors. Following SLC16A3 deficiency, lactate accumulates inside cells and induce apoptosis triggered by reactive oxygen species (ROS) [18]. Interestingly, SLC16A3 upregulation is strongly associated with abnormal cell proliferation, distant metastasis, and invasion; thus, suggesting early relapse and dismal prognostic outcomes of many cancers such as hepatocellular carcinoma and colorectal, gastric, prostate, and bladder cancers [[19], [20], [21], [22], [23]]. The selective inhibition of SLC16A3 is increasingly suggested to have positive clinical effects on therapeutic outcomes.

In the present study, SLC16A3 expression and its possible significance as a prognostic indicator were analyzed using datasets from The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) and data from the Genotype-Tissue Expression project (GTEx). Currently, immune checkpoint inhibitors (ICIs) are becoming the key tools to combat cancer and can eliminate the immune suppressive effects of both innate and adaptive immune cells. Testing biomarkers, such as microsatellite instability (MSI) and tumor mutational burden (TMB), is also critical to predict whether an ICI is effective in certain cases. Hence, we analyzed the correlations of SLC16A3 expression with immune checkpoints, tumor-infiltrating immune cells, TMB, MSI, single-stranded DNA (DNAss), and single-stranded RNA (RNAss). We also identified signaling pathways linked to SLC16A3 by using gene set enrichment analysis (GSEA). Taken together, our pan-cancer analyses revealed the therapeutic and prognostic significance of SLC16A3. By using the two datasets, SLC16A3 expression data and clinicopathological information were obtained. TIMER was also used to analyze tumor immune infiltration scores.

2. Methods

2.1. Sample source

The data on SLC16A3 expression of cancer cells were obtained from the CCLE database (https://portals.broadinstitute.org/ccle) [24]. We also collected data from the GTEx project (https://commonfund.nih.gov/GTEx/) and the TCGA database (https://portal.gdc.cancer.gov) for analyzing RNA-sequencing data in normal and tumor tissues. The data from the TIMER dataset were analyzed to obtain cancer immune infiltration scores [25]. The study followed the guidelines of the Declaration of Helsinki (revision in 2013). The study analyzed TCGA primary tumors with complete clinical, RNA-seq, and pathological data, using adjacent non-tumor tissues or TCGA normal samples as controls. Genes with zero TPM expression were filtered out, while metastatic/recurrent tumors, low-quality samples, and technical/biases-driven expression outliers were excluded.

2.2. SLC16A3 expression profiles

SLC16A3 expression levels in healthy tissues and cancer cells were analyzed. The Kruskal-Wallis test was performed to analyze the differential expression of SLC16A3 in cancer versus healthy tissues based solely on the TCGA data. The comparison of SLC16A3 expression in normal tissues between the GTEx data and the TCGA data on tumors was refined.

2.3. SLC16A3 expression in pan-cancer and its relationship with prognosis

The pan-cancer prognostic role of SLC16A3 was verified by survival analysis and univariate Cox proportional hazards regression analysis. The prognostic indicators included overall survival (OS) rate, disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI). We also calculated survival curves and plotted forest plots.

2.4. Correlation of SLC16A3 expression level with immunity

The analysis of immune checkpoints, immune neoantigens, and tumor microenvironment (TME) was conducted. First, we utilized Spearman's rank correlation coefficients to analyze the correlations of SLC16A3 expression with immune checkpoint proteins such as chemokine receptors, chemokines, immune activation proteins, and immunosuppressive proteins. Similarly, we examined the relationships of SLC16A3 expression with immune neoantigens within tumor-infiltrating immune cells, such as B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells. Immune/stromal/ESTIMATE scores were calculated with the Estimation of Stromal and Immune Cells in Malignant Tumors Using Expression Data (ESTIMATE) algorithm.

2.5. Relationships of SLC16A3 expression with TMB, TMSI, DNAss, and RNAss

By using Pearson's correlation coefficient, bubble charts were constructed to investigate the relationship of SLC16A3 expression with TMB, MSI, DNAss, and RNAss.

2.6. GSEA

By using GSEA, we identified signaling pathways enriched in the SLC16A3 high or low expression group. Genes from the Hallmark set and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the analysis. The GSEA analysis was performed to determine the relationships of SLC16A3 with the relevant pathways by using the Hallmark gene set. A normalized enrichment score (NES) of >1.5, a P value of <0.05, and an acceptable false discovery rate (FDR) of <0.25 were considered noteworthy indicators. Permutation tests used 1000 iterations by default, increased to 5000 for small subgroups (n < 15) to reduce false positives.

2.7. Immunohistochemistry-based protein analysis

Protein expression profiles obtained from the Human Protein Atlas (HPA) through IHC staining were classified into the following four categories according to stained cell percentage (>75 %, 25–75 %, and <25 %) and staining intensity (strong, moderate, weak, and negative): high, medium, low, and undetected. SLC16A3 expression in tumor samples was compared with that in healthy tissue samples that served as a control.

2.8. Statistical analysis

The data were managed, and plots were generated using R software (version 4.3.3 https://www.R-project.org; The R Foundation for Statistical Computing, Vienna, Austria). Analyses utilized the following R packages: clusterProfiler (v4.10.0), ggplot2 (v3.4.2), Cytoscape (v3.10.1), limma (v3.56.0), dplyr (v1.1.2), and survival (v3.5.5), among others. The P-value of <0.05 indicated a significant difference.

3. Results

3.1. Differential SLC16A3 expression

We downloaded the data from the TCGA and CCLE databases as well as from the GTEx project to analyze relative SLC16A3 expression in both normal and tumor samples. The GTEx dataset demonstrated the widespread expression of SLC16A3 across 31 healthy tissue samples. The spleen and blood showed the highest expression, while the pancreas and liver exhibited comparatively lower expression levels (Fig. 1A). The CCLE dataset revealed that SLC16A3 exhibits frequent expression across 21 normal tissues; the kidney and pancreas showed the highest expression, while the hematopoietic and lymphoid tissues exhibited comparatively lower expression levels (Fig. 1B). A comprehensive analysis of the expression data retrieved from the TCGA database revealed that SLC16A3 exhibits significantly elevated expression levels across 30 distinct tumor tissues (Fig. 1C). The analysis of the SLC16A3 expression data in cancer and matched healthy tissues in both GTEx dataset and TCGA database revealed increased SLC16A3 expression levels in various malignancies, including breast invasive carcinoma (BRCA), cervical squamous cell carcinoma, kidney chromophobe (KICH), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), pan-kidney cohort (KIPAN), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), and thyroid carcinoma (THCA) (Fig. 1D). However, SLC16A3 expression was not upregulated in rectum adenocarcinoma (READ), testicular germ cell tumors (TGCT), pheochromocytoma and paraganglioma (PCPG), or adrenocortical carcinoma (ACC).

Fig. 1.

Fig. 1

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SLC16A3 expression profile. (A) SLC16A3 levels in 31 normal tissues based on the GTEx database. (B) SLC16A3 levels in tumor cells of 21 tumors based on the CCLE database. (C) SLC16A3 expression levels in 26 tumor tissues and matched normal tissues based on the TCGA database. (D) SLC16A3 levels in 34 tumor tissues based on the TCGA database and matched normal samples in the GTEx database. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001; ∗∗∗∗P < 0.0001.

3.1.1. Correlation of SLC16A3 expression and prognosis

TCGA included 33 cancer types (n > 11,000); median age 55–70 years across most malignancies, with racial distribution predominantly White (80 %), Asian (9 %), and Black (7 %). A univariate Cox proportional hazards regression analysis was performed to assess the prognostic value of SLC16A3 for diverse cancer types. SLC16A3 expression showed a marked relationship with the overall survival (OS) for glioblastoma multiforme (GBMLGG), low-grade gliomas (LGG), LIHC, acute myeloid leukemia (LAML), LUAD, KIPAN, SESC, MESO, PAAD, GBM, BLCA (all P < 0.01), LUSC (P = 0.03), and HNSC (P = 0.04) (Fig. 2A). Additionally, the results of DSS revealed that SLC16A3 expression was significantly correlated with DSS in patients with GBMLGG, LGG, PAAD, LUSC, LIHC, KIPAN, CESC, MESO, LUAD, GBM (all P < 0.01), and BRCA (P = 0.04) (Fig. 2B). Moreover, SLC16A3 expression exhibited a significant relationship with patient's PFI of CESC (P < 0.01), PAAD (P < 0.01), CHOL (P = 0.01), and LUAD (P = 0.04) (Fig. 2C). We also observed that SLC16A3 expression was markedly associated with disease-free interval (DFI) among GBMLGG, LGG, CESC, LUSC, KIPAN, PAAD, PRAD, MESO, LUAD (all P < 0.01) and GBM (P < 0.05) patients (Fig. 2D).

Fig. 2.

Fig. 2

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Relationships of SLC16A3 expression with OS, DSS, DFI, and PFI. (A–D) Correlations between SLC16A3 expression and OS, DSS, PFI, and DFI. Results are shown as forest plots. SLC16A3; OS, overall survival; DSS, disease-specific survival; DFI, disease-free interval; PFI, progression-free interval.

3.2. Correlation between SLC16A3 expression and immunity

The effect of SLC16A3 on tumor immunity was investigated by analyzing the data on immune checkpoints, neoantigens, tumor-infiltrating immune cells, and immune/stromal/ESTIMATE scores. We initially conducted the correlation analysis of SLC16A3 expression with immune checkpoints, which included 24 immune inhibitors and 36 immune stimulators. Based on the data obtained for immune inhibitors from 40 common tumors, SLC16A3 expression showed a positive relationship with vascular endothelial growth factor A (VEGFA) in 35 tumors, with CD276 in 36 tumors, with transforming growth factor β1 (TGFB1) in 39 tumors, and with PD-L1 in 27 tumors. Additionally, in the dataset of immune stimulators, SLC16A3 expression was positively associated with ICAM1 in 38 tumors, with SLAMF7 in 27 tumors, with C10orf54 in 36 tumors and with programmed death receptor 1 (PDCD1) in 24 tumors. Conversely, SLC16A3 expression was negatively correlated with intercellular cell adhesion molecule 1 (ICAM1) in 39 tumors. Additionally, SLC16A3 expression showed a positive relationship with 11 of the 24 immune inhibitors and with 23 of the 36 immune stimulators in uveal melanoma (UVM), with 17 of the 24 inhibitors and 31 of the 36 stimulators in BLCA, with 19 of the 24 inhibitors and 31 of the 36 stimulators in colorectal adenocarcinoma (COADREAD), with 20 of the 24 inhibitors and 31 of the 36 stimulators in THCA, with 23 of the 24 inhibitors and 35 of the 36 stimulators in OV, and with 22 of the 24 inhibitors and 34 of the 36 stimulators in PRAD (Fig. 3A). According to the results of neoantigen analysis, SLC16A3 expression showed a positive relationship with neoantigen numbers in BRCA, UCEC, and STAD (Fig. 3B). We also analyzed the correlation of SLC16A3 expression with six distinct immune cell populations in TME, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells [26]. The results of the immune analysis suggested that SLC16A3 expression was positively associated with the stromal score in GBMLGG, LGG, and PRAD as shown in Fig. 3C. Additionally, the analysis of stromal data indicated that SLC16A3 expression was positively related to the stromal score in GBMLGG, LGG, and KIPAN (Fig. 3D). The ESTIMATE analysis data showed that SLC16A3 expression was markedly correlated with the stromal score of LGG, LUAD, and KIPAN (Fig. 3E). The results revealed that SLC16A3 expression exhibited a significant positive relationship with B cells, CD4+ T cells, neutrophils, macrophages, and dendritic cells in LGG. SLC16A3 expression showed a positive relationship with neutrophils in PRAD and CD8+ T cells in colon adenocarcinoma (COAD) and DCs in GBM, but showed a negative relationship with CD8+ T cells in thymoma (THYM) and B cells and CD4+ T cells in stomach adenocarcinoma (STES) (Fig. 4).

Fig. 3.

Fig. 3

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Correlations of SLC16A3 expression with immune checkpoints and neoantigens. (A) Pan-cancer associations of SLC16A3 expression with 24 immune inhibitors and 36 immune stimulators. (B) Associations of SLC16A3 expression with the number of neoantigens. ∗P < 0.05. SLC16A3. (C) Representative results for the relationships of SLC16A3 expression with immune scores. (D) Representative results for the relationships of SLC16A3 expression with stromal scores. (E) Representative results for the relationships of SLC16A3 expression with ESTIMATE scores. ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001; ∗∗∗∗P < 0.0001. SLC16A3, centromere protein U; TME, tumor microenvironment; DC, dendritic cells; ESTIMATE, the Estimation of Stromal and Immune Cells in Malignant Tumors Using Expression data.

Fig. 4.

Fig. 4

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Association of SLC16A3 expression with tumor-infiltrating immune cells and the TME. Pan-cancer associations of SLC16A3 expression with B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and DCs.

3.3. Relationship of SLC16A3 expression with TMB, MSI, DNAss, and RNAss

Cancer is a genetic disorder that originates from the accumulation of point mutations and structural alterations in the genome. The TMB and MSI can serve as comprehensive indicators for assessing the extent of genomic instability [27]. The present study showed that SLC16A3 expression was positively associated with the TMB of THYM and UCS (Fig. 5A). SLC16A3 expression was negatively associated with the MSI of GBMLGG, KIPAN, and ACC, but was positively associated with COADREAD, LAML, UVM, and COAD (Fig. 5B). SLC16A3 expression was negatively associated with the DNAss of TGCT, UCEC, and PCPG and positively associated with MESO, KIRP, SARC, PRAD, UVM, THYM, THCA, CHOL, LGG, GBMLGG, and OV (Fig. 5C). These analyses revealed that SLC16A3 expression was negatively associated with RNAss in several cancers, including GBMLGG, LGG, KIPAN, GBM, PRAD, THYM, PCPG, AML, KICH, THCA, KIRC, KIRP, DLBC, LUSC, CHOL, OV, and COAD (Fig. 5D).

Fig. 5.

Fig. 5

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Association of SLC16A3 expression with the TMB, MSI, DNAss, and RNAss. (A) Association of SLC16A3 expression with the TMB. (B) Association of SLC16A3 expression with the MSI. Results are shown as bubble charts. SLC16A3, centromere protein U; TMB, tumor mutational burden; MSI, microsatellite instability. (C) Correlation between SLC16A3 expression and DNAss. (D) Correlation between SLC16A3 expression and RNAss.

3.4. GSEA

The functional network of SLC16A3 was elucidated through the application of the protein-protein interaction (PPI) network analysis to comprehensively understand the underlying mechanisms. The results showed a significant association between SLC16A3 and proteins involved in glycolysis, namely SLC2A3, SLC2A1, SLC16A7, ALC17A5, and LDHA (Fig. 6A). GSEA was conducted to investigate the association between SLC16A3 and the signaling pathways by using Hallmark gene sets. SLC16A3, a pivotal molecule involved in the pentose phosphate pathway and galactose metabolism, was identified (Fig. 6B). The waterfall plot displays the top 15 genes with recurrent mutations in SLC16A3-altered cohorts (Fig. 6C). The biological processes of SLC16A13 primarily included lactate transport across the plasma membrane, transmembrane lactate transport, glucose import through the plasma membrane, l-ascorbic acid metabolic process, and dendrite self-avoidance (Table 1).

Fig. 6.

Fig. 6

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Gene set enrichment analysis (GSEA). (A) Proteins involved in the SLC16A3 functional network. (B) KEGG results for SLC16A3. KEGG, Kyoto Encyclopedia of Genes and Genomes. (C) Findings from the analysis of the waterfall plot for SCL16A3. (D) Involvement of SLC16A13 in biological processes (Table 1).

Table 1.

Biological process of SLC16A13.

Go-term description Count in network Strength P-value
GO:0035879 Plasma membrane lactate transport 2 of 3 3.08 0.0063
GO:0035873 Lactate transmembrane transport 3 of 7 2.89 0.00015
GO:0098708 Glucose import across plasma membrane 2 of 5 2.86 0.0079
GO:0019852 l-ascorbic acid metabolic process 2 of 9 2.6 0.0132
GO:0070593 Dendrite self-avoidance 2 of 17 2.32 0.0302
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3.5. Immunohistochemical analysis

We further investigated the protein expression of SLC16A3 by analyzing immunohistochemistry (IHC) results obtained in HPA across eight tumor types, where mRNA expression was associated with poor prognosis. The IHC results revealed that SLC16A3 protein was highly expressed in LUAD (Fig. 7A), LIHC (Fig. 7B), CESC (Fig. 7C), GBMLGG (Fig. 7D), COAD (Fig. 7E), PRAD (Fig. 7F), BRCA (Fig. 7G) and KIRC (Fig. 7H) tumor tissues compared to normal tissues. Elevated SLC16A3 protein expression correlated with poorer survival across malignancies. High expressors showed reduced overall survival in LUAD/LIHC (P < 0.01) and shorter disease-free intervals in GBMLGG/PRAD (P < 0.01), establishing its dual prognostic and therapeutic relevance.

Fig. 7.

Fig. 7

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SLC16A3 protein expression in 8 tumor tissues was measured by IHC (magnification, × 100). The IHC results revealed that SLC16A3 protein was highly expressed in LUAD (A), LIHC (B), CESC (C), GBMLGG (D), COAD (E), PRAD (F), BRCA (G) and KIRC (H) tumor tissues compared to normal tissues. IHC, Immunohistochemistry; HPA, the Human Protein Atlas; LUAD, Lung adenocarcinoma; LIHC, Liver hepatocellular carcinoma; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; GBMLGG, Glioblastoma multiforme and lower grade glioma; COAD, Colon adenocarcinoma; PRAD, Prostate adenocarcinoma; BRCA, Breast invasive carcinoma; KIRC, Kidney renal clear cell carcinoma.

4. Discussion

Significant advancements in immunotherapy have been achieved for treating cancer [28]. The heterogeneous nature of tumor patients [29], however, contributes to an overall unfavorable prognosis for most patients, thereby necessitating the prompt development of precise targeted therapy or multimodal treatment strategies. In tumor progression, cancer cells undergo metabolic reprogramming to adopt a “glycolysis-predominant” phenotype, although mitochondrial functions remain complete. This phenomenon is commonly termed the “Warburg effect” [30,31]. It facilitates tumor cell survival, proliferation, and metastasis and has a pivotal effect on the development of immunosuppressive TMEs, thereby aiding immune evasion and conferring resistance to the diverse forms of cancer treatment [[32], [33], [34]]. This study highlights the novel role of SLC16A3 across 30 cancer types [[35], [36], [37]], revealing its significant association with OS and DSS in 12 cancers, with high expression generally predicting poor prognosis. Notably, we identified cancer-type-specific expression patterns, as SLC16A3 was not significantly upregulated particularly in malignancies such as READ and TGCT, offering new insights for precision-targeted therapies. Innovatively, we expanded on previous findings by demonstrating the extensive links between SLC16A3 and immune regulatory factors, such as PD-L1, VEGFA, TGFB1, ICAM1, and PDCD1, shedding light on mechanisms of immune therapy resistance [[36], [37], [38]]. Additionally, we integrated proteomic validation and GSEA, identifying SLC16A3's role in immune response regulation through the pentose phosphate and galactose metabolism pathways.

The role of glycolysis in the malignant phenotype of tumors and metabolic reprogramming has been extensively investigated [39]. The highly glycolytic state in tumors usually remarkably accelerates cancer cell metabolism and increases glucose and amino acid utilization, resulting in the generation of immunosuppressive products such as lactic acid [40]. These metabolic alterations can restrict energy supply in cytotoxic T cells, recruit immunosuppressive cells (such as Tregs and MDSCs), and polarize M1 macrophages to M2 phenotype within the TME, finally resulting in tumor progression and immunotherapy resistance [41]. The metabolic reprogramming of cells toward glycolysis is typically associated with the upregulated expression of genes associated with the glycolytic pathway as well as their relevant proteins (including PKM2, HK, LDH, and PFKF) and the downstream metabolites [42]. Glycolysis regulation entails intricate interactions among diverse pathways, including the LKB1-AMPK and PI3K-AKT pathways [43,44]. The identification of these pathways and glycolysis-related genes revealed them as potential targets for augmenting the efficacy of chemotherapy, radiotherapy, and immunotherapy in tumor treatment; thus, showing promising outcomes [[45], [46], [47], [48], [49], [50], [51]].

SLC16A3 facilitates lactate efflux, whereas its influx can be primarily regulated through SLC16A1; thus, making it the ultimate product of glycolysis [52]. SLC16A3 sustains oncogenic metabolism by regulating lactate-pyruvate homeostasis: Lactate efflux maintains high lactate-to-pyruvate ratio (L/P ratio) ratios to suppress pyruvate dehydrogenase (PDH) and enforce glycolysis [53,54]; Intracellular lactate accumulation activates HIF-1α feedforward loops and epigenetic remodeling via histone lactylation (H3K18la) [55]-mediated M2 polarization (Arg1 and IL-10 etc.) [56]. Genetic and pharmacologic SLC16A3 blockade induces mitochondrial ROS through pH dysregulation and suppresses metastasis in preclinical models, confirming its dual metabolic-epigenetic oncogenic axis [57,58].

Lactate treatment can enhance CD8+ T cell stemness for synergistically augmenting immunotherapy [59]; however, lactate is commonly considered an immunosuppressive metabolite in the TME. This is because it enhances the differentiation of regulatory T cells (Tregs), induces naïve T cell apoptosis, polarizes TAMs toward the M2 phenotype, and inhibits the production of cytotoxic cytokines by natural killer (NK) and natural killer T (NKT) cells [60]. SLC16A3 expression increases in diverse cancers, including melanoma, colorectal cancer [61], and non-small cell lung cancer (NSCLC) [62]. Moreover, previous studies have confirmed an association between SLC16A3 overexpression and lymph node metastasis as well as between SLC16A3 overexpression and distant metastasis in melanoma. Additionally, according to Reuss et al. [63], SLC16A3 upregulation promotes the angiogenesis, migration, and invasion of gliomas. Conforming to these findings, the present revealed a significant overexpression of SLC16A3 in NSCLC tumor tissues, which exhibited a positive correlation with immune cell infiltration. These observations suggest that the presence of SLC16A3 in cancer cells probably contribute to tumorigenesis as a tumor promoter. Li et al. [64] confirmed that SLC16A3, the target gene of ALKBH5, can regulate lactic acid levels, thereby exerting an influence on the accumulation of Tregs and MDSCs in the TMEs in anti-PD-1 therapies. Fang et al. [65] showed that the simultaneous inhibition of SLC16A3 could potentiate the anti-PD-1 immunotherapeutic efficacy for hepatocellular carcinoma. As observed by Renner et al. [66], a new SLC16A3-targeting inhibitor showed a promising effect on improving ICI efficacy. Our study provided further evidence for the inverse correlation between the intrinsic expression levels of SLC16A3 in tumor cells and anti-PD-1 therapeutic efficacy. This study also demonstrated that the presence of SLC16A3 in tumors is crucial for regulating glycolysis in cancer cells, which exerts a significant impact on immunotherapy efficacy. In summary, our findings strongly support the potential benefits associated with targeting tumor cell-intrinsic SLC16A3 to enhance the outcomes of immunotherapy.

Our study revealed increased levels of SLC16A3 expression in various malignancies, including GBM, LGG, LUSC, KIPAN, and LUAD. This was accomplished by analyzing the SLC16A3 expression data in matched cancer and healthy tissues in the TCGA database and GTEx dataset. These findings agree with the results of Zhu et al. [67]and Tao et al. [68], thus indicating that SLC16A3 might be involved in a wider spectrum of cancers. Additionally, based on prior results and our present results for OS, DSS, DFI, and PFI, we found that SLC16A3 expression was associated with poor prognostic outcomes of patients with LGG, PAAD, GBM, LUSC, and KIPAN showing SLC16A3 overexpression.

The past decade has witnessed extensive research on the identification of numerous immune checkpoints, which revealed the remarkable effects of anti-PD-1/PD-L1 drugs for therapeutic interventions [69]. Nonetheless, because of heterogeneities among tumor patients, only a few patients can derive benefits from this therapeutic approach [70]. SLC16A3 expression correlated with immune checkpoints and neoantigen burden, suggesting concurrent immunosuppression and antigenicity. Co-elevation of PD-L1/SLC16A3 may drive immune evasion via dual mechanisms: lactate-mediated T cell dysfunction synergizing with PD-L1/PD-1 suppression, and acidic microenvironment attenuation of neoantigen immunogenicity. High SLC16A3 and neoantigen tumors could benefit from combined lactate metabolism blockade [35,71] and PD-1 inhibition [72], paralleling IDO and CTLA-4 synergy [73]. SLC16A3-VEGFA co-expression further supports anti-angiogenic combination to enhance vascular normalization.

Immune cells in both innate (such as monocytes, neutrophils, macrophages, mast cells, and dendritic cells) and adaptive (B and T cells) immune systems are critical for TME infiltration and modulation of tumor progression. To enhance our understanding of the involvement of SLC16A3 in immune responses, a correlation analysis was conducted to analyze the relationship of SLC16A3 expression with tumor-infiltrating immune cells and the ESTIMATE score. In our study, the immune analysis data revealed that SLC16A3 expression was positively associated with the stromal scores of GBMLGG, LGG, and PRAD. Furthermore, the analysis of the stromal data indicated that SLC16A3 expression was positively associated with the stromal scores of GBMLGG, LGG, and KIPAN. The ESTIMATE analysis data showed that SLC16A3 expression was significantly and positively associated with the stromal scores of LGG, LUAD, and KIPAN. The results demonstrated that SLC16A3 expression exhibited a significant positive association with various immune cell subtypes such as B cells, CD4+ T cells, macrophages, neutrophils, and DCs in LGG. SLC16A3 expression also exhibited positive correlations with neutrophils in PRAD, CD8+ T cells in COAD, and DCs in GBM, while showing negative associations with CD8+ T cells in THYM and B cells and CD4+ T cells in STES. SLC16A3 expression was observed to positively correlate with stromal score in various tumor types; thus, indicating its potential impact on the infiltration of immune cells and stromal cells, including epithelial cells, fibroblasts, and vascular cells. This finding suggests that SLC16A3 may significantly affect tumor purity [26]. Consequently, targeting SLC16A3 could represent a promising therapeutic approach for regulating immunity.

To optimize the immunotherapeutic efficacy, it is important to predict the response to checkpoint inhibitors (CPIs) [74]. TMB quantifies the mutation frequency in cancer cells and is a robust factor that predicts CPI response and a significant biomarker that identifies suitable immunotherapy candidates across diverse cancer types [74,75]. The status of deficient mismatch repair (dMMR)/MSI has been widely investigated and shows an important effect on immunotherapeutic efficacy across various tumor types [76]. The current investigation revealed that SLC16A3 expression was significantly positively associated with TMB in THYM and UCS. In contrast, SLC16A3 expression was negatively associated with MSI in GBMLGG, KIPAN, and ACC, while exhibiting a positive correlation with colon adenocarcinoma and COADREAD, LAML, UVM, and COAD. Moreover, the tumor stemness score may offer valuable insights into the inherent heterogeneity of tumors and potentially serve as a prognostic indicator [77]. SLC16A3 expression showed an inverse correlation with DNAss in several cancer types, including TGCT, UCEC, and PCPG; however, it showed a positive association with MESO, KIRP, SARC, PRAD, UVM, THYM, THCA, CHOL, LGG, GBMLGG, and OV. The analysis also demonstrated a negative relationship of SLC16A3 expression with RNAss in various cancers such as GBMLGG, LGG, KIPAN, GBM, PRAD, THYM, PCPG, AML, KICH, THCA, KIRC, KIRP, DLBC, LUSC, CHOL, OV, and COAD. In conclusion, SLC16A3 expression may serve as a promising indicator to assess the efficacy of immunotherapy; thus, necessitating further clinical investigations on this aspect.

To elucidate the diverse functions of SLC16A3 and gain more comprehensive understanding of its role, we conducted GSEA to identify the functional network and enriched signaling pathways associated with SLC16A3. The proteins identified in the functional network, including SLC2A3, SLC2A1, SLC16A7, ALC17A5, and LDHA, are associated with lactate metabolism. SLC16A3, which affects the pentose phosphate pathway, can interact with galactose metabolism and other pathways to regulate immune responses and epithelial-mesenchymal transition (EMT) of cancer cells [78]. Waterfall plot analysis revealed key SLC16A3 mutation hotspots predominantly in transmembrane domains (e.g., TM3, TM6) and the C-terminal cytoplasmic region, critical for lactate transport [52,79]. For example, the Q215H missense mutation may impair proton-coupled transport, reducing lactate efflux, while truncating mutations like R302 compromise protein stability, disrupting metabolic reprogramming [80]. These functional defects likely exacerbate tumor microenvironment acidification via impaired lactate homeostasis, promoting immune evasion.

Our IHC results revealed significantly elevated levels of SLC16A3 expression in tumor samples of LUAD, LIHC, CESC, GBMLGG, COAD, PRAD, BRCA and KIRC tumor tissues compared to normal tissues. Survival analyses linked high SLC16A3 mRNA expression to adverse clinical outcomes in these malignancies, with protein-level overexpression consistently correlating with poor survival, confirming its clinical relevance. This transcriptional-translational concordance strengthens the hypothesis that SLC16A3-mediated lactate efflux directly fuels tumor progression and immune evasion, ultimately compromising patient survival. The role of SLC16A13 in biological processes was further analyzed, which revealed its involvement in lactate transport across the plasma membrane, including both transmembrane and import processes for glucose. Our IHC results confirmed the overexpression of SLC16A3 protein in various malignancies. However, discrepancies between mRNA and protein levels in certain cancers, such as READ and TGCT, may be attributed to post-transcriptional regulation. Potential mechanisms include microRNA-mediated suppression, such as miR-34a, which targets SLC16A3 in colorectal cancer [81,82], or protein stabilization through hypoxia-induced phosphorylation [83,84]. Furthermore, factors within the tumor microenvironment, such as acidosis, may enhance SLC16A3 protein stability via pH-sensitive ubiquitination pathways [85,86]. These complex regulatory layers highlight the critical need for multi-omics validation in future biomarker studies.

To translate these findings into clinical applications, three investigative axes are proposed. First, mechanistic validation through organoid-based CRISPR screens across underrepresented cancers, such as ACC and PCPG, will be crucial for delineating the context-dependent roles of SLC16A3 in immune evasion. Second, therapeutic development efforts will focus on optimizing SLC16A3-specific inhibitors, for in vivo testing in combination with anti-PD-1 therapies in syngeneic models of LGG and LUAD, incorporating lactate flux imaging to assess metabolic-immune crosstalk. Finally, clinical integration will involve establishing longitudinal cohorts to track SLC16A3 expression dynamics via liquid biopsy during immunotherapy, correlating these changes with stromal reprogramming and T cell clonality.

This study has several limitations. Reliance on public datasets (TCGA, CCLE, GTEx) may introduce biases due to incomplete clinical data and sample variability. Observational correlations between SLC16A3 expression, tumor progression, prognosis, and immunity do not establish causality, necessitating in vitro and in vivo validation. Lack of independent cohort validation limits generalizability. Additionally, tumor heterogeneity and other confounding factors were not considered, which may influence the findings.

5. Conclusion

Taken together, our pan-cancer analysis on SLC16A3 demonstrated that SLC16A3 expression was significantly associated with DNA methylation, protein phosphorylation, prognosis, immunomodulators, and infiltration of immune cells in diverse cancers. These findings may enhance our comprehension of the pivotal role of SLC16A3 in tumorigenesis.

Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Clinical trial number

Not applicable.

Consent to participate, and consent to publish declarations

All authors have reviewed and approved the manuscript.

CRediT authorship contribution statement

Wenxing Du: Writing – review & editing, Writing – original draft, Visualization, Software, Formal analysis, Conceptualization. Bo Zang: Writing – original draft, Methodology, Investigation, Conceptualization. Yang Wo: Writing – original draft, Methodology, Investigation, Conceptualization. Shiwei Chen: Writing – review & editing, Supervision, Project administration, Conceptualization.

Ethics declarations

Not applicable.

Data availability

The data on SLC16A3 expression of cancer cells were obtained from the CCLE database (https://portals.broadinstitute.org/ccle). RNA-sequencing data were collected from the GTEx project (https://commonfund.nih.gov/GTEx/) and the TCGA database (https://portal.gdc.cancer.gov). Protein expression profiles obtained from the Human Protein Atlas (HPA).

Funding

None.

Declaration of competing interest

I have nothing to declare.

List of Abbreviations

Abbreviation Full Term
ACC Adrenocortical carcinoma
BRCA Breast invasive carcinoma
CCLE Cancer Cell Line Encyclopedia
CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL Cholangiocarcinoma
COAD Colon adenocarcinoma
COADREAD Colorectal adenocarcinoma
DCs Dendritic cells
DFI Disease-free interval
DNAss Single-stranded DNA
DSS Disease-specific survival
ESCA Esophageal carcinoma
ESTIMATE Estimation of Stromal and Immune Cells in Malignant Tumors Using Expression
FDR False discovery rate
GBM Glioblastoma multiforme
GBMLGG Glioblastoma multiforme
GSEA Gene set enrichment analysis
GTEx Genotype-Tissue Expression project
HNSC Head and neck squamous cell carcinoma
HPA Human Protein Atlas
ICAM1 Intercellular cell adhesion molecule 1
ICIs Immune checkpoint inhibitors
KEGG Kyoto Encyclopedia of Genes and Genomes
KICH Kidney chromophobe
KIPAN Pan-kidney cohort
KIRC Kidney renal clear cell carcinoma
KIRP Kidney renal papillary cell carcinoma
LAML Acute myeloid leukemia
LGG Low-grade gliomas
LIHC Liver hepatocellular carcinoma
LUAD Lung adenocarcinoma
LUSC Lung squamous cell carcinoma
MCTs Monocarboxylate transporters
MSI Microsatellite instability
NES Nnrichment score
OS Overall survival
PCPG Pheochromocytoma and paraganglioma
PDCD1 Programmed death receptor 1
PD-L1 Programmed death ligand 1
PFI Progression-free interval
PPI Protein-protein interaction
PRAD Prostate adenocarcinoma
READ Rectum adenocarcinoma
RNAss Single-stranded RNA
ROS Reactive oxygen species
STAD Stomach adenocarcinoma
STES Stomach adenocarcinoma
TAMs Tumor-associated macrophages
TCGA The Cancer Genome Atlas
TGCT Testicular germ cell tumors
TGFB1 Transforming growth factor β1
THCA Thyroid carcinoma
THYM CD8+ T cells in thymoma
TMB Tumor mutational burden
TME Tumor microenvironment
TML Tumor mutational load
UVM Uveal melanoma
VEGFA Vascular endothelial growth factor A
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Data availability

The raw data analyzed in this study are freely available to the public without any restrictions.

References

  • 1.Siegel R.L., Miller K.D., Wagle N.S., Jemal A. Cancer statistics, 2023. CA Cancer J. Clin. 2023;73(1):17–48. doi: 10.3322/caac.21763. [DOI] [PubMed] [Google Scholar]
  • 2.Mitchell D., Dey M. Neoadjuvant anti-PD-1 immunotherapy for recurrent glioblastoma. Transl. Cancer Res. 2019;8(Suppl 6):S577–S579. doi: 10.21037/tcr.2019.05.24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Zanotta S., Galati D., De Filippi R., Pinto A. Breakthrough in blastic plasmacytoid dendritic cell neoplasm cancer therapy owing to precision targeting of CD123. Int. J. Mol. Sci. 2024;25(3) doi: 10.3390/ijms25031454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Hendriks L.E., Rouleau E., Besse B. Clinical utility of tumor mutational burden in patients with non-small cell lung cancer treated with immunotherapy. Transl. Lung Cancer Res. 2018;7(6):647–660. doi: 10.21037/tlcr.2018.09.22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Certo M., Tsai C.H., Pucino V., Ho P.C., Mauro C. Lactate modulation of immune responses in inflammatory versus tumour microenvironments. Nat. Rev. Immunol. 2021;21(3):151–161. doi: 10.1038/s41577-020-0406-2. [DOI] [PubMed] [Google Scholar]
  • 6.Fischer K., Hoffmann P., Voelkl S., et al. Inhibitory effect of tumor cell-derived lactic acid on human T cells. Blood. 2007;109(9):3812–3819. doi: 10.1182/blood-2006-07-035972. [DOI] [PubMed] [Google Scholar]
  • 7.Feng J., Yang H., Zhang Y., et al. Tumor cell-derived lactate induces TAZ-dependent upregulation of PD-L1 through GPR81 in human lung cancer cells. Oncogene. 2017;36(42):5829–5839. doi: 10.1038/onc.2017.188. [DOI] [PubMed] [Google Scholar]
  • 8.Brand A., Singer K., Koehl G.E., et al. LDHA-associated lactic acid production blunts tumor immunosurveillance by T and NK cells. Cell Metab. 2016;24(5):657–671. doi: 10.1016/j.cmet.2016.08.011. [DOI] [PubMed] [Google Scholar]
  • 9.Brown T.P., Bhattacharjee P., Ramachandran S., et al. The lactate receptor GPR81 promotes breast cancer growth via a paracrine mechanism involving antigen-presenting cells in the tumor microenvironment. Oncogene. 2020;39(16):3292–3304. doi: 10.1038/s41388-020-1216-5. [DOI] [PubMed] [Google Scholar]
  • 10.Ranganathan P., Shanmugam A., Swafford D., et al. GPR81, a cell-surface receptor for lactate, regulates intestinal homeostasis and protects mice from experimental colitis. J. Immunol. 2018;200(5):1781–1789. doi: 10.4049/jimmunol.1700604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Zhang D., Tang Z., Huang H., et al. Metabolic regulation of gene expression by histone lactylation. Nature. 2019;574(7779):575–580. doi: 10.1038/s41586-019-1678-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Petty A.J., Li A., Wang X., et al. Hedgehog signaling promotes tumor-associated macrophage polarization to suppress intratumoral CD8+ T cell recruitment. J. Clin. Investig. 2019;129(12):5151–5162. doi: 10.1172/JCI128644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Shan T., Chen S., Chen X., et al. M2-TAM subsets altered by lactic acid promote T-cell apoptosis through the PD-L1/PD-1 pathway. Oncol. Rep. 2020;44(5):1885–1894. doi: 10.3892/or.2020.7767. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Brown T.P., Ganapathy V. Lactate/GPR81 signaling and proton motive force in cancer: role in angiogenesis, immune escape, nutrition, and Warburg phenomenon. Pharmacol. Ther. 2020;206 doi: 10.1016/j.pharmthera.2019.107451. [DOI] [PubMed] [Google Scholar]
  • 15.Garcia-Canaveras J.C., Chen L., Rabinowitz J.D. The tumor metabolic microenvironment: lessons from lactate. Cancer Res. 2019;79(13):3155–3162. doi: 10.1158/0008-5472.CAN-18-3726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Payen V.L., Mina E., Van Hee V.F., Porporato P.E., Sonveaux P. Monocarboxylate transporters in cancer. Mol. Metabol. 2020;33:48–66. doi: 10.1016/j.molmet.2019.07.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Contreras-Baeza Y., Sandoval P.Y., Alarcon R., 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):20135–20147. doi: 10.1074/jbc.RA119.009093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Marchiq I., Le Floch R., Roux D., Simon M.P., Pouyssegur J. Genetic disruption of lactate/H+ symporters (MCTs) and their subunit CD147/BASIGIN sensitizes glycolytic tumor cells to phenformin. Cancer Res. 2015;75(1):171–180. doi: 10.1158/0008-5472.CAN-14-2260. [DOI] [PubMed] [Google Scholar]
  • 19.Kim H.K., Lee I., Bang H., et al. MCT4 expression is a potential therapeutic target in colorectal cancer with peritoneal carcinomatosis. Mol. Cancer Therapeut. 2018;17(4):838–848. doi: 10.1158/1535-7163.MCT-17-0535. [DOI] [PubMed] [Google Scholar]
  • 20.Chen H.L., OuYang H.Y., Le Y., et al. Aberrant MCT4 and GLUT1 expression is correlated with early recurrence and poor prognosis of hepatocellular carcinoma after hepatectomy. Cancer Med. 2018;7(11):5339–5350. doi: 10.1002/cam4.1521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Zhao Z., Han F., He Y., et al. Stromal-epithelial metabolic coupling in gastric cancer: stromal MCT4 and mitochondrial TOMM20 as poor prognostic factors. Eur. J. Surg. Oncol. 2014;40(10):1361–1368. doi: 10.1016/j.ejso.2014.04.005. [DOI] [PubMed] [Google Scholar]
  • 22.Sun Q., Hu L.L., Fu Q. MCT4 promotes cell proliferation and invasion of castration-resistant prostate cancer PC-3 cell line. EXCLI J. 2019;18:187–194. doi: 10.17179/excli2018-1879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Zhao Y., Zhao B., Yan W.H., et al. Integrative analysis identified MCT4 as an independent prognostic factor for bladder cancer. Front. Oncol. 2021;11 doi: 10.3389/fonc.2021.704857. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Barretina J., Caponigro G., Stransky N., et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–607. doi: 10.1038/nature11003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Li T., Fan J., Wang B., et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–e110. doi: 10.1158/0008-5472.CAN-17-0307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Yoshihara K., Shahmoradgoli M., Martinez E., et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 2013;4:2612. doi: 10.1038/ncomms3612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Kumar S., Gahramanov V., Patel S., et al. Evolution of resistance to irinotecan in cancer cells involves generation of topoisomerase-guided mutations in non-coding genome that reduce the chances of DNA breaks. Int. J. Mol. Sci. 2023;24(10) doi: 10.3390/ijms24108717. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Li P., Jia L., Bian X., Tan S. Application of engineered dendritic cell vaccines in cancer immunotherapy: challenges and opportunities. Curr. Treat. Options Oncol. 2023;24(12):1703–1719. doi: 10.1007/s11864-023-01143-7. [DOI] [PubMed] [Google Scholar]
  • 29.Sadeghirad H., Liu N., Monkman J., et al. Compartmentalized spatial profiling of the tumor microenvironment in head and neck squamous cell carcinoma identifies immune checkpoint molecules and tumor necrosis factor receptor superfamily members as biomarkers of response to immunotherapy. Front. Immunol. 2023;14 doi: 10.3389/fimmu.2023.1135489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Vaupel P., Schmidberger H., Mayer A. The Warburg effect: essential part of metabolic reprogramming and central contributor to cancer progression. Int. J. Radiat. Biol. 2019;95(7):912–919. doi: 10.1080/09553002.2019.1589653. [DOI] [PubMed] [Google Scholar]
  • 31.Koppenol W.H., Bounds P.L., Dang C.V. Otto Warburg's contributions to current concepts of cancer metabolism. Nat. Rev. Cancer. 2011;11(5):325–337. doi: 10.1038/nrc3038. [DOI] [PubMed] [Google Scholar]
  • 32.Goncalves A.C., Richiardone E., Jorge J., et al. Impact of cancer metabolism on therapy resistance - clinical implications. Drug Resist. Updates. 2021;59 doi: 10.1016/j.drup.2021.100797. [DOI] [PubMed] [Google Scholar]
  • 33.Pavlova N.N., Zhu J., Thompson C.B. The hallmarks of cancer metabolism: still emerging. Cell Metab. 2022;34(3):355–377. doi: 10.1016/j.cmet.2022.01.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Zhou D., Duan Z., Li Z., Ge F., Wei R., Kong L. The significance of glycolysis in tumor progression and its relationship with the tumor microenvironment. Front. Pharmacol. 2022;13 doi: 10.3389/fphar.2022.1091779. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Yu T., Liu Z., Tao Q., et al. Targeting tumor-intrinsic SLC16A3 to enhance anti-PD-1 efficacy via tumor immune microenvironment reprogramming. Cancer Lett. 2024;589 doi: 10.1016/j.canlet.2024.216824. [DOI] [PubMed] [Google Scholar]
  • 36.Xue L., Liu J., Xie J., Luo J. Prognostic value of slc16a3(MCT4) in lung adenocarcinoma and its clinical significance. Int. J. Gen. Med. 2021;14:8413–8425. doi: 10.2147/IJGM.S337615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Li J., Xie J., Wu D., et al. A pan-cancer analysis revealed the role of the SLC16 family in cancer. Channels. 2021;15(1):528–540. doi: 10.1080/19336950.2021.1965422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Shen J., Wu Z., Zhou Y., et al. Knockdown of SLC16A3 decreases extracellular lactate concentration in hepatocellular carcinoma, alleviates hypoxia and induces ferroptosis. Biochem. Biophys. Res. Commun. 2024;733 doi: 10.1016/j.bbrc.2024.150709. [DOI] [PubMed] [Google Scholar]
  • 39.Martinez-Reyes I., Chandel N.S. Cancer metabolism: looking forward. Nat. Rev. Cancer. 2021;21(10):669–680. doi: 10.1038/s41568-021-00378-6. [DOI] [PubMed] [Google Scholar]
  • 40.Vaupel P., Multhoff G. Revisiting the Warburg effect: historical dogma versus current understanding. J. Physiol. 2021;599(6):1745–1757. doi: 10.1113/JP278810. [DOI] [PubMed] [Google Scholar]
  • 41.Liu Z., Yu Z., Chen D., et al. Pivotal roles of tumor-draining lymph nodes in the abscopal effects from combined immunotherapy and radiotherapy. Cancer Commun. 2022;42(10):971–986. doi: 10.1002/cac2.12348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Li Q., Chen Y., Liu H., Tian Y., Yin G., Xie Q. Targeting glycolytic pathway in fibroblast-like synoviocytes for rheumatoid arthritis therapy: challenges and opportunities. Inflamm. Res. 2023;72(12):2155–2167. doi: 10.1007/s00011-023-01807-y. [DOI] [PubMed] [Google Scholar]
  • 43.Bi L., Ren Y., Feng M., et al. HDAC11 regulates glycolysis through the LKB1/AMPK signaling pathway to maintain hepatocellular carcinoma stemness. Cancer Res. 2021;81(8):2015–2028. doi: 10.1158/0008-5472.CAN-20-3044. [DOI] [PubMed] [Google Scholar]
  • 44.Luo X., Zheng E., Wei L., et al. The fatty acid receptor CD36 promotes HCC progression through activating Src/PI3K/AKT axis-dependent aerobic glycolysis. Cell Death Dis. 2021;12(4):328. doi: 10.1038/s41419-021-03596-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Xiao C., Tian H., Zheng Y., et al. Glycolysis in tumor microenvironment as a target to improve cancer immunotherapy. Front. Cell Dev. Biol. 2022;10 doi: 10.3389/fcell.2022.1013885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Liu L.X., Heng J.H., Deng D.X., et al. Sulconazole induces PANoptosis by triggering oxidative stress and inhibiting glycolysis to increase radiosensitivity in esophageal cancer. Mol. Cell. Proteomics. 2023;22(6) doi: 10.1016/j.mcpro.2023.100551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Cantelmo A.R., Conradi L.C., Brajic A., et al. Inhibition of the glycolytic activator PFKFB3 in endothelium induces tumor vessel normalization, impairs metastasis, and improves chemotherapy. Cancer Cell. 2016;30(6):968–985. doi: 10.1016/j.ccell.2016.10.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Chen L., Xing X., Zhu Y., et al. Palmitoylation alters LDHA activity and pancreatic cancer response to chemotherapy. Cancer Lett. 2024;587 doi: 10.1016/j.canlet.2024.216696. [DOI] [PubMed] [Google Scholar]
  • 49.Zhao G., Forn-Cuni G., Scheers M., et al. Simultaneous targeting of AMPK and mTOR is a novel therapeutic strategy against prostate cancer. Cancer Lett. 2024;587 doi: 10.1016/j.canlet.2024.216657. [DOI] [PubMed] [Google Scholar]
  • 50.Chen M., Cen K., Song Y., et al. NUSAP1-LDHA-Glycolysis-Lactate feedforward loop promotes Warburg effect and metastasis in pancreatic ductal adenocarcinoma. Cancer Lett. 2023;567 doi: 10.1016/j.canlet.2023.216285. [DOI] [PubMed] [Google Scholar]
  • 51.Amorim R., Pinheiro C., Miranda-Goncalves V., et al. Monocarboxylate transport inhibition potentiates the cytotoxic effect of 5-fluorouracil in colorectal cancer cells. Cancer Lett. 2015;365(1):68–78. doi: 10.1016/j.canlet.2015.05.015. [DOI] [PubMed] [Google Scholar]
  • 52.Bosshart P.D., Kalbermatter D., Bonetti S., Fotiadis D. Mechanistic basis of L-lactate transport in the SLC16 solute carrier family. Nat. Commun. 2019;10(1):2649. doi: 10.1038/s41467-019-10566-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Golias T., Kery M., Radenkovic S., Papandreou I. Microenvironmental control of glucose metabolism in tumors by regulation of pyruvate dehydrogenase. Int. J. Cancer. 2019;144(4):674–686. doi: 10.1002/ijc.31812. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Hong S.M., Lee Y.K., Park I., Kwon S.M., Min S., Yoon G. Lactic acidosis caused by repressed lactate dehydrogenase subunit B expression down-regulates mitochondrial oxidative phosphorylation via the pyruvate dehydrogenase (PDH)-PDH kinase axis. J. Biol. Chem. 2019;294(19):7810–7820. doi: 10.1074/jbc.RA118.006095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Li F., Si W., Xia L., et al. Positive feedback regulation between glycolysis and histone lactylation drives oncogenesis in pancreatic ductal adenocarcinoma. Mol. Cancer. 2024;23(1):90. doi: 10.1186/s12943-024-02008-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Noe J.T., Rendon B.E., Geller A.E., et al. Lactate supports a metabolic-epigenetic link in macrophage polarization. Sci. Adv. 2021;7(46) doi: 10.1126/sciadv.abi8602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Yuan P., Mu J., Wang Z., et al. Down-regulation of SLC25A20 promotes hepatocellular carcinoma growth and metastasis through suppression of fatty-acid oxidation. Cell Death Dis. 2021;12(4):361. doi: 10.1038/s41419-021-03648-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Delaunay S., Pascual G., Feng B., et al. Mitochondrial RNA modifications shape metabolic plasticity in metastasis. Nature. 2022;607(7919):593–603. doi: 10.1038/s41586-022-04898-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Feng Q., Liu Z., Yu X., et al. Lactate increases stemness of CD8 + T cells to augment anti-tumor immunity. Nat. Commun. 2022;13(1):4981. doi: 10.1038/s41467-022-32521-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Ye L., Jiang Y., Zhang M. Crosstalk between glucose metabolism, lactate production and immune response modulation. Cytokine Growth Factor Rev. 2022;68:81–92. doi: 10.1016/j.cytogfr.2022.11.001. [DOI] [PubMed] [Google Scholar]
  • 61.Wang J., Akter R., Shahriar M.F., Uddin M.N. Cancer-associated stromal fibroblast-derived transcriptomes predict poor clinical outcomes and immunosuppression in colon cancer. Pathol. Oncol. Res. 2022;28 doi: 10.3389/pore.2022.1610350. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Markou A., Tzanikou E., Kallergi G., et al. Evaluation of monocarboxylate transporter 4 (MCT4) expression and its prognostic significance in circulating tumor cells from patients with early stage non-small-cell lung cancer. Front. Cell Dev. Biol. 2021;9 doi: 10.3389/fcell.2021.641978. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Reuss A.M., Groos D., Ghoochani A., Buchfelder M., Savaskan N. MCT4 promotes tumor malignancy in F98 glioma cells. JAMA Oncol. 2021;2021 doi: 10.1155/2021/6655529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Li N., Kang Y., Wang L., et al. ALKBH5 regulates anti-PD-1 therapy response by modulating lactate and suppressive immune cell accumulation in tumor microenvironment. Proc. Natl. Acad. Sci. U. S. A. 2020;117(33):20159–20170. doi: 10.1073/pnas.1918986117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Fang Y., Liu W., Tang Z., et al. Monocarboxylate transporter 4 inhibition potentiates hepatocellular carcinoma immunotherapy through enhancing T cell infiltration and immune attack. Hepatology. 2023;77(1):109–123. doi: 10.1002/hep.32348. [DOI] [PubMed] [Google Scholar]
  • 66.Babl N., Decking S.M., Voll F., et al. MCT4 blockade increases the efficacy of immune checkpoint blockade. J Immunother Cancer. 2023;11(10) doi: 10.1136/jitc-2023-007349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Zhu T., Ge X., Gong S., et al. Prognostic value of lactate transporter SLC16A1 and SLC16A3 as oncoimmunological biomarkers associating tumor metabolism and immune evasion in glioma. Cancer Innov. 2022;1(3):229–239. doi: 10.1002/cai2.32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Tao Q., Li X., Zhu T., et al. Lactate transporter SLC16A3 (MCT4) as an onco-immunological biomarker associating tumor microenvironment and immune responses in lung cancer. Int. J. Gen. Med. 2022;15:4465–4474. doi: 10.2147/IJGM.S353592. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Okiyama N., Tanaka R. Immune-related adverse events in various organs caused by immune checkpoint inhibitors. Allergol. Int. 2022;71(2):169–178. doi: 10.1016/j.alit.2022.01.001. [DOI] [PubMed] [Google Scholar]
  • 70.Gaikwad S., Agrawal M.Y., Kaushik I., Ramachandran S., Srivastava S.K. Immune checkpoint proteins: signaling mechanisms and molecular interactions in cancer immunotherapy. Semin. Cancer Biol. 2022;86(Pt 3):137–150. doi: 10.1016/j.semcancer.2022.03.014. [DOI] [PubMed] [Google Scholar]
  • 71.Zhang J., Bao Y., Li Y., Shi X., Su X., He X. Different lactate metabolism subtypes reveal heterogeneity in clinical outcomes and immunotherapy in lung adenocarcinoma patients. Heliyon. 2024;10(10) doi: 10.1016/j.heliyon.2024.e30781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Keshari S., Shavkunov A.S., Miao Q., et al. Neoantigen cancer vaccines and different immune checkpoint therapies each utilize both converging and distinct mechanisms that in combination enable synergistic therapeutic efficacy. bioRxiv. 2024 doi: 10.1101/2023.12.20.570816. [DOI] [Google Scholar]
  • 73.Kiyozawa D., Takamatsu D., Kohashi K., et al. Programmed death ligand 1/indoleamine 2,3-dioxygenase 1 expression and tumor-infiltrating lymphocyte status in renal cell carcinoma with sarcomatoid changes and rhabdoid features. Hum. Pathol. 2020;101:31–39. doi: 10.1016/j.humpath.2020.04.003. [DOI] [PubMed] [Google Scholar]
  • 74.Yuen K.C., Liu L.F., Gupta V., et al. High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade. Nat. Med. 2020;26(5):693–698. doi: 10.1038/s41591-020-0860-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Carbone D.P., Reck M., Paz-Ares L., et al. First-Line nivolumab in stage IV or recurrent non-small-cell lung cancer. N. Engl. J. Med. 2017;376(25):2415–2426. doi: 10.1056/NEJMoa1613493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Berger M.F., Hodis E., Heffernan T.P., et al. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature. 2012;485(7399):502–506. doi: 10.1038/nature11071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Walcher L., Kistenmacher A.K., Suo H., et al. Cancer stem cells-origins and biomarkers: perspectives for targeted personalized therapies. Front. Immunol. 2020;11:1280. doi: 10.3389/fimmu.2020.01280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Halestrap A.P. The SLC16 gene family - structure, role and regulation in health and disease. Mol. Aspect. Med. 2013;34(2–3):337–349. doi: 10.1016/j.mam.2012.05.003. [DOI] [PubMed] [Google Scholar]
  • 79.Higuchi A., Nonaka N., Yura K. iMusta4SLC: database for the structural property and variations of solute carrier transporters. Biophys Physicobiol. 2018;15:94–103. doi: 10.2142/biophysico.15.0_94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.White B., Swietach P. What can we learn about acid-base transporters in cancer from studying somatic mutations in their genes? Pflügers Archiv. 2024;476(4):673–688. doi: 10.1007/s00424-023-02876-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Fawzy M.S., Ibrahiem A.T., AlSel B.T.A., Alghamdi S.A., Toraih E.A. Analysis of microRNA-34a expression profile and rs2666433 variant in colorectal cancer: a pilot study. Sci. Rep. 2020;10(1) doi: 10.1038/s41598-020-73951-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Zhang X., Ai F., Li X., et al. MicroRNA-34a suppresses colorectal cancer metastasis by regulating Notch signaling. Oncol. Lett. 2017;14(2):2325–2333. doi: 10.3892/ol.2017.6444. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Han H.J., Saeidi S., Kim S.J., et al. Alternative regulation of HIF-1alpha stability through phosphorylation on Ser451. Biochem. Biophys. Res. Commun. 2021;545:150–156. doi: 10.1016/j.bbrc.2021.01.047. [DOI] [PubMed] [Google Scholar]
  • 84.Chua Y.L., Dufour E., Dassa E.P., et al. Stabilization of hypoxia-inducible factor-1alpha protein in hypoxia occurs independently of mitochondrial reactive oxygen species production. J. Biol. Chem. 2010;285(41):31277–31284. doi: 10.1074/jbc.M110.158485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Ihling A., Ihling C.H., Sinz A., Gekle M. Acidosis-induced changes in proteome patterns of the prostate cancer-derived tumor cell line AT-1. J. Proteome Res. 2015;14(9):3996–4004. doi: 10.1021/acs.jproteome.5b00503. [DOI] [PubMed] [Google Scholar]
  • 86.Jia M., Zheng D., Wang X., et al. Cancer cell enters reversible quiescence through intracellular acidification to resist paclitaxel cytotoxicity. Int. J. Med. Sci. 2020;17(11):1652–1664. doi: 10.7150/ijms.46034. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data on SLC16A3 expression of cancer cells were obtained from the CCLE database (https://portals.broadinstitute.org/ccle). RNA-sequencing data were collected from the GTEx project (https://commonfund.nih.gov/GTEx/) and the TCGA database (https://portal.gdc.cancer.gov). Protein expression profiles obtained from the Human Protein Atlas (HPA).

The raw data analyzed in this study are freely available to the public without any restrictions.

Articles from Biochemistry and Biophysics Reports are provided here courtesy of Elsevier

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