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Biochemistry and Biophysics Reports
journal homepage: www.elsevier.com/locate/bbrep
BB Reports
SLC16A3 (MCT4) expression in tumor immunity and Metabolism: Insights from pan-cancer analysis
Wenxing Du ª,11, Bo Zang b,1, Yang Woª, Shiwei Chen ”,”
a Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China
b Department of Rheumatology, Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China
” The Affiliated Traditional Chinese Medicine Hospital, Guangzhou Medical University, Guangzhou, Guangdong Province, China
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ARTICLE INFO
Keywords:
SLC16A3 (MCT4)
Lactic acid
Glycolysis
Pan-cancer analysis Immunity
ABSTRACT
Background: SLC16A3, a highly expressed H + -coupled symporter, facilitates lactate transport via mono- carboxylate 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, meta- bolism, 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.
1. Introduction
Cancer, a prevalent illness that impacts millions of individuals, is a significant global contributor to mortality [1]. Although many treat- ments 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 [6-8]. Moreover, the differentiation of dendritic cells (DCs) and the production of inflammatory factors such as IL-6 and TNF
* Corresponding author. The Affiliated Traditional Chinese Medicine Hospital, Guangzhou Medical University, No 16 Zhuji Road, Liwan District, Guangzhou City, Guangdong, 510145, China. E-mail address: 18366612@qq.com (S. Chen).
1 These authors contributed equally to this work.
https://doi.org/10.1016/j.bbrep.2025.102034
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 polariza- tion to attenuate the efficacy of immune response and facilitate immu- nosuppression [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 malig- nancy grade of tumors. Following SLC16A3 deficiency, lactate accu- mulates 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 in- vasion; thus, suggesting early relapse and dismal prognostic outcomes of many cancers such as hepatocellular carcinoma and colorectal, gastric, prostate, and bladder cancers [19-23]. 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 signifi- cance 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 infil- tration 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.go v/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 infil- tration 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 for- est 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 chemo- kine receptors, chemokines, immune activation proteins, and immuno- suppressive 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 con- structed 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 relation- ships 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 itera- tions 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 cat- egories 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 Statis- tical 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 wide- spread 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 adenocar- cinoma (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
A
Kruskal-Wallis test p=0
B
Kruskal-Wallis test p=3e-31
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log2(TPM+1)
Gene Expression
8
5.0
A
5.
2.5
8
Adipose Tissue(N=515) Adrenal Gland(N=128)
skin(N=62)
Bladder(N=9)
Blood Vessel(N=606)
Blood(N=444)
Bone Marrow(N=70)
Brain(N=1152)
Breast(N=179)
Cervix Uteri(N=10)
Colon(N=308)
Esophagus(N=653)
Fallopian Tube(N=5)
Heart(N=377)
Kidney(N=28)
Liver(N=110)
Lung(N=288)
Muscle(N=396)
Nerve(N=278)
Ovary(N=88) Pancreas(N=167)
Pituitary(N=107)
Prostate(N=100)
Salivary Gland(N=55)
Skin(N=812)
Small Intestine(N=92)
Spleen(N=100)
Stomach(N=174)
Testis(N=165)
Thyroid(N=279)
Uterus(N=78)
bone(N=29)
Vagina(N=85)
biliary_tract(N=7)
breast(N=60)
central_nervous_system(N=103)
haematopoietic_and_lymphoid(N=146)
intestine(N=61)
kidney(N=36)
liver(N=108)
lung(N=107)
oesophagus(N=26)
ovary(N=52)
pancreas(N=52)
pleura(N=11)
salivary gland(N=2)
soft_tissue(N=21)
stomach(N=38)
thyroid(N=12)
upper_aerodigestive_tract(N=32)
urinary_tract(N=27)
uterus(N=27)
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GBM(T=153,N=1157)
GBMLGG(T=662,N=1157)
LGG(T=509,N=1157)
UCEC(T=180,N=23)
BRCA(T=1092,N=292)
CESC(T=304,N=13)
LUAD(T=513,N=397)
ESCA(T=181,N=668)
STES(T=595,N=879)
KIRP(T=288,N=168)
KIPAN(T=884,N=168)’
COAD(T=288,N=349)
COADREAD(T=380,N=359)
PRAD(T=495,N=152)
STAD(T=414,N=211)
HNSC(T=518,N=44)
KIRC(T=530,N=168)
LUSC(T=498,N=397)
LIHC(T=369,N=160)
WT(T=120,N=168)
SKCM(T=102,N=558)
BLCA(T=407,N=28)
THCA(T=504,N=338)
READ(T=92,N=10)
OV(T=419,N=88)
PAAD(T=178,N=171)
TGCT(T=148,N=165)
UCS(T=57,N=78)
ALL(T=132,N=337)
LAML(T=173,N=337)
PCPG(T=177,N=3)
ACC(T=77,N=128)
KICH(T=66,N=168)
CHOL(T=36,N=9)
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GBMLGG(T=662,N=5)
LGG(T=509,N=5)
CESC(T=304,N=3)
LUAD(T=513,N=109)
COAD(T=288,N=41)
COADREAD(T=380,N=51)
BRCA(T=1092,N=113)
ESCA(T=181,N=13)
STES(T=595,N=49)
KIRP(T=288,N=129)
KIPAN(T=884,N=129)
STAD(T=414,N=36)>
PRAD(T=495,N=52)
UCEC(T=180,N=23)
HNSC(T=518,N=44)
KIRC(T=530,N=129)
LUSC(T=498,N=109)>
LIHC(T=369,N=50)
THCA(T=504,N=59)
READ(T=92,N=10)
PAAD(T=178,N=4)
PCPG(T=177,N=3)
BLCA(T=407,N=19)
KICH(T=66,N=129)
CHOL(T=36,N=9)
adenocarcinoma (READ), testicular germ cell tumors (TGCT), pheo- chromocytoma and paraganglioma (PCPG), or adrenocortical carcinoma (ACC).
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 propor- tional hazards regression analysis was performed to assess the prog- nostic value of SLC16A3 for diverse cancer types. SLC16A3 expression showed a marked relationship with the overall survival (OS) for glio- blastoma 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).
| A | OS pvalue | ||
|---|---|---|---|
| CancerCode | Hazard Ratio(95%CI) | ||
| TCGA-GBMLGG(N=619) | 5.9e-39 | 1.88(1.71,2.08) | |
| TCGA-LGG(N=474) | 7.0e-8 | 1 | 1.51(1.30,1.75) |
| TCGA-LIHC(N=341) | 2.1e-7 | 1.29(1.17,1.42) | |
| TCGA-LAML(N=209) | 3.6e-6 | O | 1.27(1.15,1.41) |
| TCGA-LUAD(N=490) | 7.6c-5 | 1 | 1.33(1.15,1.53) |
| TCGA-KIPAN(N=855) | 1.0e-4 | 1.19(1.09,1.31) | |
| TCGA-CESC(N=273) | 3.4c-4 | F- -1 | 1.53(1.21,1.93) |
| TCGA-MESO(N=84) | 3.9e-4 | 1.51(1.20,1.91) | |
| TCGA-PAAD(N=172) | 1.2e-3 | .. 1 | 1.31(1.11,1.54) |
| TCGA-GBM(N=144) | 4.3c-3 | 1- -I | 1.31(1.09,1.58) |
| TCGA-BLCA(N=398) | 7.3e-3 | 1.17(1.04,1.31) | |
| TCGA-LUSC(N=468) | 0.03 | ] | 1.17(1.02,1.35) |
| TCGA-HNSC(N=509) | 0.04 | 1 1 | 1.17(1.01,1.36) |
| TCGA-KIRP(N=276) | 0.07 | K -1 | 1.23(0.98,1.53) |
| TCGA-CHOL(N=33) | 0.07 | I .. ---- | | 1.44(0.96,2.15) |
| TCGA-UVM(N=74) | 0.08 | I ... | 1.37(0.96,1.95) |
| TARGET-LAML(N=142) | 0.12 | F: | 1.14(0.97,1.34) |
| TCGA-THCA(N=501) | 0.13 | F | 1.47(0.89,2.43) |
| TCGA-UCS(N=55) | 0.14 | 1: -- I | 1.24(0.93,1.65) |
| TCGA-TGCT(N=128) | 0.15 | | ...... .. 1 | 2.92(0.63,13.48) |
| TCGA-BRCA(N=1044) | 0.24 | -1 | 1.09(0.95,1.25) |
| TCGA-KICH(N=64) | 0.37 | I --- | 1.20(0.80,1.80) |
| TCGA-ACC(N=77) | 0.49 | I- -I | 1.08(0.87,1.33) |
| TCGA-SARC(N=254) | 0.51 | I | 1.05(0.91,1.21) |
| TARGET-ALL-R(N=99) | 0.52 | I- 4 | 1.06(0.88,1.28) |
| TARGET-NB(N=151) | 0.60 | I- -| | 1.05(0.87,1.27) |
| TCGA-PRAD(N=492) | 0.64 | -- .. ....... | | 1.15(0.63,2.10) |
| TARGET-WT(N=80) | 0.67 | 1- -4 | 1.05(0.84,1.31) |
| TCGA-SKCM-P(N=97) | 0.71 | F- 1 | 1.04(0.83,1.31) |
| TCGA-READ(N=90) | 0.75 | I- : . | 1.11(0.60,2.04) |
| TCGA-OV(N=407) | 0.86 | . 4 | 1.01(0.91,1.12) |
| TCGA-COADREAD(N=368) | 0.95 | I- -I | 1.01(0.82,1.24) |
| TARGET-ALL(N=86) | 0.03 | € | 0.78(0.62,0.98) |
| TCGA-PCPG(N=170) | 0.32 | I · | 0.70(0.35,1.41) |
| TCGA-DLBC(N=44) | 0.49 | + . 1 | 0.76(0.35,1.67) |
| TCGA-ESCA(N=175) | 0.59 | 0.93(0.72,1.21) | |
| TCGA-STES(N=547) | 0.70 | I- I | 0.98(0.86,1.11) |
| TCGA-KIRC(N=515) | 0.70 | I | 0.97(0.86,1.11) |
| TCGA-UCEC(N=166) | 0.71 | I- -I | 0.95(0.73,1.24) |
| TCGA-SKCM(N=444) | 0.73 | 0.99(0.91,1.07) | |
| TCGA-SKCM-M(N=347) | 0.77 | 0.99(0.90,1.08) | |
| TCGA-STAD(N=372) | 0.81 | 1- I | 0.98(0.85,1.14) |
| TCGA-THYM(N=117) | 0.88 | F D 1 | 0.93(0.37,2.32) |
| TCGA-COAD(N=278) | 0.98 | -I | 1.00(0.80,1.24) |
-
5-1.0
1.0-0.5 0.0
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1.0
1.5
2.0
0
2.5
3.0
3.5
log2(Hazard Ratio(95%CI))
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| CancerCode | pvalue | DFI | Hazard Ratio(95%CI) |
|---|---|---|---|
| TCGA-CESC(N=171) | 3.0e-3 | 1.82(1.22,2.73) | |
| TCGA-PAAD(N=68) | 4.3e-3 | ----- | 1.82(1.20,2.74) |
| TCGA-CHOL(N=23) | 0.01 | I- | 1.77(1.11,2.80) |
| TCGA-LUAD(N=295) | 0.02 | + + | 1.26(1.04,1.53) |
| TCGA-SARC(N=149) | 0.06 | - | 1.19(0.99,1.42) |
| TCGA-PRAD(N=337) | 0.06 | 1.46(0.98,2.17) | |
| TCGA-HNSC(N=128) | 0.09 | ---- | | 1.47(0.94,2.28) |
| TCGA-LUSC(N=292) | 0.15 | F | 1.22(0.93,1.61) |
| TCGA-LIHC(N=294) | 0.21 | OH | 1.07(0.96,1.18) |
| TCGA-THCA(N=352) | 0.22 | 1 -: - | 1.27(0.87,1.86) |
| TCGA-KIRC(N=113) | 0.22 | ト· ....... | 1.51(0.78,2.92) |
| TCGA-MESO(N=14) | 0.32 | ........ 1 | 1.43(0.69,2.95) |
| TCGA-KIPAN(N=319) | 0.33 | F -1 | 1.09(0.92,1.29) |
| TCGA-GBMLGG(N=127) | 0.35 | I -- -- 1 | 1.20(0.82,1.77) |
| TCGA-PCPG(N=152) | 0.37 | 1.28(0.74,2.22) | |
| TCGA-BRCA(N=904) | 0.38 | F 1 | 1.09(0.90,1.30) |
| TCGA-TGCT(N=101) | 0.38 | 1.18(0.81,1.71) | |
| TCGA-LGG(N=126) | 0.44 | --- 4 . -- 1 | 1.17(0.78,1.75) |
| TCGA-ACC(N=44) | 0.52 | . .1 | 1.12(0.79,1.60) |
| TCGA-COAD(N=103) | 0.53 | 1.18(0.69,2.02) | |
| TCGA-BLCA(N=184) | 0.71 | F 王 | 1.05(0.80,1.38) |
| TCGA-COADREAD(N=132) | 0.76 | -- I | 1.07(0.68,1.69) |
| TCGA-KICH(N=29) | 0.83 | I- . -1 | 1.10(0.48,2,49) |
| TCGA-KIRP(N=177) | 0.95 | F .1 | 1.01(0.77,1.32) |
| TCGA-UCS(N=26) | 0.10 | I- -1 | 0.56(0.29,1.12) |
| TCGA-OV(N=203) | 0.22 | I- I | 0.91(0.78,1.06) |
| TCGA-UCEC(N=115) | 0.27 | ·- | | 0.82(0.57,1.17) |
| TCGA-DLBC(N=26) | 0.33 | F -1 | 0.38(0.05,2.78) |
| TCGA-READ(N=29) | 0.50 | 1- -I | 0.61(0.15,2.54) |
| TCGA-ESCA(N=84) | 0.53 | 1 ---- | 0.86(0.54,1.37) |
| TCGA-STES(N-316) | 0.74 | I- -I | 0.96(0.76,1.22) |
| TCGA-STAD(N=232) | 0.90 | 0.98(0.74,1.30) |
-4.0-3.5-3.0-2.5-2.0-1.5-1.0-0.5 0.0 0.5 1.0 1.5 log2(Hazard Ratio(95%CI))
| B CancerCode | pvalue | DSS | Hazard Ratio(95%CI) | ||
|---|---|---|---|---|---|
| TCGA-GBMLGG(N=598) | 2.5e-35 | 1.89(1.70,2.10) | |||
| TCGA-LGG(N=466) | 5.7e-8 | 1.53(1.31,1.79) | |||
| TCGA-PAAD(N=166) | 6.5e-4 | ト· 1 | 1.39(1.15,1.67) | ||
| TCGA-LUSC(N=418) | 8.2e-4 | F - -1 | 1.48(1.18,1.86) | ||
| TCGA-LIHC(N=333) | 1.2e-3 | 1 | 1.23(1.08,1.40) | ||
| TCGA-KIPAN(N=840) | 1.4e-3 | 1.20(1.07,1.35) | |||
| TCGA-CESC(N=269) | 2.2e-3 | -- 1 | 1.52(1.16,2.00) | ||
| TCGA-MESO(N=64) | 3.5e-3 | -- 1 | 1.56(1.15,2.12) | ||
| TCGA-LUAD(N=457) | 4.5e-3 | + + | 1.29(1.08,1.53) | ||
| TCGA-GBM(N=131) | 7.9e-3 | ·- -| | 1.31(1.07,1.60) | ||
| TCGA-BRCA(N=1025) | 0.04 | - - | 1.21(1.01,1.46) | ||
| TCGA-UVM(N=74) | 0.08 | F | 1.41(0.96,2.06) | ||
| TCGA-CHOL(N=32) | 0.08 | I: | . | 1.44(0.95,2.18) | |
| TCGA-HNSC(N=485) | 0.09 | Pas 4 | 1.18(0.98,1.43) | ||
| TCGA-KIRP(N=272) | 0.10 | l: ·- I | 1.26(0.96,1.66) | ||
| TCGA-UCS(N=53) | 0.12 | 1- | 1.26(0.94,1.69) | ||
| TCGA-BLCA(N=385) | 0.15 | I | 1.10(0.97,1.26) | ||
| TCGA-KICH(N=64) | 0.18 | ト 1. ..... | 1.36(0.85,2.17) | ||
| TCGA-READ(N=84) | 0.20 | 2.01(0.70,5.74) | |||
| TOGA-COADREAD(N=347) | 0.22 | - A | 1.23(0.89,1.70) | ||
| TCGA-THCA(N=495) | 0.27 | F 1 | 1.53(0.72,3.28) | ||
| TCGA-SKCM-P(N=97) | 0.29 | 1-5 | 1.16(0.88,1.54) | ||
| TCGA-PRAD(N=490) | 0.30 | F : ·1 | 1.60(0.66,3.89) | ||
| TCGA-TGCT(N=128) | 0.33 | : - | -1 2.07(0.46,9.28) | ||
| TCGA-THYM(N=117) | 0.39 | * -1 | 1.70(0.50,5.73) | ||
| TCGA-COAD(N=263) | 0.44 | F | 1.14(0.82,1.57) | ||
| TCGA-STES(N=524) | 0.62 | I 1 | 1.04(0.89,1.22) | ||
| TCGA-ACC(N=75) | 0.67 | I- - I | 1.05(0.84,1.32) | ||
| TCGA-ESCA(N=173) | 0.77 | -4 | 1.05(0.76,1.44) | ||
| TCGA-STAD(N=351) | 0.79 | F 4 | 1.03(0.85,1.24) | ||
| TCGA-SARC(N=248) | 0.81 | F- I | 1.02(0.87,1.19) | ||
| TCGA-KIRC(N=504) | 0.86 | F 1 | 1.02(0.85,1.21) | ||
| TCGA-UCEC(N=164) | 0.96 | 1.01(0.73,1.40) | |||
| TCGA-PCPG(N=170) | 0.24 | 1 . ····- | | 0.54(0.20,1.48) | ||
| TCGA-DLBC(N=44) | 0.45 | F 4 | 0.68(0.24,1.89) | ||
| TCGA-OV(N=378) | 0.79 | 0.99(0.88,1.10) | |||
| TCGA-SKCM-M(N=341) | 0.83 | . | 0.99(0.90,1.09) | ||
| TCGA-SKCM(N=438) | 1.00 | O | 1.00(0.92,1.09) | ||
-2.0-1.5-1.0-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 log2(Hazard Ratio(95%CI))
D
| CancerCode | pvalue | Hazard Ratio(95%CI) | ||
|---|---|---|---|---|
| TCGA-GBMLGG(N=616) | 1.6e-32 | -1 | 1.70(1.55,1.85) | |
| TCGA-LGG(N=472) | 2.6e-8 | F | 1.44(1.27,1.64) | |
| TCGA-CESC(N=273) | 7.4c-5 | 1.61(1.27,2.03) | ||
| TCGA-LUSC(N=467) | 4.7e-4 | 1.36(1.14,1.61) | ||
| TCGA-KIPAN(N=845) | 5.2e-4 | - 1 | 1.17(1.07,1.28) | |
| TCGA-PAAD(N=171) | 5.6e-4 | 1.31(1.12,1.53) | ||
| TCGA-PRAD(N=492) | 1.0e-3 | 1.44(1.16,1.80) | ||
| TCGA-MESO(N=82) | 1.4e-3 | -- 1 | 1.50(1.17,1.92) | |
| TCGA-LUAD(N=486) | 9.3c-3 | ト· - | 1.18(1.04,1.34) | |
| TCGA-GBM(N=143) | 0.05 | --- | | 1.19(1.00,1.42) | |
| TCGA-LIHC(N=340) | 0.07 | £ -1 1 | 1.08(0.99,1.18) | |
| TCGA-SARC(N=250) | 0.08 | F -1 | 1.11(0.99,1.26) | |
| TCGA-BRCA(N=1043) | 0.09 | E' ·- 1 | 1.13(0.98,1.30) | |
| TCGA-COADREAD(N=363) | 0.12 | 1.18(0.96,1.44) | ||
| TCGA-KIRP(N=273) | 0.15 | 1 | 1.15(0.95,1.40) | |
| TCGA-COAD(N=275) | 0.17 | I- | - | 1.17(0.94,1.45) |
| TCGA-CHOL(N=33) | 0.17 | 1 | 1.25(0.91,1.71) | |
| TCGA-HNSC(N=508) | 0.20 | F | 1.11(0.95,1.30) | |
| TCGA-UVM(N=73) | 0.27 | -I | 1.18(0.88,1.57) | |
| TCGA-THYM(N=117) | 0.28 | * 4 | 1.34(0.79,2.28) | |
| TCGA-BLCA(N=397) | 0.29 | 1- | -I | 1.06(0.95,1.19) |
| TCGA-SKCM-M(N=338) | 0.39 | + -1 | 1.03(0.96,1.11) | |
| TCGA-SKCM(N=434) | 0.42 | - -1 | 1.03(0.96,1.10) | |
| TCGA-KICH(N=64) | 0.44 | ...... -| | 1.15(0.80,1.66) | |
| TCGA-PCPG(N=168) | 0.45 | 1.11(0.84,1.48) | ||
| TOGA-TGCT(N=126) | 0.50 | I- | 1.12(0.81,1.54) | |
| TCGA-STES(N=548) | 0.51 | F- | -4 | 1.04(0.92,1.19) |
| TCGA-KIRC(N=508) | 0.52 | 1 -- | - -I | 1.05(0.91,1.22) |
| TCGA-THCA(N=499) | 0.52 | F | -1 | 1.09(0.83,1.43) |
| TCGA-ACC(N=76) | 0.59 | - | -- I | 1.05(0.87,1.27) |
| TCGA-ESCA(N=173) | 0.69 | F. | ʻ | 1.05(0.83,1.34) |
| TCGA-READ(N=88) | 0.69 | I- | + " -I | 1.13(0.63,2.01) |
| TCGA-STAD(N=375) | 0.77 | 1.02(0.88,1.19) | ||
| TCGA-SKCM-P(N=96) | 0.78 | ュ | 1.03(0.84,1.26) | |
| TCGA-UCS(N=55) | 0.92 | 1- | -| | 1.01(0.76,1.35) |
| TCGA-DLBC(N=43) | 0.27 I- | . | . -I | 0.67(0.32,1.38) |
| TCGA-OV(N=407) | 0.29 | F- | :4 | 0.95(0.87,1.04) |
| TCGA-UCEC(N=166) | 0.47 | . | 0.92(0.73,1.16) |
-1.6-1.4-1.2-1.0-0.8-0.6-0.4-0.20.0 0.2 0.4 0.6 0.8 1.0 log2(Hazard Ratio(95%CI))
Fig. 2. 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.
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 sig- nificant 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).
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 rela- tionship with vascular endothelial growth factor A (VEGFA) in 35 tu- mors, with CD276 in 36 tumors, with transforming growth factor ß1 (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 corre- lated 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
A
Type
B
VEGFA
CD276
correlation coefficient
GBM
density C
spearman correlation R=0.094
OV
0.3-
spearman correlation
LUSC.
0.2-
spearman correlation
LUAD
densit
density
53
TGFBI
R=0.175
densit
spearman correlation R =- 0.105 P=0.169
C10orf54
0.0-
P=0.256
R=0.085
0.0-
P=0.024
1AVCR2
LIO
-1.0-0.5
0.0
0.5
1.0
P=0.247
0.0-
:
20274
pValue
log2(Ncoantigen count)
8
log2(Neoantigen count)
28
log2(Neoantigen count)
log2(Neoantigen count)
VEĞFB
10.0
10.0
ADORA2A
IDOI
0.0
0.5
1.0
6
PDCDI
6
7.5
7.5
LAG3
Type:
SLAMF7 CTLA4
Inhibitory
4
4
Stimulaotry
5.0
5.0
TIGIT
ARGI
4
DNRB
M
VICNI
010305
2
00 02 04
0.00.10.2
000204
BILA
BRCA
log2(SLC16A3 TPM+1)
density
KIRC
log2(SLC16A3 TPM+1) density
KIRP
log2(SLC16A3 TPM+1) density
log2(SLC16A3 TPM+1) density
density
spearman correlation R=0.178
density
density
UCEC
*
HA
density
IL12A
spearman correlation
02-
spearman correlation R =- 0.019
02-
spearman correlation R=0.22
KIRŽDLI
KIR2DL3 CIKZDL3
W
P=1.26e-06
0.2-
R=0.058
0.0-
P=Q.191
0.1-
0.0-
P=0.808
0.1=
0.0-
P=0.000643
CAMI
1GB2
INFRSF14
log2(Necantigen count)
·
log2(Neoantigen count)
log2(Necantigen count)
log2(Neoantigen count)
7.5
7.5
&
UNFERSF4
INERSF9
CD80
10
IL2RA
5.0
5.0
6
·
D40
NTPDI
2.5
4
TLR4
25
$
·
PNE
0.0
:
XCL9
U
0.00.10.20.3
5.0
log2(SLC16A3 TPM+1)
2.5
0.00.10.20.3
density
log2(SLC16A3 TPM+1)
density
log2(SLC16A3 TPM+1)
25
5.0
7.5
0.0.0.2.3.4
IFNG
density
COAD
log2(SLC16A3 TPM+1)
0.0000000620
density
CXCLIO
PRFI
density
0,4 -
spearman correlation
density
READ
Pagarman correlation
STAD
spearman correlation
density
HNSC
spearman correlation R=0.037 P=Q.543
GIZMA
0.2-
R=0.096
R=0.196
density
0.3-
R=0.23
0.4-
CCL2
CD70
19-9
P=0.341
P=0.156
0.0
250.000343
0.0
INFRSF18
TNFSF9 ILIA
log2(Neoantigen count)
log2(Neoantigen count)
log2(Neoantigen count)
12.5
log2(Neoantigen count)
10.0
9
LIB
IENA
IENAZ
10
10.0
HMGB
7.5
·
7.5
6
MACHT
ASCLI
5.0
COSLG
5
5.0
3
IL2
SELP
2.5
0.00 0.10’
2.5
CD27
0.00.10.2
D40LG
log2(SLC16A3 TPM+1) density
log2(SLC16A3 TPM+1) density
log2(SLC16A3 TPM+1)
0.00.10.2
density
log2(SLC16A3 TPM+1)
0.0 0.2
CD28
density
COS
LIHC
density
SKCM
THCA
spearman correlation
R =- 0.086 P=0.234
density
CESC
02-
Rearman correlation
density
spearman correlation
0.1=
R=0.086
R =- 0.14
density
spearman correlation R=0.052 R=0.358
1
0.0-
=0.398
P=0.0529
12.5
12.5
A
C
log2(Neoantigen count)
log2(Nenantigen count)
log2(Neoantigen count)
log2(Neoantigen count)
M
10.0
10.0
9
6
7.5
7.5
6
4
2
5.0
5.0
3
TCGA-GBMLGG(N-
56)
TCGA-LGG(N=304)
TCGA-PRAD(N=495)
·
0,00.10.20.3
0.00.10.20.3
0.00.10.20.3 density
0
2,000
r=0.78
2,000
r=0.80
2,000
r=0.59
log2(SLC16A3 TPM+1)
BLCA
density
log2(SLC16A3 TPM+1) density
log2(SLC16A3 TPM+1)
log2(SLC16A3 TPM+1)
0,00 10 20,3
density
p=1.2e-134
p=8.3e-116
p=3.2e-47.
density
PRAD 0.4.
02-
spearman correlation R =- 0.092
density
spearman correlation R =- 0.118
LĢĢ
ImmuneScore
density
spearman correlation R=0.026 P=0.72
1,000
ImmuneScore
1,000
ImmuneScore
1,000
0.1-
P=0.286
P=0.0593
log2(Neoantigen count)
log2(Neoantigen count)
log2(Neoantigen count)
0-
0
0
9
9
9
·
7
6
6
-1,000-
-1,000-
-1,000
5
3
3
-2,000
-2,000
-2,000
3
-
Q
log2(SLC16A3 TPM+1)
0.00.10.20.3
0.0 0.2 0.4
density
log2(SLC16A3 TPM+1)
log2(SLC16A3 TPM+1)
00 02 04
1
density
density
0
2
4
6
0
2
4
6
0
2
4
6
D
SLC16A3 Expression
SLC16A3 Expression
SLC16A3 Expression
E
2,000
TCGA-GBMLGG(N=656)
2,000
TCGA-LGG(N=504)
2,000
TOGA-KIPAN(N=878)
TCGA-LGG(N=504)
TCGA-LUAD(N=500)
TOGA-KIPAN(N=878)
r=0.76
r=0.74
r=0.56
p=2.0e-125
p=4.5e-90
p=1.4e-72.
4,000
r=0.80
4,000
r=0.14
4,000
r=0.57
1,000-
1,000
1,000
p=2.2e-115
p=2.4e-3
p=2.7e-75
StromalScore
ESTIMATEScore
2,000
ESTIMATEScore
2,000
ESTIMATEScore
2,000
0-
StromalScore
0
StromalScore
0
0
0
0
-1,000
-1,000
-1,000
-2,000
-2,000
-2,000
-2,000-
-2,000 -
-2,000
-4,000
-4,000
-4,000
-3,000
-3,000
-3,000
T
1
1
1
1
0
2
4
6
8
10
0
2
4
6
8
10
0
2
4
6
8
10
0
2
4
6
8
10
0
2
4
6
8
10
0
2
4
6
8
10
SLC16A3 Expression
SLC16A3 Expression
SLC16A3 Expression
SLC16A3 Expression
SLC16A3 Expression
SLC16A3 Expression
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 posi- tive 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
correlation coefficient
-0.5
0.0
0.5
pValue
0.0 0.5 1.0 1.5 2.0
0.52
0.74
0.74
0.65
0.79
0.40
0.55
0.24
0.61
0.53
0.64
0.27
0.40
0.28
0.62
0.51
0.57
0.29
0.24
0.14
0.30
0.34
**
0.30
0.37
0.13
0.58
0.48
0.54
*
0.30
0.46
0.34
0.27
0.17
0.83
*
0.18
0.25
0.59
0.61
0.12
0.23
0.22
0.11
0.24
-0.23
0.21
**
**
0.15
0.28
0.12
0.39
0.19
0.41
**
*
**
-0.10
-0.15
0.17
*
-0.20
0.23
*
**
0.11
0.44
0.50
0.39
0.58
*
0.10
0.12
0.35
0.46
0.17
0.48
*
*
0.38
0.48
0.18
0.50
**
0.18
0.36
0.15
0.35
0.32
0.50
-0.34
0.47
*
*
0.11
0.17
0.10
0.24
*
**
0.15
0.31
0.41
0.16
0.49
**
**
-0.17
0.16
**
0.27
0.26
**
**
0.44
0.51
0.47
0.21
0.43
0.47
*
0.19
0.24
0.30
**
0.15
0.22
0.23
0.30
**
0.37
**
0.24
0.43
*
0.33
0.27
0.15
0.18
0.18
0.27
-0.25
*
*
-0.24
0.32
*
0.22
0.31
0.32
**
0.34
0.51
0.47
0.48
0.55
0.13
0.21
0.23
0.30
*
0.28
0.25
0.24
0.34
*
*
**
**
0.11
0.29
0.20
0.32
B cell
T cell CD4
T cell CD8
Neutrophil
Macrophage
DC
TCGA-LGG(N=504)
TCGA-PRAD(N=495)
TCGA-GBMLGG(N=656)
TCGA-KIRC(N=528)
TCGA-LIHC(N=363)
TCGA-PCPG(N=177)
TCGA-THCA(N=503)
TCGA-KIPAN(N=878)
TCGA-ESCA(N=181)
TCGA-KIRP(N=285)
TCGA-STES(N=569)
TCGA-TGCT(N=132)
TCGA-OV(N=417)
TCGA-COADREAD(N=373)
TCGA-COAD(N=282)
TCGA-LUAD(N=500)
TCGA-DLBC(N=46)
TCGA-CESC(N=291)
TCGA-LUSC(N=491)
TCGA-BLCA(N=405)
TCGA-STAD(N=388)
TCGA-SKCM-P(N=101)
TCGA-KICH(N=65)
TCGA-READ(N=91)
TCGA-SARC(N=258)
TCGA-SKCM(N=452)
TCGA-UCS(N=56)
TCGA-MESO(N=85)
TCGA-PAAD(N=177)
TCGA-HNSC(N=517)
TCGA-UVM(N=79)
TCGA-THYM(N=118)
TCGA-UCEC(N=178)
TCGA-GBM(N=152)
TCGA-CHOL(N=36)
TCGA-SKCM-M(N=351)
TCGA-ACC(N=77)
TCGA-BRCA(N=1077)
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 neu- trophils 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 adenocar- cinoma (STES) (Fig. 4).
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).
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
A
TMB
B
MSI
LAML(N=126)
SampleSize
GBMLGG(N=657)
SampleSize
ACC(N-77)
CESC(N=286)
KIPAN(N=688)
BLCA(N=407)
200
ACC(N=77
CHOL(N-36
200
LIHC(N-357
400
KICH(N=66)
THCA(N=489)
OV(N=303)
400
KIRC(N=334)
LUSC(N=486)
600
DLBC(N=47)
LGG(N-506)
600
KICH(N=66)
KIRP(N=285)
PRAD(N=492)
800
PAAD(N=176)
800
PCPG(N=177)
L
KIRC(N-337)
PAAD(N-171)
LUAD(N=511)
= 1,000
READ(N=90)
PRAD(N=495)
TGCT(N=143)
KIRP(N=279)
pValue
CESC(N=302)
THCA(N=493
pValue
CHOL(N=36)
0.0
GBM(N=151)
0.0
HNSC(N=498)
-0.2
BLCA(N=407
GBM(N=149
PCPG(N-177)
0.2
OV(N-303)
BRCA(N=981)
0.4
LIHC(N=367
LUSC(N=490)
0.4
KIPAN(N=679)
UVM(N-79)
-0.6
BRCA(N=1039)
HNSC(N=500
-0.6
LGG(N=501)
GBMLGG(N=650)
0.8
SKCM(N=102)
TGCT(N=148)
0.8
SKCM(N=102)
MESO(N=83)
MESO(N-82)
1.0
UCEC(N=180)
1.0
COADREAD(N=372)
STES(N=592
UCEC(N=175)
THYM(N=118)
ESCA(N=180)
ESCA(N=180)
DLBC(N=37)
STAD(N=412)
COAD(N=282)
SARC(N=252
SARC(N-234)
READ(N-89)
STES(N=589)
UCS(N=57)
LUAD(N=509)
LAML(N=129)
STAD(N=409)
UVM(N-79)
THYM(N=118)
COADREAD(N=374)
UCS(N=57)
COAD(N=285)
-0.2
0.0
0.2
0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Correlation coefficient(pearson)
Correlation coefficient(pearson)
C
DNAss
D
RNAss
TGCT(N=147)
SampleSize
UCEC(N=173)
GBMLGG(N=659)
SampleSize
PCPG(N-176)
100
LGG(N=507)
KIPAN(N-860)
CESC(N=301)
200
GBM(N=152)
200
READ(N=87
LIHC(N-366)
300
THYM(N=119)
400
PRAD(N-491)
400
BLCA(N=403)
DLBC(N=47
PCPG(N=176)
500
LAML(N=167)
600
COADREAD(N=358)
LAML(N=170)
600
KICH(N-65
800
COAD(N=271)
L
700
THCA(N=499
KIRC(N=512)
-1,000
LUSC(N=361)
ESCA(N=179)
KIRP(N=283)
DLBC(N=47)
LUAD(N=451)
GBM(N=51)
pValue
LUSC(N=483)
pValue
KIRC(N=309)
0.0
CHOL(N=36)
OV(N=298)
0.0
KICH(N=65)
KIPAN(N=642)
0.2
COAD(N=281)
CESC(N=301
0.2
STES(N=548)
STAD(N=369)
-0.4
COADREAD(N=369)
BLCA(N=403)
-0.4
HNSC(N=512)
PAAD(N=156)
-0.6
HNSC(N=512
ESCA(N=179)
-0.6
UCS(N=57)
BRCA(N=774)
0.8
PAAD(N=156)
READ(N=88)
0.8
SKCM(N=102)
ACC(N=76)
1.0
LIHC(N=366)
MESO(N=87)
LUAD(N=507)
-1.0
SARC(N=253)
KIRP(N=268)
SARC(N=253)
UVM(N=79
PRAD(N=491)
BRCA(N=1080)
ACC(N=76)
UVM(N=79)
THYM(N=119)
STES(N=578)
STAD(N=399)
THCA(N=499)
CHOL(N=36)
MESO(N=87
UCEC(N=177)
LGG(N-507
GBMLGG(N=558)
SKCM(N-102)
UCS(N=57)
OV(N=9)
TGCT(N=147)
-0.4
-0.2
0.0
0.2
0.4
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
Correlation coefficient(pearson)
Correlation coefficient(pearson)
SLC17A5
A
B
CBLL1
0.6
Enrichment score
0.4
SLC16A7
0.2
LDHA
0.0
GALACTOSE METABOLISM(ES=0.5915,NP=0.0000)
PENTOSE PHOSPHATE PATHWAY(ES-0.6349,NP-0.0019)
SLC16A3
PATHOGENIC ESCHERICHIA COLI INFECTION(ES=0.4966,NP=0.0038)
AMINO SUGAR AND NUCLEOTIDE SUGAR METABOLISM(ES-0.4847,NP+0.0131)
GLYCOSAMINOGLYCAN BIOSYNTHESIS CHONDROITIN SULFATE(ES=0.5692,NP-0.0315)
SLC2A3
PROTEASOME(ES-0.6595,NP-0.0493)
FRUCTOSE AND MANNOSE METABOLISM(ES=0.5015,NP=0.0152)
EMB
1.0
H
SLC2A1
-
Ranked list metric
0.5
BSG
0.0
CA9
-0.5
L
0
5,000
10,000
15,000
CD44
Rank in Ordered DataSet
C
30
MutCount
Missense_Mutation
MutCount
20
Nonsense_Mutation
10
0
Frame_Shift_Del
SampleGroup
0
200
400
Splice_Site
TP53(2.5c-6)
56.0%
Frame_Shift_Ins
Translation_Start_Site
TTN(9.7e-3)
53.2%
In_Frame_Ins
MUC16(0.02)
In_Frame_Del
46.5%
LRPIB(1.7e-3)
37.5%
SampleGroup:
HighExp
ZFHX4(0.03)
35.4%
LowExp
PTPRD(8.3e-3)
19.2%
RPIL1(0.01)
19.0%
SI(2.3e-3)
18.5%
STK11(0.03)
18.3%
LRRC7(9.0e-3)
17.6%
ERICH3(5.9e-3)
17.4%
ASTN1(2.4e-3)
16.9%
FAM135B(0.01)
16.4%
NALCN(8.7e-3)
15.5%
PEG3(0.04)
14.8%
the plasma membrane, L-ascorbic acid metabolic process, and dendrite self-avoidance (Table 1).
| 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 |
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.
4. Discussion
Significant advancements in immunotherapy have been achieved for
A
B
C
D
E
F
G
H
treating cancer [28]. The heterogeneous nature of tumor patients [29], however, contributes to an overall unfavorable prognosis for most pa- tients, thereby necessitating the prompt development of precise targeted therapy or multimodal treatment strategies. In tumor progression, can- cer cells undergo metabolic reprogramming to adopt a “glyco- lysis-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 immunosup- pressive TMEs, thereby aiding immune evasion and conferring
resistance to the diverse forms of cancer treatment [32-34]. This study highlights the novel role of SLC16A3 across 30 cancer types [35-37], revealing its significant association with OS and DSS in 12 cancers, with high expression generally predicting poor prognosis. Notably, we iden- tified 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. Innova- tively, we expanded on previous findings by demonstrating the exten- sive links between SLC16A3 and immune regulatory factors, such as PD-L1, VEGFA, TGFB1, ICAM1, and PDCD1, shedding light on
mechanisms of immune therapy resistance [36-38]. 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 resis- tance [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 path- ways, 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 [45-51].
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 dehy- drogenase (PDH) and enforce glycolysis [53,54]; Intracellular lactate accumulation activates HIF-1« feedforward loops and epigenetic remodeling via histone lactylation (H3K18la) [55]-mediated M2 polar- ization (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 synergisti- cally 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 pheno- type, 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 identifica- tion 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 neo- antigen burden, suggesting concurrent immunosuppression and antige- nicity. 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 com- bination to enhance vascular normalization.
Immune cells in both innate (such as monocytes, neutrophils, mac- rophages, 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 associ- ated 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 im- mune cell subtypes such as B cells, CD4+ T cells, macrophages, neu- trophils, 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 pre- dict 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 ef- ficacy across various tumor types [76]. The current investigation revealed that SLC16A3 expression was significantly positively associ- ated 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 in- vestigations 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 trun- cating mutations like R302 compromise protein stability, disrupting metabolic reprogramming [80]. These functional defects likely exacer- bate tumor microenvironment acidification via impaired lactate ho- meostasis, 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. Sur- vival 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 rele- vance. 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 mecha- nisms 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, fac- tors 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 investi- gative 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 consid- ered, 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, Supervi- sion, 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://commo nfund.nih.gov/GTEx/) and the TCGA database (https://portal.gdc. cancer.gov). Protein expression profiles obtained from the Human Pro- tein 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 ß1 |
| 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 |
Data availability
The raw data analyzed in this study are freely available to the public without any restrictions.
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