Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 29;25(1):168.
doi: 10.1186/s12935-025-03791-1.

Comprehensive pan-cancer analysis identified SLC16A3 as a potential prognostic and diagnostic biomarker

Ping Yang  1 , Jiayu Yin #  2 , Gongyin Zhang #  3 , Xiaofeng Li  4 , Tongtong Chen  1 , Wanying Zhao  1 , Jinhai Tang  1 , Li Lv  5 , Xiupeng Lv  6
Affiliations

Comprehensive pan-cancer analysis identified SLC16A3 as a potential prognostic and diagnostic biomarker

Ping Yang et al. Cancer Cell Int. .

Abstract

SLC16A3, belonging to the SLC16 gene family, is involved in the transportation of monocarboxylate. SLC16A family members play important roles in tumorigenesis, nonetheless, the specific involvement of SLC16A3 in tumor prognosis and diagnosis in human cancers remains unelucidated. This study dealt with the exploration of SLC16A3 expression in human pan-cancer and its significance regarding disease prognosis. For this investigation, the mRNA expression data of SLC16A3 were acquired from the TCGA and the GTEx datasets. The Kaplan-Meier plots, univariate Cox regression, and the ROC curve were employed for assessing the prognostic and diagnostic significance of SLC16A3 in pan-cancer. Furthermore, the cBioPortal database was used to analyze the SLC16A3 genomic alterations. Moreover, the association of the infiltration of immune cells and immune checkpoint genes with SLC16A3 was analyzed by the TIMER database. Gene Ontology and KEGG pathway analysis were employed to explore the function of SLC16A3 in pan-cancer. The resulting data demonstrated that SLC16A3 mRNA expression was overexpressed in most cancers and its protein expression was also high across diverse cancer types. Moreover, upregulated SLC16A3 expression was linked to poor OS and PFI of certain cancers. Cox regression analysis further indicated that SLC16A3 is a risk factor for patients with PAAD, CESC, LUSC, LUAD, CHOL, LGG, MESO, and OSCC. The ROC curve revealed that SLC16A3 exhibited a high accuracy (AUC > 0.9) in BRCA, CHOL, ESCA, GBM, and KIRC prediction. Moreover, the acquired data indicated that in pan-cancer, the SLC16A3 expression exhibited correlations with immune checkpoint genes and immune cells. These findings collectively suggest that SLC16A3 holds promise as a biomarker for diagnostic and prognostic purposes in pan-cancer.

Keywords: Immune infiltration; Pan-cancer; Prognosis; SLC16A3; Survival.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethical approval: The tissue microarrays used in this study were approved by the Ethics Committee of Shanghai Outdo Biotech Company. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Pan-cancer analysis of SLC16A3 expression. (A) SLC16A3 mRNA expression in TCGA cancers and GTEx normal tissues. (B) SLC16A3 mRNA expression in human cancers from the TCGA database analyzed by the TIMER2.0 database. (C) SLC16A3 mRNA expression level in paired tumor samples based on TCGA database. (*P < 0.05, **P < 0.01, ***P < 0.001)
Fig. 2
Fig. 2
Immunohistochemistry (IHC) staining of SLC16A3 in human cancers. (A) THCA, (B) BRCA, (C) LUAD, (D) LUSC, (E) ESCA, (F) LIHC, (G) PAAD, and (H) COAD. Representative images of SLC16A3 expression in pan-cancer tissues are shown. Original magnification, ×40 and ×400
Fig. 3
Fig. 3
SLC16A3 protein expression analysis
Fig. 4
Fig. 4
Patient overall survival analysis. (A-L) Kaplan–Meier analysis of the association between SLC16A3 expression and OS in muti-tumor types from TCGA database
Fig. 5
Fig. 5
Kaplan-Meier overall survival curves of SLC16A3 in human cancers based on the GEPIA database. The median value of SLC16A3 is the cut-off value. (A) BLCA; (B) CESC; (C) LGG; (D) LIHC; (E) LUAD; (F) MESO; (G) PAAD; (H) UCS
Fig. 6
Fig. 6
Univariate Cox regression analysis of SLC16A3. Forest map shows the univariate cox regression results of CD161 for OS (A) and PFI (B) in TCGA pan-cancer
Fig. 7
Fig. 7
ROC curve for SLC16A3 expression in pan-cancer. (A) BLCA; (B) BRCA; (C) CHOL; (D) ESCA; (E) GBM; (F) HNSC; (G) KICH; (H) KIRC; (I) KIRP; (J) LIHC; (K) LUAD; (L) LUSC; (M) OSCC; (N) STAD; (O) UCEC; (P) UCS
Fig. 8
Fig. 8
Genetic Alteration Analysis. (A) Genetic Alteration frequency of SLC16A3 in human pan-cancer. (B) The mutation types, number, and sites of the SLC16A3 genetic alterations. (C) The copy-number alterations of SLC16A3 in pan-cancer. (D) Frequency of related-gene alterations in SLC16A3-altered and unaltered groups
Fig. 9
Fig. 9
SLC16A3 expression and tumor immune microenvironment analysis. Correlation of SLC16A3 expression with immune cells (A) and immune checkpoint genes (B)
Fig. 10
Fig. 10
Correlation of SLC16A3 expression with pan-cancer immune subtypes. (A) in BLCA, (B) in BRCA, (C) in HNSC, (D) in KIRC, (E) in LGG, (F) in LUAD, (G) in LUSC, (H) in OV, (I) in PRAD, (J) in SKCM, (K) in STAD, (L) in UCEC
See this image and copyright information in PMC

References

    1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660. - PubMed
    1. Ebell MH, Culp MB, Radke TJ. A systematic review of symptoms for the diagnosis of ovarian Cancer. Am J Prev Med. 2016;50(3):384–94. 10.1016/j.amepre.2015.09.023. - PubMed
    1. Cáceres-Lavernia HH, Nenínger-Vinageras E, Varona-Rodríguez LM, et al. Racotumomab in Non-Small cell lung Cancer as maintenance and Second-Line treatment. MEDICC Rev. 2021;23(3–4):21–8. 10.37757/MR2021.V23.N3.5. - PubMed
    1. Miao R, Luo H, Zhou H, et al. Identification of prognostic biomarkers in hepatitis B virus-related hepatocellular carcinoma and stratification by integrative multi-omics analysis. J Hepatol. 2014;61(4):840–9. 10.1016/j.jhep.2014.05.025. - PubMed
    1. Ali SR, Jordan M, Nagarajan P, Amit M. Nerve Density and Neuronal Biomarkers in Cancer. Cancers (Basel).2022;14(19):4817. Published 2022 Oct 1. 10.3390/cancers14194817 - PMC - PubMed

LinkOut - more resources