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Systematic pan-cancer analysis identifies SLC31A1 as a biomarker in multiple tumor types

BMC Medical Genomics volume 16, Article number: 61 (2023) Cite this article

Abstract

Background

Solute Carrier Family 31 Member 1 (SLC31A1) has recently been identified as a cuproptosis-regulatory gene. Recent studies have indicated that SLC31A1 may play a role in colorectal and lung cancer tumorigenesis. However, the role of SLC31A1 and its cuproptosis-regulatory functions in multiple tumor types remains to be further elucidated.

Methods

Online websites and datasets such as HPA, TIMER2, GEPIA, OncoVar, and cProSite were used to extract data on SLC31A1 in multiple cancers. DAVID and BioGRID were used to conduct functional analysis and construct the protein–protein interaction (PPI) network, respectively. The protein expression data of SLC31A1 was obtained from the cProSite database.

Results

The Cancer Genome Atlas (TCGA) datasets showed increased SLC31A1 expression in tumor tissues compared with non-tumor tissues in most tumor types. In patients with tumor types including adrenocortical carcinoma, low-grade glioma, or mesothelioma, higher SLC31A1 expression was associated with shorter overall survival and disease-free survival. S105Y was the most prevalent point mutation in SLC31A1 in TCGA pan-cancer datasets. Moreover, SLC31A1 expression was positively correlated with the infiltration of immune cells such as macrophages and neutrophils in tumor tissues in several tumor types. Functional enrichment analysis showed that SLC31A1 co-expressed genes were involved in protein binding, integral components of the membrane, metabolic pathways, protein processing, and endoplasmic reticulum. Copper Chaperone For Superoxide Dismutase, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha and Solute Carrier Family 31 Member 2 were copper homeostasis-regulated genes shown in the PPI network, and their expression was positively correlated with SLC31A1. Analysis showed there was a correlation between SLC31A1 protein and mRNA in various tumors.

Conclusions

These findings demonstrated that SLC31A1 is associated with multiple tumor types and disease prognosis. SLC31A1 may be a potential key biomarker and therapeutic target in cancers.

Peer Review reports

Introduction

Cancer is the leading cause of mortality worldwide, imposing substantial healthcare and socio-economic burden [1]. The treatment strategies for cancer mainly include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy [2,3,4,5]. Despite drug resistance, side effects, and other unelucidated issues, the prognosis and survival rate remain unsatisfactory [6]. Recently large-scale and multi-omic pan-cancer studies and databases, such as the Cancer Genome Atlas (TCGA), have made it possible to investigate both the common features and heterogeneities across various human tumors [7,8,9,10,11,12].

Solute Carrier Family 31 Member 1 (SLC31A1), also known as copper (Cu) transporter 1 (CTR1), is considered a key component in cellular Cu uptake in mammalian cells and tissues [13]. Moreover, SLC31A1 was recently identified as a cuproptosis-regulatory gene, and a high SLC31A1 expression level can cause Cu-induced cell death [14]. In addition, SLC31A1 transports platinum drugs across the plasma membrane, and in patients with non-small cell lung cancer, SLC31A1 is a potential pharmacogenetic biomarker for clinical outcomes [15, 16]. To date, however, there is no comprehensive pan-cancer study of the function and clinical significance of SLC31A1.

In our current study, we systematically described the mRNA and protein expression levels, prognostic value, genetic alterations, molecular function of SLC31A1 in several tumor types as well as the association with immune infiltration. Our findings reveal that SLC31A1 could be a potential biomarker and novel therapeutic target  of multiple tumors.

Materials and methods

Expression analysis

Expression data of SLC31A1 mRNA was obtained from the Human Protein Atlas (HPA) database (version: 21.1) (https://www.proteinatlas.org) [17]. In multiple tumor types, the “Gene DE” module of Tumor Immune Estimation Resource version 2 (TIMER2) (http://timer.cistrome.org/) was used to investigate SLC31A1 expression levels in tumors and non-tumor tissues [18,19,20]. The protein expression level of SLC31A1 was obtained from HPA.

Prognostic analysis

Kaplan–Meier (K–M) survival analysis of SLC31A1 for overall survival (OS) and disease-free survival (DFS) was conducted using the Gene Expression Profiling Interactive Analysis version 2 (GEPIA2) (http://gepia2.cancer-pku.cn/) database [21].

Genetic mutations analysis

We analyzed the characteristics of SLC31A1 genetic alterations in cBioPortal (v4.1.9) (https://www.cbioportal.org/) [22, 23]. In the "Cancer Types Summary" module, we calculated the frequency of SLC31A1 gene alterations based on TCGA Pan-Cancer Atlas Studies datasets. The "Mutations" module was used to generate a mutation site plot of SLC31A1. Then to confirm the driver mutations in SLC31A1, the platform OncoVar (https://oncovar.org/) was used [24]. And the database the Catalogue of Somatic Mutations in Cancer (COSMIC) (https://cancer.sanger.ac.uk) was used to annotate SLC31A1 somatic mutations [25]. Driver mutations were defined as somatic missense mutations with AI-Driver score ≥ 0.95 and occurred in at least two patients. The International Cancer Genome Consortium (ICGC) (https://dcc.icgc.org/) database was used to confirm the mutation site of SLC31A1 and explore the cancer distribution of SLC31A1 [26].

Immune infiltration evaluation

The "Immune" module of TIMER2 was used to analyze the correlation between SLC31A1 expression and 21 immune infiltrations, including B cells, cancer associate fibroblast, common lymphoid progenitor, common myeloid progenitor, DC, endothelial cells, eosinophil, granulocyte-monocyte progenitor, hematopoietic stem cells, macrophage, mast cells, monocyte, myeloid-derived suppressor cells, neutrophil, NK cells, CD4 + T cells, CD8 + T cells, T cell follicular helper, T cell gamma delta, NK T cells, and Tregs, Several immune deconvolution algorithms were applied, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, quanTIseq, and CIBERSORT-ABS. Consistent significant findings (P < 0.05) by all available algorithms were required to support an accurate correlation with immune infiltrations.

Gene enrichment analysis and protein interaction network construction

A list of the top 100 genes correlated with SLC31A1 that had similar expression patterns ranked by Pearson correlation coefficient was obtained from TCGA datasets using the GEPIA2 "Similar Gene Detection" module. In the meantime, Gene Ontology pathway enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were retrieved from the Database for Annotation, Visualization, and Integrated Discovery (http://david.abcc.ncifcrf.gov/) [27,28,29,30]. With multiple test correlations, FDR < 0.05 were set as the significance threshold. In addition, pairwise gene correlation analysis was performed using the GEPIA2 “Correlation Analysis” module for all tumor tissues in TCGA. SLC31A1-interactive protein networks were constructed with the "Network" module of BioGRID (version. 4.4.216) (https://thebiogrid.org/) [31].

Relative protein abundance analysis of SLC31A1

The expression data of the relative protein abundance of SLC31A1 was downloaded from Cancer Proteogenomic Data Analysis Site (cProSite) database (https://cprosite.ccr.cancer.gov/). And the correlation of SLC31A1 between relative abundance and mRNA was calculated using the cProSite website.

Statistical analysis

The statistical analysis was automatically computed based on the above online databases. Student's t-test implemented by GraphPad Prism (Version 9.1.1) was used to compare protein expression between tumor tissues and adjacent normal tissues.

Results

SLC31A1 expression in various tissues and tumors

Based on datasets from the HPA, GTEx, and FANTOM5 (function annotation of the mammalian genome), SLC31A1 was found to be widely expressed in many tissues, including the liver, gallbladder, the gastrointestinal tract (such as the small intestine and duodenum) (Fig. 1a; Additional file 1: Figures S1a, b, and c). The protein expression profile in Fig. 1b showed that SLC31A1 had a higher expression in the hippocampus, lung, endometrium, and kidney, and a lower expression in the esophagus, prostate, and skin. Additionally, single-cell RNA-seq analysis revealed high expression of SLC31A1 in prostatic glandular cells, serous glandular cells, and hepatocytes (Additional file 1: Fig. S1d). It was also noted that SLC31A1 was also highly expressed in macrophages.

Fig. 1
figure 1

The expression of SLC31A1 in normal tissues and different tumors. a Consensus SLC31A1 tissue expression based on the consensus dataset in HPA. The X-axis shows tissue or organ types. The Y-axis shows the SLC31A1 mRNA expression level. nTPM, normalized transcripts per million. b The protein expression stare of SLC31A1 in different tissues. The X-axis shows tissues or organ types. The Y-axis shows the SLC31A1 protein expression level. c The expression status of SLC31A1 in different tumor types is visualized by TIMER2. The Y-axis shows the SLC31A1 expression level in Log2 (TPM + 1). d–f The correlation between the relative abundance of protein and mRNA of SLC31A1 in liver cancer, lung squamous cell carcinoma, and ovarian cancer, respectively. g, h Comparison of SLC31A1 protein expression in tumor tissues and adjacent normal tissues from patients with liver cancer and stomach cancer. *P < 0.05; **P < 0.01; ***P < 0.001; ****, P < 0.0001

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We further examined the expression pattern of SLC31A1 in tumor tissues. In comparison to corresponding normal tissues, the expression of SLC31A1 mRNA was increased in most tumor tissues (Fig. 1c). Tumor tissues of breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA), pheochromocytoma, paraganglioma (PCPG), glioblastoma multiforme (GBM), stomach adenocarcinoma (STAD), and uterine corpus endometrioid carcinoma (UCEC) had higher SLC31A1 expression levels when compared to corresponding normal tissues (all P < 0.01). In contrast, decreased SLC31A1 mRNA expression levels were observed in cholangiocarcinoma (CHOL), kidney chromophobe (KIRC), kidney renal clear cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), thyroid carcinoma (THCA) tumor tissues (P < 0.001). No significant change in SLC31A1 expression was found in some tumor types such as pancreatic adenocarcinoma (PAAD) and uterine carcinosarcoma (UCS).

We then evaluated the possible impact of altered mRNA expression on the SLC31A1 protein. Figure 1d–f with relative protein abundance data from cProSite showed a moderate positive correlation between expression levels of SLC31A1 protein and mRNA in liver cancer, lung squamous cell carcinoma, and ovarian cancer. In addition, the relative abundance of SLC31A1 protein in liver cancer and stomach cancer showed a significant difference between tumor tissues and adjacent normal tissues (P < 0.0001) (Fig. 1g, h), in line with the mRNA expression difference. These results further confirmed that abnormal SLC31A1 expression might be involved in multiple cancers.

Association of SLC31A1 expression with cancer prognosis

Based on TCGA datasets, GEPIA2 was used to investigate the correlation between SLC31A1 expression and prognosis in different tumor types. Worse OS was found to be associated with higher SLC31A1 expression in adrenocortical carcinoma (ACC) (P = 0.0012), BRCA (P = 0.0027), mesothelioma (MESO) (P = 1.8 × 10–5), Skin cutaneous melanoma (SKCM) (P = 0.027), LGG (P = 0.00012), Testicular germ cell tumors (TGCT) (P = 0.05), and Thymoma (THYM) (P = 0.038), and associated with lower SLC31A1 expression in KIRC (P = 3.5 × 10–5) in 5 years (Fig. 2). Additionally, DFS analysis showed that patients with ACC (P = 7 × 10–4), LGG (P = 0.032), and MESO (P = 0.044) had worse outcomes if their SLC31A1 levels were higher, while patients with KIRC (P = 6.7 × 10–6) and STAD (P = 0.02) in 5 years had lower levels (Fig. 3). According to the results, abnormal SLC31A1 expression was associated with poor prognosis in several tumor types.

Fig. 2
figure 2

Effects of SLC31A1 expression on overall survival in different TCGA tumor types. a An overview survival map and overall survival analyses b–i are derived from GEPIA2. Kaplan–Meier plots of cancer types with significant p-values are shown. The 95% confidence intervals of overall survival are indicated by red and blue dotted lines for high and low SLC31A1 expression groups, respectively. The color of the squares indicates the value of hazzard ratio (HR). Squares with bold outlines in the survival map denote p(HR) < 0.05

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Fig. 3
figure 3

Effects of SLC31A1 expression on disease-free survival in different TCGA tumor types. GEPIA2 is used to build a survival map (a) and conduct disease-free survival b–f analyses. Kaplan–Meier plots of cancer types with significant results are displayed. The 95% confidence intervals of disease-free survival are indicated by red and blue lines for the high and low SLC31A1 groups, respectively. The color of the squares indicates the value of the hazzard ratio (HR). Squares with bold outlines in the survival map denote p(HR) < 0.05

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Furthermore, we used GEPIA2 to examine the association between SLC31A1 expression and pathological stages of tumors and found a significant difference of SLC31A1 expression among pathological stages of ACC, KIRC, and MESO (all P < 0.05) (Additional file 2: Figure S2).

SLC31A1 genetic alterations in tumors

CBioPortal was then utilized to examine SLC31A1 gene alterations in TCGA datasets of various tumor types. It was found that tumor samples from UCEC had the highest SLC31A1 genetic alternation frequency (2.46%). In ACC tumor samples, all SLC31A1 mutations were copy number amplified (Fig. 4a; Additional file 4: Table S1), which in all tumor samples from TCGA were the most common genetic alterations  in SLC31A1. Besides UCEC and ACC, genetic alteration of SLC31A1 was observed in more than 1% of Bladder Urothelial Carcinoma (BLCA), Prostate, Adenocarcinoma, Sarcoma, and Kidney Renal Papillary Cell Carcinoma. As shown in Fig. 4b, a total of 27 SLC31A1 mutations, including 23 missense mutations, one fusion mutation, two frame-shift mutations, and one translation start-codon mutation, were contained in TCGA tumor samples (Additional file 4: Table S2). According to the TCGA tumor samples, S105Y is the most prevalent point mutation in SLC31A1 (Fig. 4b). In the ICGC database, the mutation S105Y was also the most prevalent mutation  in SLC31A1 (Additional file 3: Figure S3).

Fig. 4
figure 4

SLC31A1 genetic alteration in various tumor types in TCGA. The frequency (a) and position of SLC31A1 mutation site b are derived from cBioPortal

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Furthermore, by using the database of OncoVar and COSMIC, S105Y was identified as a potential driver mutation with an OncoVar-score of 0.998 using the AI-driver method (Additional file 4: Tables S3, S4).

Correlation between SLC31A1 expression and immune infiltration

The immune infiltration of tumors could influence the prognosis and treatment. We used TIMER2 to explore the correlation of SLC31A1 in 21 immune infiltrates in multiple tumors using algorithms including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq (Fig. 5). Notably, SLC31A1 was positively correlated with macrophage infiltration in multiple tumors including BLCA, COAD, HNSC, KIRC, LUAD, LUSC, PAAD, and THYM. SLC31A1 was positively correlated with neutrophil infiltration in BLCA, COAD, and STAD. Such findings indicated a potential role of SLC31A1 in the immune process during cancer development and progression.

Fig. 5
figure 5

The correlation of SLC31A1 and the infiltration levels of B cells, cancer associate fibroblast, common lymphoid progenitor, common myeloid progenitor, DC, endothelial cells, eosinophil, granulocyte-monocyte progenitor, hematopoietic stem cells, macrophage, mast cells, monocyte, myeloid-derived suppressor cells, neutrophil, NK cells, CD4 + T cells, CD8 + T cells, T cell follicular helper, T cell gamma delta, NK T cells, and Tregs. Positive correlation in red and negative correlation in blue.

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Enrichment of SLC31A1-related genes in metabolic pathways

GEPIA2 was used to extract the top 100 genes with expression patterns similar to SLC31A1 in all tumor types from TCGA to investigate the gene’s functional impact. (Additional file 4: Table S5). GO and KEGG enrichment analysis indicated that these genes were involved in metabolic pathways and protein processing in the endoplasmic reticulum (Figs. 6a–d). These findings prompted us to wonder whether SLC31A1 plays a role in these biological processes by interacting with essential proteins involved in protein binding, integral components of the membrane, metabolic pathways, and protein processing in the endoplasmic reticulum (Additional file 4: Tables S6-9). Figure 6e showed the PPI network which was conducted in BioGRID (minimum evidence = 1). Based on the Wikipathways annotation, three genes in the copper homeostasis pathway including copper chaperone for superoxide dismutase (CCS), Phosphatidylinositol-4,5-bisphosphate 3-kinase Catalytic subunit alpha (PIK3CA) and Solute Carrier Family 31 Member 2 (SLC31A2) were found as nodes in the PPI network [32]. Furthermore, the expression level of SLC31A2, PIK3CA, and CCS was correlated with SCL31A1 (Fig. 6f, g) (Spearman r = 0.31, 0.34, and −0.22, respectively).

Fig. 6
figure 6

SLC31A1-related gene enrichment analysis. a–d Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the top 100 genes co-expressed with SLC31A1 ranked by Pearson correlation coefficient from GEPIA2. e C1ORF112-protein interactions obtained from BioGRID. f–h Correlation analysis between SLC31A1 and CCS and Erb-B2 Receptor Tyrosine Kinase 3 (ERBB3) is conducted by GEPIA2 across all tumor samples from TCGA

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Discussion

The multi-omics data of 33 tumor types from the TCGA project allow pan-cancer analyses of biomarkers and therapeutic targets using bioinformatic and statistic tools [7, 33,34,35,36]. Our current study evaluated the clinical significance of SCL32A1, a key cuproptosis-regulatory gene, in various cancer types and implicated a potentially substantial role of cuproptosis in cancers.

The recently reported pathophysiological role of cuproptosis may provide new insight into anticancer treatments. SLC31A1 is the primary regulator of Cu uptake, and it expresses in most cells [37, 38]. Cuproptosis is a novel mechanism of cell death whose core is the tricarboxylic acid cycle, and it relies on the mitochondrial respiration [14]. SLC31A1 has recently been proposed as a biomarker for cancer therapy and could play a role in chemoresistance in a few types of cancers [39, 40]. High-affinity copper uptake protein 1 (CTR1) encoded by SLC31A1 is the primary component responsible for Cu uptake in cells [41, 42]. A recent study revealed that CTR1 could function as a redox sensor to drive neovascularization [43]. A strong correlation between CTR1 and Programmed death-ligand 1 paved the way for clinical trials to evaluate Cu chelators as immune checkpoint inhibitors [44]. The current study comprehensively explored whether SLC31A1 plays a role in multiple tumors.

In the current study, TCGA datasets showed that SLC31A1 was expressed in various tissues. According to our findings, dysregulation of the SCL31A1 gene was associated with clinical parameters or prognosis in multiple types of cancer. It was found that a high expression level of SLC31A1 in ACC, KIRC, LGG, and MESO was associated with poor OS and DFS. More and more evidence has shown that genomic mutations influence tumor progression and chemotherapy response [45,46,47]. For example, there is evidence that genetic polymorphisms of SLC31A1 are associated with chemotherapy resistance and clinical outcomes in cancer patients [48]. In the current study, UCEC (> 2%) had the highest mutation rate of SLC31A1, followed by ACC, BLCA, and PRAD. Based on these, SLC31A1 has been found to act as an oncogene in the progression of numerous cancers and may serve as a useful predictor of cancer prognosis.

The molecular mechanism of SLC31A1 in cancers remains to be elucidated. Our results indicated that SLC31A1 might contribute to changes in the immune microenvironment in cancer tissues. The immune microenvironment has also been found to influence molecular phenotypes and prognoses [49,50,51,52]. Our results showed positive correlations of SLC31A1 expression with neutrophil and macrophage infiltration in several tumor types. Such correlation with macrophage infiltration was in line with the high expression level of SLC31A1 in macrophages in single-cell RNA-Seq data, emphasizing the importance of SLC31A1 and the related cuproptosis in the cancer-related immune process. And neutrophils were reported to be involved in the metastasis of breast cancer [53].

Our gene enrichment analysis showed that there was a strong correlation between genes that co-express with SLC31A1 and metabolic pathways in the endoplasmic reticulum. In particular, SLC31A2 and PIK3CA were copper homeostasis-regulated genes with a key role in tumor. Recently the function of SLC31A2 has been reported to associate with the development of lung adenocarcinoma, ovarian carcinoma, hepatocellular carcinoma, and sensitivity to Cisplatin [54,55,56,57]. In the aspect of copper regulation, PIK3CA was reported to be relative to glioma, breast cancer, and medulloblastoma [58,59,60]. Our results suggested that SLC31A1 may play a key role in cancer by influencing metabolic and Cu-related processes.

Our preliminary findings suggest that SLC31A1 could be involved in a variety of tumor types. Nevertheless, there are limitations in the current study. For some rare tumor types, the sample sizes were relatively small and our finding needed to be validated in independent cohorts. Further studies are warranted to determine the molecular function of SLC31A1 in tumorigenesis.

Conclusions

Our pan-cancer analysis demonstrates that the cuproptosis-regulatory gene SLC31A1 is dysregulated in various cancers with its expression and genetic alteration associated with clinical outcomes in patients with these tumors. Additionally, immune infiltration analysis and gene enrichment analysis provide new insight into potential mechanisms related to SLC31A1 in cancers. Our study thus warrants further experimental and clinical studies to understand the function of SLC31A1 and its potential practical applications in cancer therapy and prognosis prediction.

Availability of data and materials

The data used to support the findings of this study are available from the corresponding author upon request.

Abbreviations

SLC31A1:

Solute carrier family 31 member 1

PPI:

Protein–protein interaction

ACC:

Adrenocortical carcinoma

BLCA:

Bladder urothelial carcinoma

BRCA:

Breast carcinoma

CHOL:

Cholangiocarcinoma

ESCA:

Esophageal carcinoma

LGG:

Low-grade glioma

MESO:

Mesothelioma

PCPG:

Pheochromocytoma and paraganglioma

GBM:

Glioblastoma multiforme

STAD:

Stomach adenocarcinoma

SKCM:

Skin cutaneous melanoma

TGCT:

Testicular germ cell tumors

THYM:

Thymoma

UCEC:

Uterine corpus endometrioid carcinoma

KIRC:

Kidney chromophobe

KIRP:

Kidney renal clear cell carcinoma

LIHC:

Liver hepatocellular carcinoma

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

PRAD:

Prostate adenocarcinoma

THCA:

Thyroid carcinoma

PAAD:

Pancreatic adenocarcinoma

UCS:

Uterine Carcinosarcoma

OS:

Overall survival

DFS:

Disease-free survival

GO:

Gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

ICGC:

International Cancer Genome Consortium

TCGA:

The Cancer Genome Atlas

FANTOM5:

Function annotation of the mammalian genome 5

CCS:

Copper chaperone for superoxide dismutase

SLC31A2:

Solute carrier family 31 member 2

PIK3CA:

Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha

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Acknowledgements

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Funding

This project was supported by the National Natural Science Foundation of China (No. 31671311), the National first-class discipline program of Light Industry Technology and Engineering (LITE2018-14), the “Six Talent Peak” Plan of Jiangsu Province (No. SWYY-127), the Innovative and Entrepreneurial Talents of Jiangsu Province, the Program for High-Level Entrepreneurial and Innovative Talents of Jiangsu Province, Taihu Lake Talent Plan, and Fundamental Research Funds for the Central Universities (JUSRP51712B and JUSRP1901XNC), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_1946). Top Talent Support Program for young and middle-aged people of Wuxi Health Committee (No.HB2020038); the Maternal and Child Health Research Project of Jiangsu in China (No. F202117); the Maternal and Child Health Research Project of Wuxi in China (No. FYKY201804); and the Excellent Program for Overseas Students of Wuxi City in 2018 (No. 23), China.

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  1. Fan-Sheng Kong and Chun-Yan Ren are equal contribution

Authors and Affiliations

  1. Department of Pediatrics, Affiliated Hospital of Jiangnan University, Wuxi, 214122, Jiangsu, China

    Fan-Sheng Kong, Ruofan Jia, Yuan Zhou & Yaping Ma

  2. Laboratory of Genomic and Precision Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, China

    Fan-Sheng Kong, Chun-Yan Ren, Ruofan Jia, Yuan Zhou, Jian-Huan Chen & Yaping Ma

  3. Joint Primate Research Center for Chronic Diseases, Institute of Zoology of Guangdong Academy of Science, Jiangnan University, Wuxi, Jiangsu, China

    Jian-Huan Chen

  4. Jiangnan University Brain Institute, Wuxi, Jiangsu, China

    Jian-Huan Chen

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Contributions

FK and CR contributed equally to this article. FK and CR collected the data and performed the data analysis. FK contributed to drafting the manuscript. RJ and YZ contributed to the revision of the manuscript. JHC and YM designed the study and revised the manuscript. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1

. Fig. S1: SLC31A1 expression status in different normal tissues. a, b, and c tissue expression profiles of SLC31A1 based on datasets of the GTEx, FANTOM5 (Function annotation of the mammalian genome 5), and HPA dataset. d SLC31A1 expression in various cell types.

Additional file 2

. Fig. S2. Correlation between SLC31A1 expression and pathological stages of ACC, KIRC, and MESO from TCGA datasets. SLC31A1 expression is in Log2 (TPM + 1).

Additional file 3

. Fig. S3. a The mutation S105Y in the database of ICGC. b The top 10 cancer distribution of donors with S105Y from different cohorts. Donors affected: donors in the current project with SLC31A1 affected by simple somatic mutation (SSM)/SSM-tested donors in the current project. LMS-FR: Soft Tissue cancer (France), BTCA-SG: Biliary Tract cancer (Singapore), SKCA-BR: Biliary Tract cancer (Brazil), MELA-AU: Skin cancer (Australia), LIRI-JP: Liver cancer (Japan), ESAD-UK: Esophageal cancer (United Kingdom), UTCA-FR: Uterine cancer (France), NACA-CN: Nasopharyngeal cancer (China), LICA-CN: Liver cancer (China).

Additional file 4

.Table S1: SLC31A1 genetic alteration types in TCGA datasets from cBioportal. Table S2. SLC31A1 genetic mutations summary in TCGA datasets from cBioportal. Table S3. SLC31A1 genetic alteration profile. Table S4: The source of the mutation of SLC31A1. Table S5: Top 100 genes with similar expression patterns to the SLC3A1 gene from all tumor types of TCGA datasets by GEPIA2. Table S6: GO cellular component (CC) enrichment analysis of 100 SLC31A1-correlated genes. Table S7: GO biological process (BP) enrichment analysis of 100 SLC31A1-correlated genes. Table S8: GO molecular function (MF) enrichment analysis of 100 SLC31A1-correlated genes. Table S9: KEGG enrichment analysis of 100 SLC31A1-correlated genes.

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Kong, FS., Ren, CY., Jia, R. et al. Systematic pan-cancer analysis identifies SLC31A1 as a biomarker in multiple tumor types. BMC Med Genomics 16, 61 (2023). https://doi.org/10.1186/s12920-023-01489-9

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BMC Medical Genomics

ISSN: 1755-8794

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