SLC31A1 Identifying a Novel Biomarker with Potential Prognostic and Immunotherapeutic Potential in Pan-Cancer
Abstract
:1. Introduction
2. Methods
2.1. Gene Expression Analysis
2.1.1. GEPIA Database
2.1.2. HPA Database
2.2. Gene Enrichment Analysis
2.2.1. GeneMANIA Database
2.2.2. STRING Database
2.3. Genetic Alteration Analysis and DNA Methylation Analysis
2.3.1. The cBioPortal Database
2.3.2. UALCAN Database
2.4. Survival Prognosis Analysis
2.5. Immune Infiltration Analysis
3. Results
- A pan-cancer landscape of mRNA expression: we used the GEPIA dataset to analyze the mRNA levels of SLC31A1 in the interactive body map to learn more about its role in human pan-cancer. SLC31A1 expression was shown to be altered throughout many human tumor tissues compared to their corresponding normal tissues. This was notably true for the central nervous system, circulatory system, gastrointestinal system, urinary system, parathyroid glands, and thyroid (Figure 1a). Considering these results, we next examined the mRNA expression levels in 33 malignancies and adjacent normal tissues. Astonishingly, only eight tumor tissues (COAD, DLBC, GBM, LGG, PAAD, READ, STAD, and UCEC) showed higher median mRNA levels of SLC31A1 than normal tissues (Figure 1b). Finally, we examined SLC31A1’s cellular mRNA expression levels using data from the HPA database. The skin, the proximal gastrointestinal tract, the female reproductive system, the eye, and mesenchyme were among the tissue organ cell lines with higher SLC31A1 mRNA expression levels (Figure 1c).
- SLC31A1 expression and the pathological staging of cancers have been shown to have a substantial relationship. The pathological staging of malignancies is one of the key indications of patient prospects. As a result, our research investigated the connection between the SLC31A1 expression levels in cancers and their pathological stages using GEPIA, and it included 17 different malignancies. It is interesting to note that the level of SLC31A1 expression was not found to relate to the pathological stage of any other tumors, apart from ACC (p = 0.0152), KIRC (p = 0.000562), OV (p = 0.0405), and THCA (p = 0.00861); SLC31A1 exhibited an upward trend in relation to the pathological stage in ACC, while displaying a contrasting pattern in KIRC, OV, and THCA (Figure 2). The level of expression of SLC31A1 was shown to be linked with the pathological staging of ACC, KIRC, OV, and THCA, which suggests that it may be of importance in guiding the pathological staging of these malignancies. Interestingly, additional analyses conducted on the identical open-source database produced congruent experimental outcomes to ours, thereby providing further validation of the dependability of our results [17].
- 3.
- Our investigation into the GeneMANIA databases led us to the discovery of 20 genes that are linked with the protein–protein interactions of SLC31A1 (Figure 4a). The small molecule route and protein–protein interaction network of SLC31A1 are shown below. According to the information found in the STRING database, there are a total of 10 nodes connected to the SLC31A1 gene (Figure 4b).
- 4.
- An investigation into the mutations of the SLC31A1 gene and the methylation levels of pan-cancerous tumors: the cBioPortal database was analyzed, and the results showed that 2.1% (54 out of 2584) of pan-cancer patients had mutations in the SLC31A1 gene (Figure 5a). In addition, we investigated the prevalence of mutations in the SLC31A1 gene among the various tumor types. The results showed that the disease with the highest frequency of aberrations was pancreatic cancer, followed by esophageal and gastric cancer and bone cancer (Figure 5b). Notably, mutations are the most common SLC31A1 aberrations. Our research found a total of two mutation sites, both of which were situated between numbers 0 and 200 (Figure 5c). This was done so that we could learn more about the SLC31A mutation sites found throughout the protein domains involved in cancer.
- 5.
- DNA that has been methylated incorrectly is a substantial contributor to the development of cancer. Therefore, in the next step, we analyzed SLC31A1 methylation across cancers and the tissues that correlate with it using the UALCAN database. Compared to normal tissues, the levels of SLC31A1 methylation in HNSC, KIIRP, LIHC, LUSC, PRAD, READ, and UCEC tissues were found to be very different (Figure 6).
- 6.
- The expression and permeation of immunocytes in pan-cancers: in terms of the reality that there is a connection between SLC31A1 and the immune response, we decided to carry out pan-cancer research to investigate the link between SLC31A1 and the degree to which immune cells infiltrated the cancerous tissue. According to the data available here, 20 tumors were related to CD8+ T cells, 14 tumors were related to CD4+ T cells, 20 tumors were related to neutrophils, 21 tumors were related to medullary dendritic cells, 23 tumors were related to macrophages, and 13 tumors were related to B cells (Figure 7a).
- 7.
- We performed an analysis of the expression of SLC31A1 across many cancer types, together with the immune regulators TMB and MSI, and the immunological checkpoints. We assessed the link between SLC31A1 expression and two important immune regulators to quantify the relationship between SLC31A1 expression and the TME in the pan-cancer dataset. This allowed us to better understand the nature of this interaction. Positive associations were found between immune checkpoint genes and most different types of cancer, including UVM, UCEC, STAD, READ, OV, PAAD, LGG, LUSC, LAML, LUAD, DLBC, COAD, and BLCA. Only a small percentage of cancers, including THCA and CHOL tumors, were shown to have a negative association with immune checkpoint genes (Figure 8a).
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
SLC31A1 | Solute carrier family 31 member 1 |
ACC | Adrenocortical carcinoma |
BLCA | Bladder urothelial carcinoma |
BRCA | Breast invasive carcinoma |
CESC | Cervical squamous cell carcinoma |
CHOL | Cholangiocarcinoma |
COAD | Colon adenocarcinoma |
DLBC | Lymphoid neoplasm diffuse large B cell lymphoma |
ESCA | Esophageal carcinoma |
GBM | Glioblastoma |
LGG | Brain lower grade glioma |
HNSC | Head and neck squamous cell carcinoma |
KICH | Kidney chromophobe |
KIRC | Kidney renal clear cell carcinoma |
KIRP | Kidney renal papillary cell carcinoma |
LAML | Acute myeloid leukemia |
LIHC | Liver hepatocellular carcinoma |
LUAD | Lung adenocarcinoma |
LUSC | Lung squamous cell carcinoma |
MESO | Mesothelioma |
OV | Ovarian serous cystadenocarcinoma |
PAAD | Pancreatic adenocarcinoma |
PCPG | Pheochromocytoma and paraganglioma |
PRAD | Prostate adenocarcinoma |
READ | Rectum adenocarcinoma |
SARC | Sarcoma |
SKCM | Skin cutaneous melanoma |
STAD | Stomach adenocarcinoma |
TGCT | Testicular germ cell tumors |
THCA | Thyroid carcinoma |
THYM | Thymoma |
UCEC | Uterine corpus endometrial carcinoma |
UCS | Uterine carcinosarcoma |
UVM | Uveal melanoma |
COX17 | Cytochrome c oxidase copper chaperone |
ATOX1 | Antioxidant 1 |
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Zhang, P.; Yang, H.; Zhu, K.; Chang, C.; Lv, W.; Li, R.; Li, X.; Ye, T.; Cao, D. SLC31A1 Identifying a Novel Biomarker with Potential Prognostic and Immunotherapeutic Potential in Pan-Cancer. Biomedicines 2023, 11, 2884. https://doi.org/10.3390/biomedicines11112884
Zhang P, Yang H, Zhu K, Chang C, Lv W, Li R, Li X, Ye T, Cao D. SLC31A1 Identifying a Novel Biomarker with Potential Prognostic and Immunotherapeutic Potential in Pan-Cancer. Biomedicines. 2023; 11(11):2884. https://doi.org/10.3390/biomedicines11112884
Chicago/Turabian StyleZhang, Pei, Heqi Yang, Kaiguo Zhu, Chen Chang, Wanrui Lv, Ruizhen Li, Xiaoying Li, Tinghong Ye, and Dan Cao. 2023. "SLC31A1 Identifying a Novel Biomarker with Potential Prognostic and Immunotherapeutic Potential in Pan-Cancer" Biomedicines 11, no. 11: 2884. https://doi.org/10.3390/biomedicines11112884