
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13244-6
10.1016/j.heliyon.2024.e37213
e37213
Research Article
A pan-cancer analysis of the association of METRN with prognosis and immune infiltration in human tumors
Wang Li a
Huang Guofu guofuhuang2023@hotmail.com
b⁎
Xiao Han a
Leng Xiaoling b
a The Fifth Affiliated Hospital of Xinjiang Medical University, Xinjiang Medical University, Urumqi, China
b Dongguan Institute of Clinical Cancer Research, Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, China
⁎ Corresponding author. guofuhuang2023@hotmail.com
30 8 2024
15 9 2024
30 8 2024
10 17 e372135 5 2024
16 7 2024
29 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Meteorin (METRN) is expressed predominantly in the central nervous system (CNS), where it functions by regulating glial cell differentiation and promoting axonal elongation. Nonetheless, its function within tumors is still not well understood. In this study, we focused on investigating its expression across various cancers and delving deeper into how METRN expression correlates with prognosis and immune infiltration.

Methods

We explored METRN expression patterns in pan-cancers utilizing data obtained from the UCSC Xena and TCGA. In addition, analyses of survival and clinical association were conducted for tumors where METRN could affect the prognosis. Subsequently, nomogram models were constructed for sarcoma (SARC) and prostate adenocarcinoma (PRAD) to verify METRN's prognostic value in tumors. Furthermore, we also discussed the link between METRN and immune infiltration. As far as mechanisms are concerned, functional enrichment analysis was conducted to analyze the functional components and signaling pathways involved in METRN.

Results

This study found that METRN was abnormally expressed in various tumors, closely connected with the prognosis and clinical characteristics of several tumors, and had good prognostic value. Moreover, analysis of immune infiltration revealed that METRN interacts with multiple immune cells, with alterations in the immune microenvironment potentially influencing tumor prognosis. Enrichment analysis indicates that METRN may influence tumorigenesis and progression through immune-related pathways.

Conclusion

To sum up, our study demonstrates that METRN can be a prospective predictive biomarker in diverse cancer types and a promising target for cancer immunotherapy for pan-cancer.

Highlights

• METRN has always been studied as a neuroprotective factor, and this study is the first to explore its association with cancer.

Keywords

METRN
Pan-cancer
Prognosis
Immune infiltration
Biomarker
==== Body
pmc1 Background

Globally, cancer stands as a leading cause of disease and death, placing a considerable burden on both health and the economy of society [1]. Regrettably, the number of new cases is still rising, causing an increased cancer burden [2]. Even though significant efforts have been devoted to improving the diagnosis and treatment of tumors, the outcomes for the majority of tumors remain unsatisfactory [3]. Consequently, it's critically important to actively seek novel, sensitive biomarkers and alternative therapeutic targets for diagnosing and treating tumors. Pan-cancer analysis refers to the comparison of genetic variants of selected genes across multiple cancers [4]. Exploring effective biomarkers in pan-cancer can aid in diagnosing, predicting prognosis, and administering immunotherapy, facilitating the effective treatment of many tumors without effectively targeting agents currently.

Meteorin, also known as METRN, can be found on human chromosome 16 (p13.3), according to the NCBI database. METRN was first identified in 2004 as a secretory protein in the brain for regulating glial cell differentiation and promoting axon extension [5]. It has been demonstrated by previous research that METRN, expressed predominantly in the central nervous system (CNS), acts as a protective factor [6]. For example, METRN drives gliogenesis after striatal injury [7] and promotes striatal neurogenesis after stroke [8]. Mechanistically, METRN facilitates the development of GFAP-positive glia by activating the Jak-STAT3 pathway [9]. Moreover, Neuroprotective effects of METRN have been reported in the quinolinic acid-induced neuropathology and rat neuropathic pain model [10,11], and a recent study reconfirmed the role of METRN in relieving hyperalgesia in rats with chronic nerve contractile injury [12]. METRN also has the therapeutic effect of reducing pain-related behaviors and neurotoxic symptoms in peripheral neuropathic pain induced by paclitaxel in mice [13]. With the development of sequencing technology, METRN's role in skeletal, retinal, and cardiovascular diseases has also attracted attention.

Although METRN is widely expressed in many rodent organs, most studies have concentrated on the role it plays in the CNS. However, unlike in non-cancerous diseases, there has been less research on the function of METRN in cancer, leading to an unclear role for METRN in pan-cancer. Thus, the aim of this study was to comprehensively investigate the expression of METRN in relation to prognosis and immune cell infiltration by performing pan-cancer analysis.

Here, we analyzed how METRN is expressed in tumors and its prognosis and investigated the possible association between METRN and immune infiltration using databases, including the TCGA, HPA, GEPIA2, and TIMER2.0 databases. We further investigated the biological functions and signaling pathways involved in METRN by performing enrichment analysis. The majority of tumors exhibited varying levels of METRN expression according to the differential expression analysis, and the survival analysis indicated that METRN had a prognostic impact on cholangiocarcinoma (CHOL), acute myeloid leukemia (LAML), prostate adenocarcinoma (PRAD), sarcoma (SARC), uterine carcinosarcoma (UCS), and uveal melanoma (UVM). Analysis of immune infiltration showed that METRN was associated with multiple immune cells, which may be responsible for the impact on tumor prognosis. Furthermore, enrichment analysis revealed METRN could be connected to immune-related pathways. From the results of this study, we speculate that METRN may interact with infiltrating immune cells, thereby affecting the prognosis of cancer patients. METRN may be a valuable biological marker for general cancer diagnosis, prognosis, and immunological prediction. This work might offer a new tumor biomarker as well as a possible target for treatment.

2 Materials and methods

Ethics approval and patient informed consent were not needed since the study complied with TCGA and UCSC published guidelines.

2.1 Expression analysis of METRN

The expression profile data of thirty-three tumors provided by the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and University of California Santa Cruz (UCSC) Xena (https://xenabrowser.net/datapages/) were used to compare the mRNA expression of METRN of TCGA_GTEx samples, TCGA samples, and TCGA matched samples, separately. Based on the format characteristics of the acquired RNAseq data in TPM format, appropriate statistical methods were selected for statistics (“stats” and “car” package), and the data were visualized using the “ggplot2” package (software: R (4.2.1); R packages: ggplot2 [3.3.6], stats [4.2.1], car [3.1–0]). Moreover, the immunohistochemical (IHC) images of METRN at protein levels were obtained from the Human Protein Atlas (HPA, https://www.proteinatlas.org/).

2.2 Prognosis analysis of METRN

We employed Kaplan-Meier analysis to explore the link between METRN and prognostic parameters, such as OS, PFI, and DSS, in thirty-three available tumor types on the basis of the clinical information from TCGA datasets. HRs (95 % CI) and p-values were shown for all tumors using forest plots, while for tumors with p < 0.05, survival curves were plotted separately. Furthermore, for cancers in which the prognosis may be impacted by METRN expression, we created the receiver operating characteristic curve (ROC).

2.3 Correlation analysis between METRN and clinical characteristics

In cancers where METRN expression can influence prognosis, we discussed the relevance between METRN and several vital clinical characteristics, which included WBC count (x10 ^ 9/L), pathologic N stage, and tumor invasion (%).

2.4 Construction and evaluation of the nomogram models

Based on prior studies, analyses of OS based on univariate Cox regression were carried out for cancers where METRN may impact the prognosis. The nomogram model, a reliable and effective method to predict OS in individual patients, was built on tumors with sample sizes >200 and p < 0.05. The accuracy of the predictions for one, three, and five years was calculated using the calibration curves.

2.5 Correlations between METRN and immune infiltration

The Tumor Immune Estimation Resource 2.0 (TIMER2.0, http://timer.cistrome.org/) was employed to explore the correlations between METRN and the abundance of six major immune cells, which included B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells. Apart from common analysis, we evaluated the association between METRN expression and tumor purity, as well as the relationship between different SCNAs of METRN and the abundance of immune infiltration.

2.6 Enrichment analysis and construction of the PPI network

We retrieved the top three hundred METRN-related genes from the GEPIA2 database (http://gepia2.cancer-pku.cn/#similar) that had the most comparable expression pattern to METRN. To further investigate the possible roles of METRN, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were undertaken on METRN-related genes. Furthermore, the three hundred genes were utilized to build a protein-protein interaction (PPI) network with the species defined as human and the minimal interaction threshold set as 0.4 in the STRING database (https://cn.string-db.org/).

2.7 Gene set enrichment analysis (GSEA)

Finally, GSEA analysis was conduct in cancers where METRN can influence prognosis.

2.8 Statistical analysis

The Wilcoxon rank sum test was used to compare the differences between the two groups, and the Spearman rank sum test was used to assess the relevance. Clinical parameters affecting prognosis were determined using univariate Cox regression analysis. Survival curves were estimated using the Kaplan-Meier method. P < 0.05 was deemed statistically significant, if not specially noted.

3 Results

3.1 The expression of METRN in pan-cancer

For a comprehensive understanding of METRN mRNA levels over a cancer-wide range, we analyzed TCGA_GTEx data from the UCSC Xena. It revealed that in most tumor tissues, there were differences in METRN expression compared to the respective normal tissues, with some being highly expressed and some being lowly expressed (Fig. 1A), in agreement with the findings of the TCGA datasets (Fig. 1B). Besides, we further investigated METRN expression in twenty-three different tumor types and matched samples (Fig. 1C). In order to guarantee the reliability of the findings, we also used TIMER2.0 to detect the RNA sequencing data in TCGA. The varying patterns of METRN expression in tumor tissues were generally in keeping with the above results (Supplementary Fig. S1).Fig. 1 The mRNA expression Level of METRN in pan-cancer.

(A)The mRNA expression level of METRN in 33 tumors in TCGA_GTEx samples.

(B)The mRNA expression level of METRN in 33 tumors in TCGA database.

(C)Expression level of METRN in paired samples of 23 tumors in TCGA database.

ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical and endocervical cancers; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; 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; LGG, brain lower grade glioma; 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; STES, stomach and esophageal carcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma. Abbreviations with corresponding full names are also shown in Supplementary Table 1. (ns, p > 0.05; *p < 0.05; **p < 0.01; ***p < 0.001).

Fig. 1

At the same time, we used the HPA to check the protein levels of METRN in various human organs. Typical IHC images of normal thyroid gland, liver, breast, bile duct, prostate, and corresponding tumor tissues were selected and presented (Supplementary Fig. S2).

3.2 Multifaceted prognostic value of METRN in pan-cancer

Subsequently, to investigate the prognostic significance of METRN in various cancers, the correlation between METRN and clinical prognosis was assessed using Kaplan-Meier analysis.

First, as indicated by Fig. 2A, we explored the correlation between METRN and overall survival (OS) in thirty-three tumor types. The findings indicate that aberrant METRN expression was related to OS in CHOL (Fig. 2B), LAML (Fig. 2C), UCS (Fig. 2D), and UVM (Fig. 2E). In CHOL, UCS, and UVM, highly expressed METRN was correlated with a shorter OS, whereas in LAML it was correlated with a longer OS.Fig. 2 The relationship between METRN expression and OS in pan-cancer.

(A)The effects of METRN expression on OS in pan-cancer were exhibited by a forest diagram.

(B–E) Survival curves of METRN on OS in CHOL, LAML, UCS and UVM, respectively.

Fig. 2

In the next place, our research discussed the relativity between disease-specific survival (DSS) and METRN expression (Fig. 3A), indicating a connection between METRN expression and the DSS of UCS (Fig. 3B) and UVM (Fig. 3C). Up-regulation of METRN expression is correlated with poorer DSS in both UCS and UVM.Fig. 3 The relationship between METRN expression and DSS in pan-cancer.

(A)The effects of METRN expression on DSS in pan-cancer were exhibited by a forest diagram.

(B–C) Survival curves of METRN on DSS in UCS and UVM, respectively.

Fig. 3

Eventually, we studied the expression of METRN in relation to the progression-free interval (PFI) (Fig. 4A), and METRN expression was connected with PFI of PRAD (Fig. 4B), SARC (Fig. 4C), UCS (Fig. 4D), and UVM (Fig. 4E). We can easily see that a low expression of METRN predicts a better PFI in PRAD, SARC, UCS, and UVM in the above four cancers.Fig. 4 The relationship between METRN expression and PFI in pan-cancer.

(A)The effects of METRN expression on PFI in pan-cancer were exhibited by a forest diagram.

(B–E) Survival curves of METRN on PFI in PARD, SARC, UCS and UVM, respectively.

Fig. 4

Furthermore, we plotted ROC curves (Supplementary Figs. S3A–C) for three tumors whose prognosis correlated with METRN expression, illustrating the ability of METRN in their diagnosis.

3.3 The correlations between METRN and clinical characteristics

According to the results of the prognostic analysis, the prognosis of six tumor types, including CHOL, LAML, PRAD, SARC, USC, and UVM, was correlated with METRN expression. Therefore, we delved into the connection between METRN and clinical parameters in the tumors mentioned above and indicated that METRN expression was associated with WBC count (x10^9/L), FAB classifications, and cytogenetic risk in LAML (Fig. 5A–C). Meanwhile, METRN was correlated with pathologic N stage and Gleason score in PRAD (Fig. 5D and E). Furthermore, METRN expression was associated with tumor invasion (%) in UCS (Fig. 5F).Fig. 5 The correlations between METRN expression and clinical parameters.

(A-C) METRN expression was correlated with WBC count(x10^9/L), FAB classifications, and Cytogenetic risk in LAML.

(D-E) METRN expression was correlated with Pathologic N stage and Gleason score in PRAD.

(F) METRN expression was correlated with Tumor invasion (%) in UCS.

(*p < 0.05; **p < 0.01; ***p < 0.001).

Fig. 5

3.4 Construction and evaluation of nomogram models

To further explore the correlation between METRN and prognosis, univariate Cox regression analyses on the OS were carried out in tumors where METRN could influence the prognosis (Supplementary Tables S2–S7).

In accordance with the above results, LAML, UCS, and UVM, which could affect prognosis and have sample sizes greater than 200, were chosen to establish nomogram models to validate the prognostic values, and the accuracy of the predictions for one, three, and five years was calculated using the calibration curves.

As the results showed, in SARC (Fig. 6A and B) and PRAD (Fig. 6C and D), the nomogram models indicated that METRN exhibited promising predictive ability for OS, while the calibration curves indicated that nomogram models provided excellent prediction accuracy.Fig. 6 Construction and evaluation of Nomogram models were constructed and evaluated in SARC and PRAD.

(A)Construction of a nomogram model incorporating METRN expression in SARC.

(B)Calibration curves were used to evaluate the nomogram model in PRAD at 1-year, 3-year, and 5-year.

(C)Construction of a nomogram model incorporating METRN expression in SARC.

(D)Calibration curves were used to evaluate the nomogram model in PRAD at 1-year, 3-year, and 5-year.

Fig. 6

3.5 The correlation of METRN and immune infiltration

Tumor occurrence and development are largely influenced by the immune microenvironment. To clarify the interaction between METRN and the tumor immune microenvironment, we investigated the relationship between METRN and six major immune cells using TIMER 2.0.

First of all, we explored the relationship between METRN and tumor immune cells and demonstrated the correlation heatmaps, including B cells (Fig. 7A), CD4+ T cells (Fig. 7B), CD8+ T cells (Fig. 7C), macrophages (Fig. 7D), neutrophils (Fig. 7E), and dendritic cells (Fig. 7F). We can see that METRN expression correlates with immune infiltration in a wide range of tumors. In addition, we plotted correlation scatter plots in tumors that can affect prognosis to further describe the relationship between METRN expression and the level of immune cell infiltration (Supplementary Fig. S4), and from the results, there is a statistically significant relationship between the alteration of some specific types of immune cells and the expression of METRN, suggesting that the alteration of these immune cells dependent on MERTN expression.Fig. 7 The correlation of METRN expression and immune infiltration.

(A-F) Heatmaps of correlations between METRN expression and B cells, T cell CD4+, CD8+ T cells, macrophages, neutrophils, and dendritic cells in TIMER2 database, respectively.

Fig. 7

Then, we also focused on correlating METRN expression with tumor purity. From the results, it can be seen that METRN was positively linked to tumor purity in UVM and was not significantly correlated in CHOL, PRAD, SARS, or UCS (Supplementary Fig. S5). In CHOL, METRN had a negative connection with the infiltration of B cells, CD8+ T cells, and macrophages. Similarly, METRN had a negative connection with the infiltration of B cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells in PRAD. Additionally, there was a negative connection between METRN and neutrophil infiltration in SARC and a positive connection with the level of B-cell infiltration in UCS. Moreover, METRN was positively linked to macrophage and dendritic cell infiltration levels and negatively linked to B cells and neutrophils in UVM.

Except for the general analysis above, we analyzed the connection between METRN SCNAs and the abundance of infiltrating immune cells. The findings indicated that different SCNAs of METRN were statistically related to CD4+ T cell infiltration in PRAD. Furthermore, the METRN of different SCNAs had a statistical correlation with the infiltration of B cells, CD4 + T cells, macrophages, and dendritic cells in SARC and B cells and CD8 + T cells in UCS (Supplementary Fig. S6).

3.6 Enrichment analysis and PPI network of METRN-related genes

For further investigation of the biological role of METRN in tumors, we derived three hundred METRN-related genes from the GEPIA2 database (Supplementary Table S8). GO and KEGG of the resulting genes were performed to determine the key functional components and signaling pathways involved in tumor occurrence and development.

GO enrichment analysis revealed a remarkable influence of one hundred and twenty-seven biological processes (BP), including axon development (GO:0061564), axonogenesis (GO:0007409), glial cell differentiation (GO:0010001), and regulation of nervous system development (GO:0051960). Additionally, fifty-nine cellular components (CC), such as integral component of postsynaptic membrane (GO:0099055), intrinsic component of postsynaptic membrane (GO:0098936), integral component of synaptic membrane (GO:0099699), and intrinsic component of synaptic membrane (GO:0099240) had notable influences. Furthermore, significant effects were seen in four molecular functions (MF), including tubulin binding (GO:0015631), microtubule binding (GO:0008017), GABA receptor binding (GO:0050811), and structural constituent of cytoskeleton (GO:0005200) showed remarkable impacts (Fig. 8A).

At the same time, KEGG enrichment analysis (Fig. 8B) suggested that METRN-related genes are connected to mannose type O-glycan biosynthesis (hsa00515) and cell adhesion molecules (hsa04514). Additionally, 300 METRN-related genes were input into the String database for constructing the PPI network (Supplementary Fig. S7). As can be seen, there were 282 edges and 233 nodes that demonstrated correlations between proteins.Fig. 8 Functional enrichment analysis of METRN-related genes.

(A)GO enrichment analysis based on 300 METRN-related genes, including BP, CC, and MF.

(B) KEGG pathways analysis based on 300 METRN-related genes.

(B-H) GSEA based on the differentially expression analysis in CHOL, LAML, PRAD, SARC, UCS and UVM, respectively.

Fig. 8

3.7 Gene set enrichment analysis

Finally, the function of METRN was further confirmed by performing GSEA on the basis of the expression of METRN in six tumors where METRN expression was linked to prognosis, including CHOL (Fig. 8C), LAML (Fig. 8D), PRAD (Fig. 8E), SARC (Fig. 8F), UCS (Fig. 8G), and UVM (Fig. 8H). The findings demonstrated that METRN is involved in immune-related pathways.

4 Discussion

Human survival is seriously threatened by tumors. Although certain cancers have been shown to have valid diagnostic and therapeutic targets, many tumors still lack effective biomarkers [14,15], which results in a very dismal prognosis. Consequently, the identification of significant biomarkers helpful for multiple tumors may make it possible to treat tumors without effective targets.

METRN is a significant neurotrophic factor expressed primarily in the CNS. It is important for maintaining astrocyte homeostasis [16]. Apart from its function as a neurotrophic factor, METRN has also been proven to inhibit microvascular endothelial cells' angiogenic activity by inducing astrocytes to produce thrombin-sensitive protein-1/-2 [17]. Due to its angiogenic regulatory activity, it was found to be abnormally expressed in serum of preeclampsia characterized by an imbalance in the ratio of angiogenic/antiangiogenic factors, suggesting that METRN's functional role is not limited to the central nervous system [18]. For instance, in a study exploring the effects of diclofenac sodium (DS) on the cardiovascular system (CVS), the authors found that, in comparison to the untreated group, METRN was elevated in the DS group [19]. Besides, METRN is particularly highly expressed in bone marrow macrophages and restrains hematopoietic stem/progenitor cells (HSPCs) mobilization while also regulates HSPCs homeostasis in a hypoxic environment [20]. According to a prior study, METRN exhibits significant expression in early embryos during gastrulation [21], while recent research showed that overexpression of METRN in CRC was significantly correlated with a poorer clinical stage and suggested a worse clinical prognosis [22]. Furthermore, METRN represents a unique therapeutic target in wet age-related macular degeneration [23].

Currently, although METRN has been well studied in non-tumor diseases, its research in tumors is still relatively lacking. As far as we know, the relationship between METRN and tumor prognosis has only been demonstrated in CRC, and higher expression of METRN in CRC has a significant correlation with a late stage and a worse prognosis [22]. Thus, it seems logical to hypothesize that METRN expression may influence the prognosis of other tumors. Therefore, how METRN is expressed in different tumors and how it relates to prognosis and immunological infiltration remain unclear.

Here, for the first time, we visualized the prognosis of METRN in thirty-three tumors and explored the potential connection between METRN and immune infiltration with the use of the TCGA, HPA, GEPIA2, and TIMER2.0 databases.

We compared the expression of METRN in normal and tumor tissues from various organs using samples from TCGA and UCSC Xena. Overall, the analysis results of the three datasets were in general agreement, and we observed higher expression of METRN in tumors like BRCA, CHOL, HNSC, KICH, KIRP, LUAD, PRAD, and LIHC but lower expression in tumors like KIRC, TGCT, and THCA. Furthermore, relative to normal adjacent tissues, METRN was higher expressed in BRCA, CHOL, HNSC, KICH, KIRP, LUAD, PRAD, and LIHC but lower in KIRC and THCA, according to the expression analysis in TIMER2.0. This suggests that tumor occurrence and development may be influenced by the abnormal expression of METRN. In addition, based on ICH images from HPA, we found that the expression levels of METRN in normal bile ducts, prostate and corresponding tumor tissues were consistent with their mRNA levels.

Similarly, studies of the clinical relevance of METRN to cancer and prognosis are lacking. We discovered that METRN expression had a prognostic value in some tumors on the basis of the TCGA database. For example, METRN was related to OS in CHOL, LAML, UCS, and UVM; highly expressed METRN was related to shorter OS in CHOL, UCS, and UVM; and conversely, highly expressed METRN in LAML means longer OS. Moreover, METRN expression was associated with DSS in UCS and UVM, and high METRN expression was related to poorer DSS. Furthermore, METRN expression was related to PFI in PRAD, SARC, UCS, and UVM, and low expression of METRN predicted better PFI in four cancers. We can see that METRN acts as an oncogene in some tumors and as a tumor suppressor gene in others, suggesting that METRN may have completely opposite functions in different tumors. In cancers where METRN can affect prognosis, our findings showed that METRN expression is associated with clinical parameters, indicating that METRN may affect prognosis by changing clinical parameters. According to these results, METRN is a novel tumor-prognostic biomarker.

In addition, we selected representative tumors (SARC and PRAD) to further elucidate how METRN affects tumor prognosis. The findings demonstrated a noticeable influence of METRN on prognosis and good predictive ability for OS.

Another essential discovery is the association between METRN and the immune infiltration level in tumors. According to our analysis, the expression of METRN is significantly related to the infiltration of immune cells in pan-cancer. Interestingly, the results indicated that METRN expression was positively related to tumor purity in UVM, suggesting its relative enrichment in tumor cells. However, in CHOL, PRAD, SARS, and UCS, there was no significant correlation, indicating that METRN expression was no different in the tumor microenvironment and tumor cells. Additionally, we examined the association between the SCNAs of METRN and the abundance of infiltrating immune cells, and the results showed that METRN of different SCNA showed a statistically significant correlation with immune infiltration in PRAD, SARC, and UCS. Immune cell is an important component of the tumor immune microenvironment, immune infiltration can mediate immunosuppression or activation of the tumor microenvironment, helping tumor cells to achieve immune escape and promoting malignant tumor progression, which in turn affects prognosis. These results imply that METRN is crucial for tumor immune microenvironment and may eventually impact patient survival.

Functional enrichment analyses were performed on genes most comparable to METRN to learn more about the biological role of METRN. GO enrichment analysis revealed that METRN is associated with four molecular functions. Previous studies have found that the process of cell invasion and migration depends on changes in cytoskeletal components, including tubulin [24]. It has also been found that structural constituents of the cytoskeleton are associated with tumor immunity. For example, alterations in microtubule can lead to suppression of T-cell function, and furthermore, microtubule binding inhibitors not only increase tumor-infiltrating lymphocytes to mediate anti-tumor immunity in melanoma but are also an ideal therapeutic option for highly immunologically active gastric cancers[[25], [26], [27]]. The KEGG analysis indicated that METRN is associated with cell adhesion molecule (CAM) (hsa04514), and as CAM is well-recognized promoters leading to tumor metastasis [28,29], we rationally propose that METRN may modulate tumor metastasis through its involvement in the biological actions of CAM. In addition, GSEA enrichment analysis of METRN-related genes revealed that eukaryotic translation elongation factors were enriched in PRAD, which previous studies have shown to be an important molecule in promoting cancer metastasis [30,31]. These evidences suggest that METRN may be a key regulatory molecule in tumor metastasis. Furthermore, it is of interest to note that METRN is connected to immune-related pathways, including signaling by the B-cell receptor (BCR) as well as immunomodulatory interactions between lymphoid and non-lymphoid cells, as seen from the results of the GSEA enrichment analysis. BCR signaling is crucial for normal B-cell development and adaptive immunity. It can exert an anti-tumor effect through the production of antibodies against tumor-associated antigens and, conversely, can promote tumors through the production of factors that suppress anti-tumor immunity. For example, BCR signaling drives cell proliferation in Chronic Lymphocytic Leukemia (CLL), and inhibitors targeting BCR-related kinase (like BTK) have been clinically successful in the treatment of CLL [32]. Furthermore, modulation of BCR signaling in CRC can inhibit tumorigenesis by enhancing B-cell responses and improve the efficacy of immunotherapy [33].

We postulated, in light of these findings, that the METRN expression level influences the immune cells' activity in tumors. Alterations in the tumor immune microenvironment caused by differential gene expression offer enormous promise for immune therapy [34,35], and considerable progress has been made in a number of studies [[36], [37], [38]]. The present study found that METRN may affect prognosis by altering immune infiltration, which means that METRN may be promising for the study of novel targeted medications for the immune therapy of certain tumors, which might help a significant number of cancer patients.

In this study, we performed comprehensive analyses of METRN expression, prognosis, immune infiltration, and biological function in tumors, and we found that METRN was associated with prognosis and that this role may be achieved by affecting immune infiltration and pathways related to immunity. It suggested that METRN can be a useful biomarker for general cancer diagnosis, prognosis, and immune prediction. In addition, immunotherapy strategies targeting METRN may also be of benefit to cancer patients. Considering the association of METRN with immune cells and immune-related pathways, the development of therapeutic agents targeting METRN or the biological processes, cellular components, molecular functions, and signaling pathways involved in its immune effects could provide tumor suppression through the modulation of the tumor immune response. In general, this work provides a new tumor biomarker and possible therapeutic target and provides direction for future mechanistic studies.

Although we merged information from several databases to make our findings accurate and reliable, some limitations of this study are inevitable. First of all, we performed the tumor tissue information analysis by collecting a large amount of microarray and sequencing data. Therefore, different sequencing approaches may introduce a systematic bias. In the next place, inadequate sample sizes of cancers or control groups result in unclear conclusions. Thirdly, in this study, bioinformatics analyses of METRN expression and prognosis were performed only in public databases, and more experiments are expected to confirm the conclusions obtained. Finally, although we discovered that METRN was associated with both prognosis and immune infiltration in tumors, we could not demonstrate that METRN influences the survival of patients by influencing immune infiltration. More studies of METRN and immune function in tumors may contribute to giving a clear answer.

5 Conclusion

In conclusion, we found that differentially expressed METRN is related to prognosis in a range of cancers, and that may be achieved through modulation of the immune microenvironment. METRN could be a tumor diagnostic and prognostic marker and might offer an immune-based therapeutic approach, requiring further studies to confirm.

Ethics statement

Ethics approval and patient informed consent were not needed since the study complied with TCGA and UCSC published guidelines.

Review and approval by an ethics committee was not needed for this study because the study complied with TCGA and UCSC published guidelines.

Data availability statement

The datasets analyzed during the current study are available in the TCGA (https://portal.gdc.cancer.gov/), UCSC Xena (https://xenabrowser.net/datapages/), HPA (https://www.proteinatlas.org/), TIMER2.0 (http://timer.cistrome.org/), GEPIA2 (http://gepia2.cancer-pku.cn/#index), and STRING database (https://cn.string-db.org/).

Funding

This work was supported by 10.13039/100009110 Natural Science Foundation of Xinjiang Uygur Autonomous Region (No. 2022D01C310 ).

CRediT authorship contribution statement

Li Wang: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation. Guofu Huang: Supervision, Project administration, Methodology, Conceptualization. Han Xiao: Writing – review & editing, Validation, Data curation. Xiaoling Leng: Writing – review & editing, Supervision.

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.

Appendix A Supplementary data

The following are the Supplementary data to this article.Multimedia component 1

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Acknowledgements

Not applicable.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e37213.
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References

1 Sung H. Ferlay J. Siegel R.L. Laversanne M. Soerjomataram I. Jemal A. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA A Cancer J. Clin. 71 2021 209 249 10.3322/caac.21660
2 Sun D. Cao M. Li H. He S. Chen W. National cancer center/national clinical research center for cancer/cancer hospital, Chinese academy of medical sciences & peking union medical college, Beijing 100021, China. Cancer burden and trends in China: a review and comparison with Japan and South Korea Chin. J. Cancer Res. 32 2020 129 139 10.21147/j.issn.1000-9604.2020.02.01 32410791
3 Cao W. Chen H.-D. Yu Y.-W. Li N. Chen W.-Q. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020 Chinese Med J 134 2021 783 791 10.1097/CM9.0000000000001474
4 Srivastava S. Hanash S. Pan-cancer early detection: hype or hope? Cancer Cell 38 2020 23 24 10.1016/j.ccell.2020.05.021 32531269
5 Nishino J. Yamashita K. Hashiguchi H. Fujii H. Shimazaki T. Hamada H. Meteorin: a secreted protein that regulates glial cell differentiation and promotes axonal extension EMBO J. 23 2004 1998 2008 10.1038/sj.emboj.7600202 15085178
6 Jørgensen J.R. Thompson L. Fjord-Larsen L. Krabbe C. Torp M. Kalkkinen N. Characterization of meteorin—an evolutionary conserved neurotrophic factor J. Mol. Neurosci. 39 2009 104 116 10.1007/s12031-009-9189-4 19259827
7 Wright J.L. Ermine C.M. Jørgensen J.R. Parish C.L. Thompson L.H. Over-expression of meteorin drives gliogenesis following striatal injury Front. Cell. Neurosci. 10 2016 177 10.3389/fncel.2016.00177 27458346
8 Wang Z. Andrade N. Torp M. Wattananit S. Arvidsson A. Kokaia Z. Meteorin is a chemokinetic factor in neuroblast migration and promotes stroke-induced striatal neurogenesis J. Cerebr. Blood Flow Metabol. 32 2012 387 398 10.1038/jcbfm.2011.156
9 Lee H.S. Han J. Lee S.-H. Park J.A. Kim K.-W. Meteorin promotes the formation of GFAP-positive glia via activation of the Jak-STAT3 pathway J. Cell Sci. 123 2010 1959 1968 10.1242/jcs.063784 20460434
10 Jørgensen J.R. Xu X.-J. Arnold H.M. Munro G. Hao J.-X. Pepinsky B. Meteorin reverses hypersensitivity in rat models of neuropathic pain Exp. Neurol. 237 2012 260 266 10.1016/j.expneurol.2012.06.027 22766205
11 Tornøe J. Torp M. Jørgensen J.R. Emerich D.F. Thanos C. Bintz B. Encapsulated cell-based biodelivery of meteorin is neuroprotective in the quinolinic acid rat model of neurodegenerative disease Restor. Neurol. Neurosci. 30 2012 225 236 10.3233/RNN-2012-110199 22426041
12 Xie J.Y. Qu C. Munro G. Petersen K.A. Porreca F. Antihyperalgesic effects of Meteorin in the rat chronic constriction injury model: a replication study Pain 160 2019 1847 1855 10.1097/j.pain.0000000000001569 31335652
13 Sankaranarayanan I. Tavares-Ferreira D. He L. Kume M. Mwirigi J.M. Madsen T.M. Meteorin alleviates paclitaxel-induced peripheral neuropathic pain in mice J. Pain 24 2023 555 567 10.1016/j.jpain.2022.10.015 36336327
14 Nakamura Y. Kawazoe A. Lordick F. Janjigian Y.Y. Shitara K. Biomarker-targeted therapies for advanced-stage gastric and gastro-oesophageal junction cancers: an emerging paradigm Nat. Rev. Clin. Oncol. 18 2021 473 487 10.1038/s41571-021-00492-2 33790428
15 Zhao X. Ren Y. Lu Z. Potential diagnostic and therapeutic roles of exosomes in pancreatic cancer Biochim. Biophys. Acta Rev. Canc 1874 2020 188414 10.1016/j.bbcan.2020.188414
16 Lee H.S. Lee S.-H. Cha J.-H. Seo J.H. Ahn B.J. Kim K.-W. Meteorin is upregulated in reactive astrocytes and functions as a negative feedback effector in reactive gliosis Mol. Med. Rep. 12 2015 1817 1823 10.3892/mmr.2015.3610 25873382
17 Park J.A. Lee H.S. Ko K.J. Park S.Y. Kim J.H. Choe G. Meteorin regulates angiogenesis at the gliovascular interface Glia 56 2008 247 258 10.1002/glia.20600 18059000
18 Garcés M.F. Sanchez E. Cardona L.F. Simanca E.L. González I. Leal L.G. Maternal serum meteorin levels and the risk of preeclampsia PLoS One 10 2015 e0131013 10.1371/journal.pone.0131013
19 Dolanbay T. Makav M. Gul H.F. Karakurt E. The effect of diclofenac sodium intoxication on the cardiovascular system in rats Am. J. Emerg. Med. 46 2021 560 566 10.1016/j.ajem.2020.11.022 33272872
20 Dai Y.-W. Ma J.-K. Jiang R. Zhan X.-L. Chen S.-Y. Feng L.-L. Meteorin links the bone marrow hypoxic state to hematopoietic stem/progenitor cell mobilization Cell Rep. 40 2022 111361 10.1016/j.celrep.2022.111361
21 Kim Y.-Y. Moon J.-S. Kwon M. Shin J. Im S.-K. Kim H.-A. Meteorin regulates mesendoderm development by enhancing nodal expression PLoS One 9 2014 e88811 10.1371/journal.pone.0088811
22 Xu X. Zhang C. Xia Y. Yu J. Over expression of METRN predicts poor clinical prognosis in colorectal cancer Mol Genet Genomic Med 8 2020 e1102 10.1002/mgg3.1102
23 Delaunay K. Sellam A. Dinet V. Moulin A. Zhao M. Gelizé E. Meteorin is a novel therapeutic target for wet age-related macular degeneration JCM 10 2021 2973 10.3390/jcm10132973 34279457
24 Aseervatham J. Cytoskeletal remodeling in cancer Biology 9 2020 385 10.3390/biology9110385 33171868
25 Kremer K.N. Buser A. Thumkeo D. Narumiya S. Jacobelli J. Pelanda R. LPA suppresses T cell function by altering the cytoskeleton and disrupting immune synapse formation Proc Natl Acad Sci U S A 119 2022 e2118816119 10.1073/pnas.2118816119
26 Ren Y. Wang Y. Liu J. Liu T. Yuan L. Wu C. X-Ray crystal structure-guided discovery of novel indole analogues as colchicine-binding site tubulin inhibitors with immune-potentiating and antitumor effects against melanoma J. Med. Chem. 66 2023 6697 6714 10.1021/acs.jmedchem.3c00011 37145846
27 Bai J. Yang B. Shi R. Shao X. Yang Y. Wang F. Could microtubule inhibitors be the best choice of therapy in gastric cancer with high immune activity: mutant DYNC1H1 as a biomarker Aging (Albany NY) 12 2020 25101 25119 10.18632/aging.104084 33221769
28 Li H. Gao J. Zhang S. Functional and clinical characteristics of cell adhesion molecule CADM1 in cancer Front. Cell Dev. Biol. 9 2021 714298 10.3389/fcell.2021.714298
29 Polcik L. Dannewitz Prosseda S. Pozzo F. Zucchetto A. Gattei V. Hartmann T.N. Integrin signaling shaping BTK-inhibitor resistance Cells 11 2022 2235 10.3390/cells11142235 35883678
30 Wang R. Lv C. Li D. Song Y. Yan Z. EEF1D stabilized by SRSF9 promotes colorectal cancer via enhancing the proliferation and metastasis Int. J. Cancer 2024 10.1002/ijc.35039
31 Wu Q. Hu Q. Hai Y. Li Y. Gao Y. METTL13 facilitates cell growth and metastasis in gastric cancer via an eEF1A/HN1L positive feedback circuit J Cell Commun Signal 17 2023 121 135 10.1007/s12079-022-00687-x 35925508
32 Koehrer S. Burger J.A. Chronic lymphocytic leukemia: disease biology Acta Haematol. 147 2024 8 21 10.1159/000533610 37717577
33 Ye J. Lee P.P. B cell receptor signaling strength modulates cancer immunity J. Clin. Invest. 132 2022 e157665 10.1172/JCI157665
34 Zhang Y. Zhang Z. The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications Cell. Mol. Immunol. 17 2020 807 821 10.1038/s41423-020-0488-6 32612154
35 Abbott M. Ustoyev Y. Cancer and the immune system: the history and background of immunotherapy Semin. Oncol. Nurs. 35 2019 150923 10.1016/j.soncn.2019.08.002
36 Reckamp K.L. Redman M.W. Dragnev K.H. Minichiello K. Villaruz L.C. Faller B. Phase II randomized study of ramucirumab and pembrolizumab versus standard of care in advanced non–small-cell lung cancer previously treated with immunotherapy—lung-MAP S1800A J. Clin. Orthod. 40 2022 2295 2307 10.1200/JCO.22.00912
37 Chu X. Tian W. Wang Z. Zhang J. Zhou R. Co-Inhibition of TIGIT and PD-1/PD-L1 in cancer immunotherapy: mechanisms and clinical trials Mol. Cancer 22 2023 93 10.1186/s12943-023-01800-3 37291608
38 Luo H. Lu J. Bai Y. Mao T. Wang J. Fan Q. Effect of camrelizumab vs placebo added to chemotherapy on survival and progression-free survival in patients with advanced or metastatic esophageal squamous cell carcinoma: the ESCORT-1st randomized clinical trial JAMA 326 2021 916 925 10.1001/jama.2021.12836 34519801
