TCN1 is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma

doi:10.21203/rs.3.rs-985718/v1

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TCN1 is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-985718/v1

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published 14 Mar, 2022

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Background: Around the world Lung cancer is the leading cause of cancer-related death. Lung adenocarcinomas are among the most common diagnosed forms of lung cancer, whose overall survival has not improved significantly, which makes finding an effective therapeutic target vital. Transcobalamin (TCN1) is a vitamin B12 binding protein which regulates cobalamin homeostasis. In tumor tissues, TCN1 is expressed highly, and its expression is correlated with cancer aggressiveness and poor prognosis according to recent studies and bioinformatic analyses. However, its effect on lung adenocarcinoma (LUAD) is unknown.

Methods: We evaluated whether TCN1 shows diagnostic and prognostic value in LUAD using bioinformatic analysis. In particularly, various databases and analysis tools were used to determine TCN1’s relationship with LUAD, including TCGA, GTEx, GEO, STRING and TISIDB.

Results: As compared to normal lung tissue, the level of TCN1 expression in LUAD tissues was significantly higher. According to univariate Cox statistics, high expression levels of TCN1 correlate with poor OS (overall survival) in LUAD. Moreover, based on a multivariate Cox analysis, TCN1 expression was independently correlated with OS. GO/KEGG and GSEA indicated enrichment in epidermal cell differentiation, keratinocyte differentiation, neuroactive ligand receptor interaction, epithelial mesenchymal transition and TNFA signaling via NFKB. Furthermore, TCN1 is associated with immune infiltration based on an analysis of immune cell infiltration.

Conclusions: In summary, TCN1 could be used as a prognostic and diagnostic biomarker and provide deeper perspectives for the development of therapies and prognostic markers in LUAD.

Biomedical Engineering

Biomaterials

TCN1

LUAD

Prognosis

Immune infiltrates

Globally, lung cancer is one of the most common cause of cancer-related death [1]. About 85% of lung cancer cases are non-small cell lung cancer (NSCLC), of which lung adenocarcinoma is the most common pathological subtype[2]. During the past decade, the diagnosis and treatment of lung cancer have been developed continuously, especially the emergence of targeted therapy, which has led to the extension of many patients' lives. However, the OS for lung adenocarcinoma has not improved substantially, due to the lack of effective therapeutic targets[3]. Therefore, it is urgent to identify novel molecular mechanisms and effective therapeutic targets for LUAD.

TCN1 [haptocorrin or vitamin B12 (cobalamin) R binder], is one of the three transport proteins that binds vitamin B12 and is present in human serum and various biological fluids[4]. It plays multiple roles in keeping the basic functions of cell metabolism and proliferation, particularly in haematopoiesis and neural development[5]. Upregulation of TCN1 has been reported in the cytoplasm of tumor tissues, and TCN1 is overexpressed in some malignant tumors and associated with tumor proliferation and metastasis, such as colon cancer, hypopharyngeal squamous cell cancer, breast cancer, and gastric cancers[6-8]. The overexpression of TCN1 in tumor tissues causes tumorigenesis and poor biological behaviour[9]. Recently, a study on clinicopathological analysis and prognostic by Zhu et al has reported that TCN1 may be an vital oncogene for colorectal cancer[10]. However, the expression of TCN1 in LUAD and its clinical significance remain unclear.

During this study, we first assessed the correlation between TCN1 expression and the prognosis of LUAD patients from the GEO (Gene expression omnibus) database and TCGA (The cancer genome atlas) database. Furthermore, our study examined the relationship between TCN1 mRNA levels and tumor-infiltrating immune cells. Based on these results, we can better understand TCN1's role in LUAD and the immune response against tumors.

LUAD patients' Clinical characteristics

From the TCGA database, we downloaded clinical and gene expression data of 535 tumors and 59 normal samples, including patients’ age, gender, T classification, M classification, N classification and pathologic stage (Table 1).

TCN1 expression in different tumors and in LUAD patients

First, we examined the expression of TCN1 in various tumor and normal tissue types using the TCGA database and GTEx database. As a result, we concluded the expression of TCN1 was significantly enhanced compared to normal tissues in colon adenocarcinoma (COAD), bladder urothelial carcinoma (BLCA), in cholangiocarcinoma (CHOL), kidney renal papillary cell carcinoma (KIRP), uterine corpus endometrial carcinoma (UCEC), lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). To the contrary, the expression of TCN1 was significantly lower than that in normal control tissues in lymphoid neoplasm diffuse large B-cell Lymphoma (DLBC), breast invasive carcinoma (BRCA), liver hepatocellular carcinoma (LIHC), testicular germ cell tumors (TDCT), acute myeloid leukemia (LAML) and head and neck squamous cell carcinoma (HNSC) (Fig. 1a). Based on TCGA and GEO data, we analyzed TCN1 transcription levels. A significant upregulation of TCN1 mRNA was found in LUAD tissues compared to normal tissues (P < 0.001) (Fig. 1b). The expression of TCN1 mRNA was significantly elevated in LUAD as compared to adjacent tissues (P < 0.001) (Fig. 1c).

TCN1 expression and diagnostic value in LUAD

In our study, we evaluated the diagnostic value of TCN1 mRNA expression using ROC curves. The results indicated that the Area Under the Curve (AUC) of TCN1 was 0.788. Also, we examined the expression of TCN1 mRNA at various stages, with AUC values of 0.779, 0.788, 0.834 and 0.701 for the stages I, II, III and IV, respectively. (Fig. 2).

TCN1 expression is independently associated with a poorer outcome in patients with LUAD

Kaplan-Meier analysis indicates that high TCN1 mRNA expression was associated with poor OS (P=0.001). Subgroup analysis indicated that overexpression of TCN1 mRNA was significantly affected the OS in LUAD cases of pathologic stage I&II (P=0.036), pathologic stage III&IV (P=0.001), T1&T2 (P=0.01), N2 (P=0.001), M0 (P=0.006) and M1 (P=0.028), respectively (Fig. 3). The univariate analysis revealed that higher TCN1 mRNA expression, T stage, N stage, M stage, pathologic stage were correlated with OS (Table 2). According to the multivariate analysis, the mRNA expression of TCN1 was an independent risk factor for OS in LUAD (Table 2、Fig. 4).

Functional inference of TCN1 in LUAD

GO term annotation indicated that co-expressed genes of TCN1 join mainly in epidermal cell differentiation, keratinization, keratinocyte differentiation, cornification, apical part of cell, endopeptidase regulator activity, peptidase inhibitor activity, endopeptidase inhibitor activity, serine-type endopeptidase inhibitor activity and golgi lumen, etc (Fig. 5a). The KEGG pathway analysis showed enrichment in retinol metabolism, neuroactive ligand-receptor interaction, chemical carcinogenesis, drug metabolism-cytochrome P450, etc (Fig. 5b).

GSEA identifies a TCN1-related signaling pathway

Signaling pathways associated with LUAD were identified using GSEA. The results showed that Epithelial mesenchymal transition and TNFA signaling via NFKB were differentially enriched in the positively correlated with TCN1 expression phenotype (Table 3, Fig. 6).

Creating protein interaction networks

Functional interactions between proteins are essential for the cancer metabolism and molecular mechanisms. Consequently, STRING was used to analyze the TCN1 protein PPI network in order to identify their interactions in LUAD progression. Using text mining and experimental evidence identification, the TCN1-binding protein interaction network was visualized (Fig. 7a). Moreover, by comparing TCN1-interacted genes with TCN1 expression-correlated DEGs, a common member was screened out such as SERPINA4, GC, and SULT1C3 (Fig. 7b). Furthermore, there was a remarkable positive association between TCN1 expression and that of SERPINA4 (r = 0.300, P < 0.001), GC (r = 0.170, P< 0.001), and SULT1C3 (r = 0.090, P = 0.038) (Fig. 7c).

TCN1 expression correlates with immune infiltration levels

Infiltration of several types of immune cells was positively correlated with TCN1 expression, including neutrophils (r = 0.180, P < 0.001), eosinophils (r = 0.120, P = 0.005), mast cells (r = 0.110, P = 0.010), CD56bright cells (r = 0.110, P = 0.012), NK cells (r = 0.110, P = 0.015), macrophages (r = 0.100, P = 0.020) and aDC (r = 0.090, P = 0.037), while negatively correlated with the infiltration of T helper cells (r = −0.090, P = 0.037) in LUAD (Fig. 8). Furthermore, we used TISIDB to examine the correlations between TCN1 expression and TILs. As shown in Fig. 9a, in different cancer types, TCN1 expression has been associated with TILs. Specifically, in LUAD, Multiple types of TILs were significantly correlated with TCN1 expression (Fig. 9b). A new strategy for tumor immunotherapy, immune checkpoint inhibitors (ICIs), Patients with many types of cancer are already benefiting from it [11]. Following this, we compared the expression of TCN1 with over 40 other genes associated with immune control. Accordingly, the expression of TCN1 in LUAD was correlated with over 18 immune checkpoint markers, including CD276, CD44, HHLA2, LGALS9 and so on (Fig. 10a). In order to understand the association between TCN1 expression and immune cell migration, we examined chemokines and chemokine receptors (Fig. 10b). According to the study results, there was a positive correlation between TCN1 expression and immune cell chemokine and receptor expression, such as CXCL1 (r = 0.144, P = 0.001), CXCL5 (r = 0.172, P = 8.97e−05), CXCL6 (r = 0.161, P = 2.38e−04), CXCL14 (r = 0.176, P = 5.63e−05) and CX3CL1 (r = 0.162, P = 2.20e−04). Considering that the expression of TCN1 increases the expression of these chemokines and chemokine receptors, high TCN1 expression may facilitate the migratory ability of immune cells.

While lung cancer therapy has improved, it remains one of the most malignant types of cancer with a poor five-year survival rate[12]. We urgently need to identify new biomarkers and investigate molecular mechanisms. As cancer immunotherapy has shifted paradigms in recent years, it is gradually being recognized as a promising strategy for treating some cancers[13]. The current research on immunotherapeutic strategies for lung adenocarcinoma is mainly focused on chemoradiotherapy, cancer vaccines, immune checkpoint inhibitors, and combinations with other immunotherapeutic agents, and other molecularly targeted agents. The immunotherapy field has increasingly focused on immune checkpoint inhibitors (ICIs) therapy, which includes anti-PD-1 and anti-PD-L1 antibody drugs, which have evident advantages in application across cancer types and excellent clinical effectiveness[14]. However, only some patients respond well to this treatment. The key now is to discover a new biomarker that is associated with immune infiltrates and molecular mechanisms that may be responsible for immunotherapy's effectiveness.

As little research has been done on cancer and the TCN1 gene, we decided to perform an advanced bioinformatic analysis to investigate its potential regulatory mechanisms and biological functions in LUAD. TCN1(a member of the vitamin B12-binding protein family) is a 60-70 kDa molecular weight protein, derived from the granulocyte line[15]. TCN1, intrinsic factor and TCN2 are involved in vitamin B12 homeostasis. A higher serum level of TCN1 and vitamin B12 has been reported in cancer patients[16]. Furthermore, it has been well established that high serum levels of vitamin B12 are associated with malignant hematological diseases such as chronic acute leukemia and myeloid leukemia, which are caused by granulocyte proliferation releasing TCN1[15, 17]. The expression of TCN1 was also reported to be increased in hepatocellular carcinoma and immunodeficiency virus (HIV) seropositive patients[18, 19]. Also, There was a better correlation between TCN1 and progression of gastric cancer than vitamin B12[20]. Liu et al recently identified TCN1 expression can predict neoadjuvant chemosensitivity and is a negative prognostic biomarker in colon cancer [6]. Wang et al found that low TCN1 expression might be a potential prognostic biomarker for predicting neoadjuvant chemotherapy sensitivity and clinical outcome in local advanced hypopharyngeal squamous cell carcinoma patients[7]. In this study, we analyzed TCN1 differential expression, diagnostic value, prognostic value, and we also analyzed the association with tumor immune infiltration in LUAD. We found that TCN1 expression was highly elevated in LUAD, compared to normal tissues. According to our findings TCN1 may play a vital role in LUAD development. Our findings are in accordance with previous research regarding the TCN1 role in other cancers. We found that the expression of TCN1 had certain diagnostic value in different stages of LUAD by ROC curve. Survival analysis revealed that high TCN1 expression was correlated with poor overall survival in LUAD. Our results strongly indicate that TCN1 can be used as a prognostic and diagnostic biomarker for LUAD.

Furthermore, TCN1 expression is associated with a variety of immune checkpoint markers and immune cells. According to previous studies, the immune microenvironment is crucial to tumor growth and development. For growth and metastasis, tumor cells are dependent upon a complex tumor microenvironment (TME). Tumor cells are involved in the manipulation of TME components and the progression of tumors from all stages of tumorigenesis [21]. Tumor-infiltrating immune cells, such as CD8+ T cells, CD4+ T cells, B cells, tumor associated macrophages (TAMs), are main components of the lung cancer microenvironment[22-24]. The GSVA package of R was used to observe the tumor-infiltrating immune cell levels in relation to TCN1 expression in order to explore its possible role in the immune system. Our results show that the expression of TCN1 correlates with multiple immune markers and immune infiltration levels in LUAD. In addition, the study suggests that TCN1 expression is closely related with tcm_CD4, CD56bright and neutrophil in LUAD. Furthermore, according to our study, TCN1 and CD276 have a close positive relationship, which was consistent with the prognostic value of TCN1. CD276, which is shown to be expressed on multiple tumor cell lines, tumor-infiltrating dendritic cells, and macrophages, inhibits autoimmunity and T-cell activation[25].Wang et al recently identified CD276 is highly expressed in squamous cell carcinoma of the head and neck and acts as an immune checkpoint to enable cancer stem cells to evade surveillance by the immune system. Blocking CD276 can effectively enhance the T-cell-mediated anti-tumor immunity, thus eliminating cancer stem cells and preventing the growth and spread of cancer[26]. Therefore, TCN1 and CD276 interaction may explain the association between TCN1 expression, immune infiltration, and poor prognosis in LUAD. For complete elucidation of the biological function of TCN1 in LUAD, further basic and clinical experiments are needed.

Date

We investigated the TCN1 expression level in various types of normal tissues and tumor in the integrated datasets combined TCGA[27] (https://portal.gdc.cancer.gov) with GTEx (Genotype-Tissue Expression) database (https://www.gtexportal.org/ home/-index.html). TCGA are open-ended, public and do not need the approval of a local ethics committee. We obtained the Profiles of RNA expression (RNA-Seq2 level 3 data; format: TPM; platform: Illumina HiSeq 2000) and clinical sample of LUAD patients from TCGA database. TCGA included 535 LUAD samples and 59 normal lung tissue samples, which containing general information, clinicopathological details and prognostic information. The gene expression profiling data sets (GSE10072, GSE116959, GSE75037, GSE32863) were obtained from GEO database (https://www.ncbi.nlm.nih.gov/gds).

Survival analysis

Based on high and low levels of TCN1 expression, patients were separated into two groups. Based on Kaplan-Meier (KM) survival curves, we constructed a prognostic classifier to determine if TCN1 expression level affects clinical outcomes of LUAD patients.

Analyses of univariate and multivariate logistic regression

Based on univariate Cox regression analysis, we compared the OS of two cohorts of patients with LUAD and the level of TCN1 expression. Furthermore, we used multivariate analysis to determine whether TCN1 is an independent prognostic marker for OS of LUAD patients. There is statistical significance for the TCN1 in Cox regression analysis, when p value < 0.05.

Functional enrichment analysis

Using the limma package, we explored mRNA differential expression. False positive results were corrected using the adjusted P value. The screening thresholds for differentially expressed genes were defined as follows: adjusted P < 0.05 and |log2 (Fold Change) | > 1 (DEGs). GO (Gene ontology) and KEGG (Kyoto encyclopedia of genes and genomes) analyses were conducted using clusterprofiler package to better understand TCN1 carcinogenesis. The ggplot2 package was employed for visualization. Adjusted P < 0.05 is considered to be statistically significant in the enrichment results.

Gene set enrichment analysis

TCN1 mRNA expression was analyzed by R (version 3.6.3), followed by GSEA analysis using clusterprofiler and ggplot2 package. P < 0.05, |ES|>1, and FDR < 0.25 were considered to be statistically significant.

Protein protein interaction comprehensive analysis

We used the online tool STRING[28] (Search Tool for the Retrieval of Interacting Genes/Proteins) website (https://string-db.org) to investigate the protein–protein interaction (PPI) of TCN1-binding proteins. This tool included a large collection of consolidated and integrated data on protein-protein interactions. The main settings such as active interaction sources (“textmining and experiments”), meaning of network edges (“evidence”), minimum required interaction score [“Low confidence (0.400)”], and max number of interactors to show (“no more than 50 interactors”) operated. Then, we obtained the interaction networks for 50 TCN1-binding proteins. The Venn diagram was generated with R package VennDiagram to compare the TCN1 express-related genes interacting with DEGs and TCN1. The expression of TCN1 and the genes from intersection analysis were correlated using Spearman's correlation analysis. P < 0.05 was considered to be statistically significant.

TCN1 expression and immune Infiltrates

The massively related immune cells infiltrating tumor tissue was assessed by ssGSEA (single-sample gene set enrichment analysis). We used the GSVA package of R and immune datasets to determine the infiltration level of immune cells in LUAD. The TISIDB[29] database (http://cis.hku.hk/TISIDB) is an integrated repository tool, has a significant impact in finding the interaction between tumor and immune system. With this tool, we will further explore the immune correlates of TCN1 in cancer. First, correlations between TCN1 expression and TILs across multiple types of cancer were determined via the "lymphocyte" module. Secondly, Statistical assessment was performed using a Spearman correlation to search and screen the appropriate candidates.

In addition, the correlation between TCN1 expression and immune checkpoint gene levels were analyzed using spearman correlation analysis. Based on the TISIDB database, we used the "chemokine" module to investigate the relationship to determine chemokines/chemokine receptors between TCN1 and immune cell migration.

Statistical analysis

We conducted all statistical analyses by R (3.6.3). We generated ROC curves using pROC package. We used chi-square test to examine the association between TCN1 mRNA expression and clinical characteristics. The prognostic value of TCN1 mRNA expression was analyzed by multivariate Cox analysis and Kaplan-Meier analysis. P < 0.05 represents statistical significance.

TCN1:Transcobalamin; LUAD:Lung adenocarcinoma; OS:overall survival; NSCLC:Non-small cell lungcancer; GTEx:Genotype-tissue expression; GO:Gene ontology; KEGG:Kyoto encyclopedia of genes andgenomes; STRING:Search tool for the retrieval of interacting genes/proteins; PPI:protein–proteininteraction; TCGA:The cancer genome atlas; KM:Kaplan-Meier; ssGSEA:single-sample gene set enrichment analysis.

Acknowledgements

We acknowledge the GEO, TCGA, GTEx, STRING and TISIDB databases for free use.

Funding

Not applicable.

Availability of data and materials

The data sets during and/or analysed during the current study available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Authors’ contributions

HL was involved in data curation, data analysis, investigation, methodology, and writing; LG, ZC were involved in project administration, supervision, and writing—review.

Author details

Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, No.215 Heping West Road, Shijiazhuang, China

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Table 1 Clinical characteristics of the LUAD patients

Characteristic	levels	Overall
n		535
Age, n (%)	<=65	255 (49.4%)
	>65	261 (50.6%)
Gender, n (%)	Female	286 (53.5%)
	Male	249 (46.5%)
T stage, n (%)	T1	175 (32.9%)
	T2	289 (54.3%)
	T3	49 (9.2%)
	T4	19 (3.6%)
N stage, n (%)	N0	348 (67.1%)
	N1	95 (18.3%)
	N2	74 (14.3%)
	N3	2 (0.4%)
M stage, n (%)	M0	361 (93.5%)
	M1	25 (6.5%)
Pathologic stage, n (%)	Stage I	294 (55.8%)
	Stage II	123 (23.3%)
	Stage III	84 (15.9%)
	Stage IV	26 (4.9%)
Smoker, n (%)	No	75 (14.4%)
	Yes	446 (85.6%)

Table 2 Correlations between OS and mRNA expression of TCN1 analyzed by univariate and multivariate Cox regression

Characteristics	Total(N)	Univariate analysis		Multivariate analysis
Characteristics	Total(N)	Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
Age	516	1.223 (0.916-1.635)	0.172
Gender	526	1.070 (0.803-1.426)	0.642
Pathologic stage	518	2.664 (1.960-3.621)	<0.001	1.859 (1.192-2.898)	0.006
T stage	523	1.728 (1.229-2.431)	0.002	1.753 (1.122-2.736)	0.014
N stage	510	2.601 (1.944-3.480)	<0.001	1.847 (1.248-2.735)	0.002
M stage	377	2.136 (1.248-3.653)	0.006	1.132 (0.598-2.142)	0.703
TCN1	526	1.669 (1.249-2.231)	<0.001	1.548 (1.106-2.166)	0.011

Due to technical limitations, Table 3 is only available as a download in the Supplemental Files section.

No competing interests reported.

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TCN1 is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma

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