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Screening and evaluation of the role of immune genes of brain metastasis in lung adenocarcinoma progression based on the TCGA and GEO databases

Background Brain metastasis was one of the factors leading to the poor long-term prognosis of patients with lung adenocarcinoma (LUAD). Methods The expression levels of immune genes in LUAD and LUAD brain metastases tissues were analyzed in GSE161116 dataset using the GEO2R, and the levels of differential immune genes in normal lung and LUAD tissues were verified. The biological functions and signaling mechanisms of the differential immune genes were explored via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Cox regression analysis was used to screen the prognostic factors of LUAD patients, and a risk model was constructed. The role of the model was checked in the development of LUAD via receiver operating characteristic analysis, gene set enrichment analysis, and Cox regression analysis. Results Differentially expressed genes (DEGs) in brain metastasis were involved in the adaptive immune response, B cell differentiation, leukocyte migration, NF-kB signaling pathway, among others. The expression levels of TNFRSF11A, MS4A2, IL11, CAMP, MS4A1, and F2RL1 were independent factors affecting the poor prognosis of LUAD patients via Cox regression analysis and Akaike information criterion. In the constructed risk model, the overall survival of LUAD patients in the high-risk group was poor. The risk model was significantly related to the gender, clinical stage, T stage, lymph node metastasis, and survival status of LUAD patients. In addition, the risk model score was an independent risk factor that affected the poor prognosis of LUAD patients. TNFRSF11A, CAMP, F2RL1, IL11, MS4A1, and MS4A2 of the risk factors had diagnostic significance in LUAD brain metastasis and LUAD. The risk model participated in cytokinetic process, cell cycle, citrate cycle TCA cycle, etc. The risk model score was correlated with the levels of B cells memory, mast cells resting, macrophages M0, mast cells activated, neutrophils, eosinophils, T cells gamma delta, and immune cell markers. Conclusions The risk model based on the LUAD brain metastasis immune factors TNFRSF11A, MS4A2, IL11, CAMP, MS4A1, and F2RL1 was related to the diagnosis, poor prognosis, and immune infiltrating cells of LUAD patients, and is expected to provide a reference for the development of treatment strategies for LUAD patients.


Introduction
In recent years, cancer immunotherapy has been applied in the treatment of a variety of cancer patients, which has improved the prognosis of patients and has had a profound impact on the current treatment patterns (1)(2)(3)(4)(5)(6). For example, Liu et al. reported that the overall response rate of patients was 77.8% (N=7/9), and the complete remission rate was 55.6% (N=5/9) with CD19-CAR T cell treatment of B-cell lymphoma (BCL). The CD19-CAR T cells were safe and effective in the treatment of patients with refractory and relapsed BCL (1). Buderath et al. reported that PD-1 -positive ovarian cancer (OC) infiltrating immune cells were associated with long overall survival (OS). PD-1and PD-L1-positive OC infiltrating immune cells were related to lymph node metastasis and tumor grade (2). These findings indicate that immunotherapy for cancer patients has shown great therapeutic value, and is also a hotspot in current research. However, immunotherapy is effective for few cancer patients. Therefore, the development of new immunotherapy targets would improve the prognosis of cancer patients, highlighting the urgent need for further research.
In addition, a variety of immune cells and abnormal expression of immune genes are involved in the process of disease progression (7)(8)(9)(10). For instance, fingolimod therapy could reduce CXCR4 expression and B cell migration. B cells of patients treated with fingolimod could induce the decrease of T cell pro-inflammatory cytokines, and increase the expression of transforming growth factor beta (TGFβ) B and T cells (7). TGF-β1 stimulation led to the up-regulation of L1CAM expression in a colonic intestinal epithelial cell line, NCM460, which enhanced cell motility and resistance to apoptosis. The expression of L1CAM in inflammatory bowel disease (IBD) patients was elevated.
The expression level of L1CAM increased with the duration of the disease and the presence of CD33 + macrophages. In co-cultured NCM460 cells, inhibiting the TGF-β1 signaling pathway could eliminate the expression of Slug and L1CAM, which resulted in decreased cell migration and apoptosis resistance (8). However, the characteristics of the tumor immune microenvironment and immune factors in lung adenocarcinoma (LUAD) progression have not been fully revealed. Song et al. reported on the expression profile of immune factors in the primary tumors and brain metastases of non-small cell lung cancer (11), then the role and regulation mechanism of immune microenvironment molecules were identified in the progression of LUAD via the data of Song et al. and LUAD gene expression profiles from the Cancer Genome Atlas (TCGA) database. We present the following article in accordance with the STARD reporting checklist (available at https://dx.doi.org/10.21037/ jtd-21-935).

Differentially expressed immune genes
There were 9 LUAD and LUAD brain metastases tissues in the Gene Expression Omnibus (GEO) database GSE161116 dataset ( Figure S1A). Among them, there were 10 EGFR mutant and 8 wild-type patients ( Figure S1B,C). There were 11 female patients and 7 male patients ( Figure S1D). The expression levels of immune genes involved in LUAD brain metastasis were analyzed in the GSE161116 dataset using the GEO2R and screening criteria: adjusted P < 0.05, and log (fold change) >1 or <−1. In addition, we downloaded the transcriptome data of the type HTSeq-FPKM on the TCGA website. Among them, there were 59 normal lung tissues and 535 LUAD tissues. The expression data of brain metastasis immune genes of the GSE161116 dataset were extracted in the TCGA database. The expression levels of these immune genes were verified via the R limma package in normal lung and LUAD tissues. Screening criteria: adjusted P<0.05, and log (fold change) >1 or <−1. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Biological functions and signaling mechanisms
Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis have often been used to explore the biological functions and signaling mechanisms of multiple genes. The Metascape website contains an online website with GO annotation and KEGG analysis. Metascape was used to analyze the biological functions and signaling mechanisms involved in the differentially expressed immune genes of brain metastasis in LUAD tissues. Screening criteria: race type was human, and P<0.05.

The protein and protein interaction (PPI) network
The PPI network showed the possible interaction relationship between the 2 genes. The String database was used to construct the relationships between the differentially expressed immune proteins of brain metastasis in LUAD tissue, and the screening criterion was a combined score >0.4. Cytoscape software was used to display the interaction network between the differentially expressed immune proteins of brain metastasis, and the CytoHubba plug-in maximal clique centrality (MCC) score was used to screen the hub genes in the PPI network.

Risk model
The clinical data of 522 LUAD patients were obtained from the TCGA database (available online: https://cdn. amegroups.cn/static/public/jtd-21-935-01.pdf), and the differentially expressed immune gene expression levels of brain metastasis were sorted and merged with the clinical prognostic data of LUAD patients. Univariate Cox regression analysis was used to evaluate the prognostic genes of LUAD patients, with a screening criterion of P<0.05. On this basis, multivariate Cox regression analysis and Akaike information criterion (AIC) screened the independent genes affecting the prognosis of LUAD, and a risk model was constructed.

Identification of independent prognostic genes
The GEO GSE161116 dataset of 9 LUAD and 9 LUAD brain metastasis samples and GPL19965 annotation information were downloaded, and the expression levels of the prognostic genes were analyzed in LUAD and LUAD brain metastasis tissues by gender and EGFR mutation groups. The expression levels of the prognostic genes were explored in normal lung and LUAD tissues from the TCGA database. The diagnostic value of prognostic genes was identified in LUAD and LUAD brain metastasis via receiver operating characteristic (ROC) analysis. The value of prognostic genes in the prognosis of LUAD patients was verified in the PrognoScan database.

The clinical value of the risk model in LUAD
Kaplan-Meier (K-M) survival analysis and heat map visualization showed the relationship between highand low-risk groups and risk genes, as well as the poor prognosis and clinicopathological characteristics of LUAD patients. In addition, univariate and multivariate Cox regression analyses were performed to evaluate the impact of high-and low-risk scores on the prognosis of LUAD patients.

Gene set enrichment analysis (GSEA)
GSEA was a method of analyzing gene expression information. In our study, we divided the gene expression data of 535 LUAD patients from the TCGA database into high-and low-risk groups based on the median expression values of risk scores, and the biological processes (BP) and signaling mechanisms involved in the high-risk groups were explored using GSEA. The enrichment was performed with 1,000 cycles, and nominal (NOM ) P<0.05 was the screening criterion.

The value of the risk model in the LUAD microenvironment
The scores of immune cells and the microenvironment were calculated in the tissues of 535 LUAD patients with the TCGA database via CIBERSORT. The scores of the risk model were sorted and merged with the immune cells and immune cell marker data of LUAD patients. Pearson correlation analysis was used to analyze the relationship between the risk model scores and the immune cells and immune cell markers of LUAD patients. The screening criterion was P<0.05.

Statistical analysis
The differentially expressed genes of brain metastases were analyzed in the GSE161116 dataset via the GEO2R analysis, and the gene expression level was identified in the LUAD tissue of the TCGA database via the use the R limma package. Univariate and multivariate Cox regression analysis screened the factors affecting the prognosis of LUAD, and constructed the risk model. ROC analysis was used to determine the diagnostic value of prognostic genes in LUAD and LUAD brain metastases. The relationship between risk model scores and LUAD immune cells and immune cell markers were explored via the Pearson correlation analysis. P<0.05 was regarded as a sign of statistical significance.

The biological functions, signaling mechanisms, and the PPI network involved in the differentially expressed immune genes of LUAD brain metastasis
GO annotation showed that 97 differentially expressed immune genes of brain metastasis were involved in the adaptive immune response, B cell differentiation, myeloid leukocyte differentiation, cellular extravasation, positive regulation of leukocyte migration, interleukin-12 production, activation of immune response, regulation of inflammatory response, regulation of cytokine production, leukocyte migration, leukocyte activation involved in immune response, and leukocyte differentiation, among others ( Figure 3A,B). KEGG pathway analysis found that these genes might be involved in the regulation of cellular senescence, c-type lectin receptor signaling pathway, renin-angiotensin system, natural killer cell mediated cytotoxicity, primary immunodeficiency, complement and coagulation cascades, cell adhesion molecules (CAMs), NF-kappa B (NF-kB) signaling pathway, and other mechanisms ( Figure 3C,D). Figure S2 shows the relationships between the 97 differentially expressed immune proteins of brain metastasis. The hub genes in the PPI network were FOXP3, CTLA4, IL6, CD19, IFNG, CD27, PRF1, IL7R, TNFRSF9, and TNFSF4 ( Figure S2B and Table 2).

The clinical role of the risk model
K-M survival analysis showed that LUAD patients in the high-risk group had shorter OS ( Figure 4B). Figure  4C,D,E shows the relationships between high-and lowrisk groups and risk genes and the prognosis of LUAD   patients. In addition, the risk score was significantly related to the gender, clinical stage, T stage, lymph node metastasis, and survival status of LUAD patients ( Figure  5A). Univariate and multivariate Cox regression analyses showed that the risk model score was an independent risk factor that affected the poor prognosis of LUAD patients ( Figure 5B,C).
In the TCGA database, compared with normal lung tissues, the expression levels of IL11, TNFRSF11A, F2RL1, and MS4A1 in LUAD tissues were significantly increased, and the expression levels of CAMP and MS4A2 in LUAD tissues were significantly decreased ( Figure 6G,H,I,J,K,L).
Subtype analysis showed that the expression levels of MS4A1 were significantly decreased, and the expression level of MS4A2 was significantly increased in EGFR wildtype LUAD brain metastasis tissues. The expression levels of IL11 and MS4A2 were significantly decreased, and the expression level of TNFRSF11A was significantly increased in EGFR-mutant LUAD brain metastasis tissues. The    Table 5).

The risk model was related to the LUAD immune microenvironment
Correlation analysis revealed that the risk score was significantly correlated with LUAD B cells memory, mast cells resting, macrophages M0, mast cells activated, neutrophils, eosinophils, and T cells gamma delta levels ( Figure 9). Further analysis found that the risk score was  Figure 10 and Table 6).

Discussion
Lung cancer has a high recurrence rate and distant metastasis rate, threatening human health. LUAD is one of the common subtypes of lung cancer. In the process of LUAD metastasis, genes, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs), among others, play roles as carcinogenic or tumor suppressor factors (12)(13)(14)(15)(16). For example, the up-regulation of polypeptide N-acetylgalacto saminyltransferase 6 (GALNT6) expression was associated with LUAD lymph node metastasis and poor prognosis. In LUAD cells, overexpression of GALNT6 could promote epithelial-mesenchymal transition (EMT), migration, and invasion, while the results of GALNT6 silencing were significantly the opposite. Silencing GALNT6 could prolong the survival time of xenograft mice (12). Circ-CAMK2A was overexpressed in LUAD, and the high expression level of CAMK2A was closely related to the lymph node metastasis, distant metastasis, advanced clinical stage, and poor prognosis of LUAD patients. Silencing of CAMK2A inhibited the migration and invasion of LUAD cells, while overexpression of CAMK2A had the opposite effect. Furthermore, overexpression of CAMK2A enhanced LUAD metastasis in vivo. CAMK2A could up- regulate the expression of fibronectin 1 by sponging miR-615-5p, thereby increasing the expression of MMP2 and MMP9 to promote LUAD metastasis (13). Mex3a was significantly up-regulated in LUAD tissues, and increased expression of Mex3a was related to the poor prognosis and metastasis of LUAD. Interference with the expression of Mex3a significantly inhibited the migration, invasion, and metastasis of LUAD cells. Inhibition of LAMA2 expression could partly reverse the decrease in Mex3a expression and cause cell migration and invasion. Mex3a and LAMA2 could affect the PI3K-AKT signaling pathway to participate in LUAD metastasis (14). In this study, the GSE161116 dataset was used to analyze the differentially expressed immune genes involved in the process of LUAD brain metastasis, and the TCGA database verified that 97 immune genes were differentially expressed in both LUAD tissue and LUAD brain metastasis tissue. Immune genes of LUAD brain metastasis were involved in the adaptive immune response, B cell differentiation, myeloid leukocyte differentiation, cellular extravasation, positive regulation of leukocyte migration, interleukin-12 production, activation of immune response, regulation of inflammatory response, regulation of cytokine production, leukocyte migration, leukocyte activation involved in immune response, leukocyte differentiation, and other biological functions, and might be involved in the regulation of cellular senescence, c-type lectin receptor signaling pathway, natural killer cell mediated cytotoxicity, CAMs, primary immunodeficiency, and NF-κB signaling pathway, among others. Studies have reported that the adaptive immune response, the NF-κB signaling pathway, and the IL12 signaling mechanism, among others, play important roles in the progression of LUAD (17)(18)(19)(20). For example, IL33 inhibited the growth of LUAD by regulating dendritic cells (DCs). Knockdown of CYLD expression in DC cells could inhibit NF-κB signaling transduction and inhibit the proliferation and function of CD4 T cells induced by DCs (17). There was also a negative correlation between WNT1 and T cell abundance. Abnormal expression of WNT1 could affect the growth of LUAD, which was related to the level of DCs and T cells.    gene was up-regulated in DCs, and the level of WNT1 target gene decreased when WNT1 was silenced, accompanied by enhanced T cell toxicity (20). These findings indicate that these genes play important roles in the occurrence and metastasis of LUAD. The risk score model was a common method to predict the poor prognosis of cancer patients (21,22). A risk model was constructed through Cox regression analysis and AIC screening. K-M survival analysis showed that LUAD patients in the high-risk group had poorer OS. The risk score was significantly related to the gender, clinical stage, T stage, lymph node metastasis, and survival status of LUAD patients, and was an independent risk factor affecting the poor prognosis of LUAD patients. Among them, our risk model genes were TNFRSF11A, MS4A2, IL11, CAMP, MS4A1, and F2RL1. ROC analysis showed that the AUCs of TNFRSF11A, CAMP, F2RL1, IL11, MS4A1, and MS4A2 in the diagnosis of LUAD brain metastasis and LUAD were all greater than 0.5, and were statistically significant. K-M survival analysis showed that TNFRSF11A, MS4A2, IL11, CAMP, MS4A1, and F2RL1 were related to the prognosis of LUAD patients. Currently, studies have found that miR-3150b-3p was downregulated in cervical cancer cells. MiR-3150b-3p attenuated the proliferation, migration, and invasion of HeLa and SiHa cells. Overexpression of TNFRSF11A could eliminate the antitumor effect of miR-3150b-3p (23). Furthermore, IL11 was upregulated in endometrial cancer, and in AN3CA cells, IL11 could cause cell adhesion and migration through STAT3 (24). Protease-activated receptor 2 (PAR) is also known as F2RL1. F2RL1 activation could promote cancer cell migration. F2RL1 activation could also reduce the expression level of miR-205. MiR-205 inhibition promoted cancer cell migration, while overexpression of miR-205 prevented PAR-mediated cell migration (25). Recently, IL11 was shown to promote A549 cell growth and EMT, which might be related to the activation of the STAT3/HIF-1α/EMT signaling pathway (26). The same genes could play different roles in different cancers. For example, TNFRSF11A was overexpressed in colorectal cancer (CRC) tissues. TNFRSF11A overexpression was positively correlated with metastasis, high TNM stage, and poor prognosis of CRC patients. Overexpression of TNFRSF11A could promote CRC cell migration and invasion in vitro and in vivo (27). In glioma cells, overexpression of TNFRSF11A could lead to reduced lesion formation and increased apoptotic activity (28). Therefore, the roles of TNFRSF11A, MS4A2, CAMP, MS4A1, and F2RL1 in LUAD progression need further verification.
Research by Song et al. showed that the immune microenvironment and its molecular abnormalities were involved in NSCLC brain metastasis (11,29). A total of 535 LUAD patients in the TCGA were scored on immune cells and the microenvironment, and it was found that the risk model score was significantly correlated with LUAD B cells memory, mast cells resting, macrophages M0, mast cells activated, neutrophils, eosinophils, and T cells gamma delta levels. In addition, the risk model score was correlated with the levels of immune cell markers CCR7, CD48,     In this study, the commonly databases of GEO and TCGA were used to screen and evaluate the roles of LUAD brain metastasis-related immune genes in the cancer progression, and the risk model was constructed to evaluate the prognosis of patients. Our research had the advantages of a wide variety of methods and a large sample size. However, our research had the certain limitations. The GEO dataset lacked prognostic data, and could not show the value of brain metastasis-related immune target molecules in the prognosis of patients with EGFR mutation. In addition, our research results needed to be confirmed by collecting LUAD tissue specimens. In general, the expression levels of TNFRSF11A, MS4A2, IL11, CAMP, MS4A1, and F2RL1 were independent factors affecting the poor prognosis of LUAD patients. The risk model was significantly related to the gender, clinical stage, T stage, lymph node metastasis, and survival time of LUAD patients. The risk genes TNFRSF11A, CAMP, F2RL1, IL11, MS4A1, and MS4A2 had diagnostic value in the diagnosis of LUAD brain metastasis and LUAD. The risk model participated in the regulation of translational initiation in response to stress, cytokinetic process, and cell cycle, among others. The risk model score was significantly correlated with the levels of LUAD immune cells. However, these research results need to be further confirmed by basic experiments. In general, our risk model is expected to provide a reference for the development of treatment strategies for LUAD patients.