A retraction of this article was approved in:
Retraction: Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles
Bo Ling
Guangbin Ye1,2†
Qianli Tang- 1College of Pharmacy, Youjiang Medical University for Nationalities, Baise, China
- 2Medical College of Guangxi University, Nanning, China
- 3Key Laboratory of High Incidence of Disease Prevention in the West of Guangxi, Youjiang Medical University for Nationalities, Baise, China
Background: Lung cancer is one of the most common types of cancer, and it has a poor prognosis. It is urgent to identify prognostic biomarkers to guide therapy.
Methods: The immune gene expression profiles for patients with lung adenocarcinomas (LUADs) were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The relationships between the expression of 45 immune checkpoint genes (ICGs) and prognosis were analyzed. Additionally, the correlations between the expression of 45 biomarkers and immunotherapy biomarkers, including tumor mutation burden (TMB), mismatch repair defects, neoantigens, and others, were identified. Ultimately, prognostic ICGs were combined to determine immune subgroups, and the prognostic differences between these subgroups were identified in LUAD.
Results: A total of 11 and nine ICGs closely related to prognosis were obtained from the GEO and TCGA databases, respectively. CD200R1 expression had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68, and GZMB, indicating that it may cause the disordered expression of adaptive immune resistance pathway genes. Multivariable Cox regression was used to construct a signature composed of four prognostic ICGs (IDO1, CD274, CTLA4, and CD200R1): Risk Score = −0.002*IDO1+0.031*CD274−0.069*CTLA4−0.517*CD200R1. The median Risk Score was used to classify the samples for the high- and low-risk groups. We observed significant differences between groups in the training, testing, and external validation cohorts.
Conclusion: Our research provides a method of integrating ICG expression profiles and clinical prognosis information to predict lung cancer prognosis, which will provide a unique reference for gene immunotherapy for LUAD.
Background
Lung cancer accounts for more than one-tenth of all cancer cases worldwide and is one of the most common types of cancer (Ferlay et al., 2015), with smoking being a major risk factor. Exposure to high levels of carcinogens in tobacco smoke can induce the accumulation of mutation load in lung cells (Cataldo et al., 2010; Pedersen et al., 2016). Lung cancer prognosis is rather poor, with a 5-year survival rate of 18% (Parascandola and Xiao, 2019). Prognosis is related to patients' clinicopathological characteristics, and the early diagnosis rate is low. Because of this, the formation and pathological grade of lung cancer cannot be accurately judged, thus limiting operation strategies (Ergonul et al., 2017). By the time of diagnosis, most patients already have extensive metastasis. The main therapeutic strategy aims to reduce the malignant phenotype by suppressing the oncogenic pathway. Although the response to chemotherapy and radiotherapy in the early stage is relatively good, this treatment often leads to drug resistance during further treatment. Because of this effect, treatment strategies for lung cancer are relatively limited (Nakaoku and Kohno, 2017; Zhang et al., 2018).
Genome sequencing technologies and data have deepened our understanding of lung cancer development, cellular heterogeneity, and interactions at precise molecular interactions (Xu et al., 2016), and immunotherapy developments have shown great potential in treating various malignant tumors (Turner, 2017). At present, monoclonal antibody-targeted drugs are leading to innovations in the treatment of lung cancer. Most notably, immunotherapy with programmed cell death protein 1 (PD-1) and CD274 as targets has achieved significant results in clinical trials of lung cancer treatment (Galetta et al., 2017; Choy et al., 2019). Currently, these targeted drugs can be used as first or second-line drugs for lung cancer since more than 50% of tumor cells in patients express programmed death-ligand 1 (PD-L1) (Ru and Zhuang, 2018). However, this therapy does not work for all patients due to dynamic and isomeric expression implications, which suggest that other therapeutic targets are needed for specific patients (Merelli et al., 2014; Kasahara et al., 2019). On the whole, the effect of most targeted monoclonal antibodies is still limited among lung cancer patients. While encouraging responses are observed in some patients, many others do not show the same success (Wang et al., 2017). This indicates that not all patients demonstrate equal sensitivity to therapeutic targets because of tumor heterogeneity. Since established immunotherapy is not effective for all patients, it is necessary to further differentiate tumor immune subtypes to improve clinical trial design and identify patients who may benefit from other types of immunotherapy.
Traditional cancer research usually focuses on the role of a particular target gene in cancer development (Huang et al., 2019). However, each case of cancer is complex and involves the abnormality of multiple genes and signaling pathways. Therefore, studying the role of a single gene has its limitations (Wang and Xia, 2019). At present, biological studies have entered the multi-omics era, which opens the possibility of understanding and studying tumors at the genome level. The establishment of large cancer databases, such as The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), enables researchers to obtain the gene expression data of multiple immune checkpoints and patients' corresponding clinical data (Tomczak et al., 2015; Zhang et al., 2019). On this basis, integrated analysis can divide different immune subtypes in lung cancer samples in more detail and guide individualized treatment (Yuan et al., 2015; Hanke, 2016). Most existing reports related to lung cancer immunotherapy are drug design studies that use a single immune checkpoint gene (ICG) as a target. There is still a lack of research on gene expression profiles involving multiple immune checkpoints (Sadreddini et al., 2018; Duffy and John, 2019; Long et al., 2019). Therefore, based on the gene expression data of 47 immune checkpoints, such as PD-1 (PDCD1), PD-L1 (CD274), and IDO1, the immune subgroups and prognoses of 761 lung cancer samples from the TCGA and GEO databases were identified.
In our research, the relationships between the expression of 45 ICGs and prognosis were analyzed. Additionally, the relationships between ICGs and immunotherapy biomarkers, such as mismatch repair (MMR) defects and tumor mutation burden (TMB), were studied using integrated cell mutation data. This was done to determine the relationships between the expression of ICGs (PD1, PD-L1, and CTLA4) and biomarkers that have and have not been widely used in immunotherapy. Ultimately, the relationship between ICG expression and immune activation-related signature genes was studied to determine the relationship between immune activation and immunosuppression in the tumor microenvironment, which can provide a reference for individualized gene immunotherapy in patients with lung cancer.
Materials and Methods
Data Download and Processing
A total of 47 ICGs from Danilova et al. were downloaded (Supplementary Table 1) (Danilova et al., 2019). The RNA sequencing data of lung adenocarcinoma (LUAD) were downloaded from the TCGA Genomic Data Commons (GDC) application programming interface (API), which contained 515 samples. The GSE31210 microarray expression data set was downloaded from the National Center for Biotechnology Information (NCBI), and it contained 226 LUAD samples with clinical features.
The following precursory steps were conducted in the processing of the 515 RNA sequencing samples: (1) samples without clinical information or an overall survival (OS) <30 days were removed; (2) normal tissue samples were removed; and (3) genes whose fragments per kilobase million (FPKM) value was equal to 0 were removed. Thus, more than half of the samples were removed. Furthermore, GSE31210 data were pre-processed as follows: (1) normal tissue samples were removed, and only primary tumor data were retained; (2) year/month-based OS data were converted into day-based data; (3) the microarray probe was mapped to human gene symbols using the Bioconductor package; and (4) only the expression profile of immune-related genes was retained. Statistical clinical information for the two cohorts is shown in Table 1.
TABLE 1
Table 1. Pre-processed clinical information of the two cohorts in LUAD.
All the LUAD samples collected in our study were from before the first treatment, and all the patients received standard treatment, such as resection. Considering the complexity of clinical treatments, many patients used multiple treatment options during subsequent radiotherapy and chemotherapy. Because the GEO database does not include such treatment information, we made statistics on the TCGA cohort (Supplementary Table 2). In the TCGA cohort, 64 samples were collected from the central lung and 127 from the peripheral lung; the GSE31210 cohort did not contain LUAD location records (Supplementary Table 3).
Relationship Between ICG Expression and Prognosis
ICGs with gene expression in the TCGA cohort were identified. The patients were divided into different groups based on gene expression. In order to investigate the relation between the expression of ICGs and survival time, we used univariate cox regression analysis to identify prognostic ICGs. The logrank test (logrank p < 0.05) was used to compare survival curves between different ICGs expression. The expression and prognostic patterns of the ICGs were analyzed with the same method in the GEO cohorts.
Relationship Between ICGs and Other Immune Checkpoint Biomarkers
Spearman's Rank correlation coefficient is a technique which can be used to summarize the strength and direction (negative or positive) of a relationship between two variables. The Spearman method was used to evaluate the correlation between TMB and ICGs. The relation between neoantigen and ICG expression was further analyzed based on TCGA somatic cell mutation data.
CD200R1 Silencing by Lentiviral-Delivered RNA Interference
The shRNAs targeting the MGAT1 coding sequences are as follows: CD200R1-sh1 (NM_002405), 5′- CCCTTTCAGATCAAGCCA-3′; and CD200R1-sh2 (NM_0075606), 5′-CTGTTCAAAAATTATCAAGCT-3′. The control shRNA coding sequences are as follows: RFP, 5′- CTACCATGGCCTACAAGCT-3′. Adherent cells were treated with 0.5 mL of the virus followed by overnight incubation (37 uC, 5% CO2) without removing the virus. The next day, viral medium was replaced with fresh medium containing puromycin (1 mg/mL) to select a population of resistant cells.
Cell Viability Assay
After knocking down of CD200R1, the tumor cells were seeded in 96- well plates (5 × 103 cells/well), with cell transfected by negative control served as control group. The cells was cultured overnight, the medium was exchanged with 100 μl of medium supplemented with 10 μl CCK8. The plates were incubated for 2 h. Afterwards, absorbance at 450 nm was measured to obtain OD value. Similarly, other time points (24, 48, 72, and 96 h) were also assessed according to the procedures.
Flow Cytometry Analysis
Cells were digested with trypsin and then washed with phosphate-buffered saline (PBS). An Annexin V-FITC Apoptosis Detection Kit (Beyotime) was applied to practice cell apoptosis in line with the manufacturer's instructions. The apoptotic cells were dual-stained with propidium iodide (PI) and Annexin V-FITC using an Annexin V-FITC Kit (Thermo Scientific, Shanghai, China). Analysis was carried out via BDTM LSRII flow cytometer (BD Biosciences). Afterward, the data were measured with the Cell Quest software (BD Bioscience, San Jose, CA, USA).
Wound Healing and Transwell Assays
For the wound healing assay, tumor cells were seeded on 6-well plates. When 95% confluence was achieved, the cell monolayer was gently scratched using a 200 μm sterile plastic pipette tip. Then, the wound was photographed. After 24 h, the healing wound was photographed. For Transwell migration or invasion assays, 4 × 104 cells suspended in medium without serum were seeded in the upper chamber membranes coated without/with Matrigel (BD Biosciences). Then, 600 μl of medium with 10% fetal bovine serum (FBS) was added to the lower chamber. After 24 h, the underside of the membrane was fixed for 30 min and stained with 0.1% crystal violet. The inner side of the membrane was wiped with a cotton swab. Then, the cells were quantified under a microscope.
Western Blotting
Western blotting was performed according to standard protocols previously described. We used primary antibodies raised against GAPDH, Snail, E-cadherin, and Vimentin (Santa Cruz Biotechnology, Santa Cruz, CA, USA) and CD200R1 (Proteintech, China). Goat anti-mouse and anti-rabbit antibodies conjugated with horseradish peroxidase were used as secondary antibodies (Jackson ImmunoResearch, West Grove, PA, USA), and we detected the blots using enhanced chemiluminescence (ECL) (Dura, Pierce, NJ, USA).
Statistical Analysis
For comparison between two groups, the statistical significance of the normal distribution variables was estimated with the non-paired student's t-test, and the non-normal distribution variables were analyzed using the Mann-Whitney U-test. For comparisons between more than two groups, the Kruskal-Wallis test and one-way analysis of variance (ANOVA) were used as non-parametric and parametric methods, respectively (Yuan et al., 2015). The correlation coefficients were calculated using Spearman correlation analysis. The Kaplan-Meier method was used to generate a survival curve for the subgroups in each cohort, and the logrank test was used to determine the statistical significance at the level of p < 0.05. All the analyses were performed using R 3.4.3 using default parameters, unless otherwise specified.
Results
Relationship Between ICGs and Prognosis in the TCGA Cohort
First, a total of 45 ICGs with expression in the TCGA cohort were selected. Depending on their expression, the 45 ICGs were divided into three distinct expression patterns: the high-expression group (red) was represented by CD44, CD40, TNFRSF14, and NRP1, with generally high expression levels in all samples; the medium-expression group (green) was represented by IDO1, LAG3, and PDCD1, with notable expression differences between samples; and the low-expression group (blue) was represented by KIR3DL1, BTNL2, and the remainder, with lower expression levels in most samples (Figure 1).
FIGURE 1
Figure 1. Unsupervised clustering of ICGs in the TCGA-LUAD cohort. The overall survival and status event were used as patient annotations. Red: high expression group; green: medium expression group; blue: low expression group.
Using univariate Cox regression analysis, we calculated the relationships between the 45 ICGs and prognosis in the TCGA cohort and obtained nine genes that were significantly correlated with prognosis: TNFSF14, CD70, TNFRSF14, CD27, LAIR1, CTLA4, CD28, CD40LG, and CD200R1 (logrank p < 0.05; Figure 2A). These nine genes were mainly positively correlated and showed a significant aggregate effect (Figure 2B) through correlation analysis of ICG expression levels, indicating coordinated expression between the ICGs.
FIGURE 2
Figure 2. (A) Relationship between ICGs expression and prognosis in forest plot. Abscissa represents 95% confidence interval, ordinate (left) represents gene, and the vertical line in the middle of P-value represents the invalid line. When the horizontal line of 95% confidence interval of a gene crosses with the invalid line, it can be considered that there is no statistical correlation between gene expression and patient survival. If the horizontal line falls to the left of the invalid line, the gene can indicate a higher likelihood of success for patient prognosis. If the horizontal line falls to the right of the invalid line, it can be considered as a risk factor for the patient survival. (B) Correlation heatmap of ICGs expression levels. Only the gene pairs with significant correlation test are shown: the blank indicates zero significance of correlation test, blue represents negative correlation, and red represents positive correlation. *P < 0.05, **P < 0.01.
Relationship Between ICGs and Treatment Using Biomarkers for Other Immune Checkpoints
Relationship Between ICGs and TMB
We calculated the TMB of LAUD according to TCGA somatic mutation data, and we removed the intron interval and the mutation annotated as silent. First, we selected nine ICGs with expression quantities strongly correlated with prognosis and used the Spearman method to evaluate the correlation between TMB and the nine ICGs (Shapiro-Wilk test, p < 2.2e-16). We observed highly negative correlations between TMB and CD40LG, CD200R1, TNFRSF14, and TNFSF14 expression (R < 0 and false discovery rate [FDR] < 0.005; Figure 3). TNFSF14 was the factor potentially causing unfavorable prognosis. Thus, we speculated that higher TNFSF14 expression corresponds to lower TMB. Additionally, a low TMB was generally not an indicator of successful immune checkpoint inhibitor treatment.
FIGURE 3
Figure 3. Correlation scatter diagram of IGCs' expression and tumor mutation burden (TMB). R2 is the correlation coefficient, and FDR is false discovery rate. The abscissa represents TMB, and the ordinate represents genes.
Relationship Between ICGs and MMRs
MMRs are intracellular mismatch repair mechanisms, and the loss of function of key genes in these mechanisms leads to irreparable DNA replication errors, thereby increasing somatic cell mutations. We used LUAD somatic mutation data from TCGA to evaluate the relationships between five MMR gene mutations (MLH1, MSH2, MSH6, PMS2, and EPCAM) and ICG expression. It was found that the MMRs mutant gene was mainly positively correlated with the expression level of ICGs, and was highly correlated with TNFRSF8 and CD70 genes (Figure 4). CD70, a prominent prognosis-correlated gene, could be regarded as a potential predictor of MMR gene mutations.
FIGURE 4
Figure 4. Correlation heatmap between IGCs' expression and mismatch repair (MMR) gene mutation. The blank shows non-significant correlation. Blue represents negative correlation, while red represents positive correlation. The larger the point and the redder the color, the stronger the correlation.
Relationship Between ICGs and Neoantigens
Tumor somatic mutations in protein-coding genes produce neoantigens, which can be presented by the major histocompatibility class I complex (MHC I). These new peptides induce anti-tumor adaptive immune responses by binding to T-cell receptors (TCR). Based on the TCGA somatic mutation data, we further analyzed the relationship between neoantigens and the expression of the nine ICGs that largely affected prognosis. We found a high negative correlation between four genes (CD40LG, CD200R1, TNFSF14, and TNFRSF14) and neoantigen expression (R < 0 and FDR < 0.05; Figure 5). These results were consistent with the aforementioned results, in which TMB was extremely negatively correlated with the expression of the four genes.
FIGURE 5
Figure 5. Correlation scatter diagram of IGCs' expression levels and neoantigens. R2 is the correlation coefficient, and FDR is false discovery rate. The abscissa represents log2 (neoantigens), and the ordinate represents genes.
Relationship Between ICGs and Adaptive Immune Resistance Pathway Genes
CD8 T cells can produce interferon-γ and lead to upregulated expression of adaptive immune resistance pathway genes (including the PD-1/PD-L1 axis, IDO1, etc.). Therefore, we analyzed the correlation between CD8A, GZMB, CD68, and NOS2 and ICGs. Except for NOS2, there were four adaptive immune resistance pathway genes that were positively correlated with ICG expression (Figure 6A). At least half of the correlations between these genes were significant (p < 1e-3; Figure 6B).
FIGURE 6
Figure 6. (A) Heatmap of the correlation coefficients between ICGs and adaptive immune-resistance pathway genes. The abscissa represents the genes of the adaptive immune resistance pathway, and the ordinate represents the ICGs; (B) −log10 (P-value) is tested for the correlation coefficients between ICGs and adaptive immune-resistance pathway genes. The redder the color, the greater the significance.
Relationship Between ICGs and Clinical Features
Univariate Cox analysis identified nine prognostic ICGs. Using the clinical information provided by TCGA, we analyzed the relationship between these nine ICGs and clinical features. Due to the large deviation in the distribution of the samples in the metastasis (M) stage of LUAD (M0/M1:326/24), this study focused on the analysis of the expression patterns of these nine ICGs in the tumor (T), node (N), and clinical stages. In terms of expression level, the ICGs were clearly divided into three high-expression groups and six low-expression groups (Supplementary Figure 1). The expression level of the nine ICGs in early stage tumor samples was higher than in advanced tumor samples (Figures 7A–C), indicating that TNFRSF14, CD27, and CD200R1 show significant differences in the T, N, and clinical stages (Kruskal-Wallis test, p < 0.05). No significant relationship was observed between sex, new events, and these ICGs (Supplementary Figure 2).
FIGURE 7
Figure 7. (A) Box diagram of nine ICGs' expression in different T stages (FPKM); (B) Box diagram of nine ICGs' expression in different N stages (FPKM); (C) Box diagram of nine ICGs' expression in different Stages (FPKM). The Abscissa represents the ICGs, and the ordinate represents the expression of ICGs in different T/N/Stages. The test method is Kruskal-Wallis Test. *P < 0.05; **P < 0.01; ***P < 1e-5.
Validation of ICG Expression and Prognosis in the GEO Cohort
Among the 47 selected ICGs in the GSE31210 cohort, there were 44 ICGs with expression, and their expression patterns were significantly clustered into three categories: high, medium, and low expression (Figure 8A). We downloaded the expression profile of the GSE30219, GSE3141, and GSE81089 cohorts, and analyzed the expression of ICGs with three different expression patterns in these three data sets. As shown in the Supplementary Figure 3, it can be observed that their expression patterns were also clustered into three categories: high, medium, and low expression.
FIGURE 8
Figure 8. (A) Unsupervised clustering of ICGs in the GSE31210 cohort. The overall survival and status event were used as patient annotations. Red: high expression group; green: medium expression group; blue: low expression group. (B) Relationship between ICGs expression and prognosis in forest plot. Abscissa represents 95% confidence interval, ordinate (left) represents gene, and the vertical line in the middle of P-value represents the invalid line. When the horizontal line of 95% confidence interval of a gene crosses with the invalid line, it can be considered that there is no statistical correlation between gene expression and patient survival. If the horizontal line falls to the left of the invalid line, the gene can indicate a higher likelihood of success for patient prognosis. If the horizontal line falls to the right of the invalid line, it can be considered as a risk factor for the patient survival. (C) Correlation heatmap of ICGs expression levels. Only the gene pairs with significant correlation test are shown: the blank indicates zero significance of correlation test, blue represents negative correlation, and red represents positive correlation. *P < 0.05, **P < 0.01.
Univariate survival analysis of the relationship between the 44 ICGs and OS revealed that 11 ICGs were auspiciously correlated with the prognosis (Figure 8B). Within the GSE31210 cohort, ICG expression levels were largely positively correlated and had a clear aggregation effect (Figure 8C).
Prognostic Signature Construction and Validation
CTLA4, CD274, and IDO1 are important ICGs and biomarkers for immunotherapy. We found that CTLA4 had a significant prognostic relationship with OS in the TCGA cohort, and CD274 and IDO1 had a significant prognostic relationship with OS in the GSE30219 cohort.
CD200R1 was not only significantly correlated with prognosis in the TCGA cohort, but it was also negatively correlated with TMB and neoantigens. In addition, it showed significant positive correlation with CD8A, CD68, and GZMB, indicating that CD200R1 could cause the imbalanced gene expression in the adaptive immune resistance pathway. Therefore, we combined CD200R1, CD274, IDO1, and CTLA4 to analyze the relationship with prognosis. We chose the TCGA-LUAD cohort as the training set, the GSE31210 cohort as the testing set, and the GSE30219 cohort as the external validation set. For a unified quantitative analysis, we used multivariable Cox regression to construct a signature with the four ICGs: Risk Score = −0.002*IDO1+0.031*CD274−0.069*CTLA4−0.517*CD200R1.
The risk scores of the samples in the three cohorts were calculated by the same method. The median Risk Score was used to classify the high- and low-risk groups, and we observed significant prognostic differences between groups in the training (Figure 9A), testing (Figure 9B), and external validation cohort (Figure 9C).
FIGURE 9
Figure 9. Prognostic signature construction and validation. (A) Significant prognostic differences between groups in the TCGA training cohort. (B) Significant prognostic differences between groups in the testing cohort (GSE31210). (C) Significant prognostic differences between groups in the external validation cohort of GSE30219. Abscissa represents survival time (days), ordinate represents overall survival, the p-value represents the significance of gene combinations with different expression for the prognosis classification of LUAD. Log rank p-test was used.
Biological Behavior of CD200R1 in LUAD
Through the previous analysis, we showed that CD200R1, as an important immune checkpoint, has an important relationship with the prognosis and clinical characteristics of patients with cancer. Therefore, this biomarker was further explored.
Baseline expression of CD200R1 in four kinds of cancer cells was analyzed by western blot, as shown in Figure 10A, the A549 cell line expressed the most CD200R1 and was therefore used for subsequent study.
FIGURE 10
Figure 10. Biological behavior of CD200R1 in lung adenocarcinoma. (A) Baseline expression of CD200R1 in four kinds of cancer cells; (B) Two shRNA targeting CD200R1 was transfected into A549, effect of knock down was confirmed by western blot; (C) Cancer cell growth rate was increased after CD200R1 was knocked down; (D) S phase distribution was increased after CD200R1 was down-regulated. (E) Percentage of Ki67 positive was used to confirm the effect of CD200R1 on cell proliferation. (F) Apoptosis rate was studied by ANNEXINV stain; (G,H) The effect of CD200R1 on cell migration was studied by transwell and wound healing assay; (I) The expression of Snail and Vimentin was increased and that of E-cadherin was reduced, indicted EMT was occur after CD200R1 was knocked down.
Two shRNAs targeting CD200R1 were transfected into A549 cell line, and the knockdown effect was confirmed by Western blotting, as shown in Figure 10B. The result shown that shRNA#1 have better knock down effect than shRNA#2, therefore shRNA #1 was selected for subsequently study.CCK8 was used to confirmed effect of CD200R1 on cancer cell proliferation. As shown in Figure 10C, after CD200R1 was knocked down, cancer cell growth rate was increased, the conclusion was further confirmed by EdU incorporation assay, as show in Figure 10D, S phase distribution was increased after CD200R1 was down regulated. Percentage of Ki67 positive was further used to confirm the effect of CD200R1 on cell proliferation. As shown in Figure 10E, after CD200R1 was knocked down, percentage of Ki67 positive was increased significantly. Apoptosis rate was studied by ANNEXINV stain, as shown in Figure 10F, after CD200R1 was knocked down, apoptosis rate of cancer cells was reduced. The effect of CD200R1 on cell migration was studied by transwell and wound healing assay, as shown in Figures 10G,H, enhanced migration capacity was observed after CD200R1 was knocked down. To investigated underlying mechanism which result in enhanced migration capacity, EMT related marker was analyzed, as shown in Figure 10I, after CD200R1 was knocked down, expression of Snail and Vimentin was increased and that of E-cadherin was reduced, indicted EMT was occur after CD200R1 was knocked down.
Taken together, our result confirmed that down regulation of CD200R1 promotes cancer cell growth, impaired cancer cell apoptosis, and enhanced migration capacity of cancer cells through EMT mechanism.
Discussion
In recent years, breakthroughs pertaining to immune checkpoint inhibitors have progressed the treatment of lung cancer (Bai et al., 2020). Atezolizumab for first-line treatment and nivolumab and pembrolizumab for third-line treatment have been approved by the U.S. Food and Drug Administration (FDA) (Borghaei et al., 2015; Garon et al., 2015; Socinski et al., 2018). Compared with traditional chemotherapy, immune checkpoint inhibitors have lower toxicity, but only a small proportion of patients in the unselected treatment group can benefit from them (Koster et al., 2015). Therefore, it is crucial to develop methods of identifying lung cancer immune subgroups based on ICG expression profiles, selecting patients more suitable for immunotherapy and thereby enhancing individualized immunotherapy.
This study aimed to identify the relationship between immune subgroups and the prognosis of lung cancer samples based on gene expression data from multiple immune checkpoints. The gene expression profiles from the GEO and TCGA databases were divided into high-, medium-, and low-expression groups according to the gene expression levels of 47 immune checkpoints. The gene expression of the three expression groups from the two databases were of high consistency. In other words, genes with either high or low expression in the TCGA database were similarly expressed in the GEO database. This indicated a relatively consistent distribution of genes in the collected samples of the two databases. Therefore, the analysis results can be mutually verified in subsequent studies.
Using univariate Cox regression analysis to identify genes associated with OS, we obtained nine genes significantly related to prognosis from the TCGA database and 11 prognosis-related genes from the GEO database. Among them, CD40LG, which is a cluster of differentiation 40 ligand (CD40L), was significantly correlated with better prognosis in both databases (HR < 1, logrank p < 0.05) (Takada et al., 2019). CD40L, which is a type II membrane-related glycoprotein and a member of the tumor necrosis factor (TNF) superfamily, is conducive to regulating the immune response and inhibiting tumor growth. It produces direct growth-inhibiting effects through apoptosis in malignant tumor cells with CD40L overexpression, such as breast cancer (Pan et al., 2013). The correlation of ICG expression levels in both databases was positive and showed a dramatic aggregation effect, indicating the possibility of a coordinated expression relationship between ICGs.
To clarify the relationship between ICGs and other biomarkers for immune checkpoint treatment, we analyzed the relationships between ICGs and TMB, MMRs, and neoantigens. TMB is a reliable indicator of predicted clinical efficacy of the PD-1 inhibitor (Jafarnejad et al., 2019); the more mutant genes in the tumor tissue, the more likely it is that abnormal proteins will be significantly produced. These abnormal proteins can activate the body's anti-tumor immune response, improving the sensitivity of immunotherapy. Therefore, it is more suitable for patients with high TMB levels to continue immunotherapy with PD-1 inhibitors (Penault-Llorca and Radosevic-Robin, 2018).
Some studies have found that TMB can be used as a biomarker for predicting the efficacy of nivolumab monoclonal antibodies. Subsequent phase II and phase III studies have shown that nivolumab combined with ipilimumab produces more significant survival benefits when compared with chemotherapy in patients with high TMB (TMB ≥ 10 mut/Mb) (Hellmann et al., 2018; Ready et al., 2019). Studies have also shown that atezolizumab is more effective in patients with blood TMB ≥ 16 mut/Mb (Gandara et al., 2018). Through analyzing the relationship between ICGs and TMB, we found that the expression of TMB is strongly inversely correlated with CD40LG, CD200R1, TNFRSF14, and TNFSF14 expression (R < 0 and FDR < 0.005). That is, the high expression of CD40LG, CD200R1, TNFRSF14, and TNFSF14 corresponds to the low expression of TMB. TNFSF14, specifically, is a potential cause of unfavorable prognosis. Since high TNFSF14 expression corresponds to low TMB, the high expression of TNFSF14 may not be suitable for the treatment of immune checkpoint inhibitors.
MMRs are intracellular mismatch repair mechanisms. Their loss of function leads to irreparable DNA replication errors, thus leading to high generation of gene mutations (Chen X. et al., 2019). When tumor cells are mismatch repair-deficient (dMMR) and microsatellite instability is present, many gene mutations will accumulate in tumor cells, and some neoantigens may be recognized and attacked by the immune system, thereby achieving better immunotherapy effects. Recently, the effectiveness of the PD-1 drug pembrolizumab was tested in patients with advanced metastasis in a phase II clinical trial; the objective response rate and non-progressive stage in the MMR colorectal cancer group were as high as 40 and 78%, while in the control group, they were 0 and 11%, respectively (Lee and Le, 2015). Subsequent exon sequencing data indicated that the MMR tumor group had high TMB, which may translate to more tumor neoantigens and induce more T cells to attack tumor cells, which is conducive to the success of PD-1 immune drugs. MMR is an excellent predictor of the efficacy of PD-1 immune tumor drugs. In 2017, pembrolizumab was approved by the FDA for the treatment of adult and pediatric solid tumor patients with dMMR (Boyiadzis et al., 2018). Further, nivolumab combined with ipilimumab has a more significant effect on patients with dMMR metastatic colorectal cancer (2018).
The five MMR gene mutations evaluated in this study were positively correlated with ICG expression. Among them, CD70, which is also significantly correlated with prognosis, might be a potential predictor of MMR gene mutation. New proteins produced by gene mutation that may activate the immune system and trigger it to attack cancer cells are called neoantigens (Chen F. et al., 2019). Our study found that CD40LG, CD200R1, TNFSF14, and TNFRSF14 were significantly negatively correlated with neoantigen expression (R < 0 and FDR < 0.05), which was in line with the extremely high correlation of negative expression between TMB and the four genes mentioned above. Our research showed that TNFSF14, the unfavorable prognostic gene, showed high expression in patients. The high expression of TNFSF14 corresponds to a low level of neoantigens, so, in this case, it was not appropriate for patients to be treated with personalized neoantigens (Lou et al., 2017). In general, the greater the total number of gene mutations—that is, the higher the TMB—in the tumor tissues of patients, the more neoantigens they carry. According to our results, CD40LG, CD200R1, TNFSF14, and TNFRSF14 expression was negatively correlated with TMB and neoantigens. This indicated that TMB and neoantigens share the same variation trend, which supports what has been reported in the literature (Jia et al., 2018).
To reveal the relationship between ICGs and adaptive immune resistance pathway genes, we analyzed the correlation between CD8A, GZMB, CD68, and NOS2 and ICGs. Most were found to be positively and significantly correlated. We obtained the same results from the GEO and TCGA databases. This indicated that adaptive immune pathway genes might have a certain regulatory effect on ICG expression.
In terms of the relationship between ICGs and clinical features, we found that the expression levels of ICGs in early tumor samples were higher than in advanced tumor samples, CD200R1 in particular. We also found that CD200R1 was highly correlated with the OS of patients in the TCGA cohort, but negatively correlated with TMB and neoantigens. In addition, in combination with adaptive immune resistance pathway genes, we found that CD200R1 was positively correlated with CD8A, CD68, and GZMB, suggesting that CD200R1 might be the possible cause of the expression dysregulation of adaptive immune resistance pathway genes.
Based on the previous analysis, we found that CTLA4 had a significant prognostic relationship with OS in the TCGA cohort, and CD274 and IDO1 were prognostic genes in the GSE30219 cohort. Therefore, CD200R1, CD274, IDO1, and CTLA4 were combined to analyze the relationship with prognosis. The TCGA-LUAD cohort was chosen as the training set, the GSE30219 cohort as the testing set, and the GSE31210 cohort as the external validation set. Multivariable Cox regression was used to construct the 4-ICG signature. The risk scores of the samples in the three cohorts were calculated by the same method. There were significant prognostic differences between groups in the training, testing, and external validation sets. CD200R1, CD274, IDO1, and CTLA4 expression levels can be integrated to evaluate the immune status of patients with LUAD, and together they can provide prognostic information that cannot be obtained by a single ICG.
As an important immune checkpoint, CD200R1, is closely related to the prognosis and clinical characteristics of patients with cancer. To further study the relationship between CD200R1 and the occurrence and development of lung cancer, we carried out cytological behavior experiments. The results showed that after inhibiting CD200R1 expression, the proliferation and migration capacity of tumor cells increased, the expression of Snail and Vimentin increased, and the expression of E-cadherin decreased, indicating that EMT occurred after CD200R1 knockout.
In this genomic era, many genome sequencing technologies have emerged (Wang et al., 2009). Researchers using these tools have made great contributions to tumor diagnosis and prognosis prediction. TCGA, a landmark cancer genomics program, molecularly characterized more than 20,000 primary cancers and matched normal samples spanning 33 cancer types. Although many studies have used bioinformatics methods to find key molecules and potential regulatory pathways related to LUAD prognosis or diagnosis, many others obscure the importance of immune checkpoint-related genes. In this study, we focused on the expression patterns and clinical correlations of ICGs in lung cancer for the first time. We analyzed the potential relationships between ICGs and TMB, MMR defects, neoantigens, and adaptive immune resistance pathway genes, and we combined multiple ICGs to subtype the prognosis of patients with lung cancer. Finally, a gene signature composed of these immune checkpoints was validated by external cohorts.
A gene signature containing 30 immune genes that can predict the prognosis of LUAD patients has been established by Song et al. (2019). He obtained 1534 immune genes from the ImmPort database (https://immport.niaid.nih.gov) as the research object, which containing genes related to innate immunity, so the focus of this research is innate immunity genes. In this study, there was no exploration related to immunotherapy. In addition, the number of genes in the signature reported was as many as 30, the clinical application was limited and the detection cost was relatively high.
Our research mainly used immune checkpoint genes as the research object to determine the subgroups that benefit from immunotherapy and related biomarkers, and finally identified four biomarkers closely related to immunotherapy, so although they are all related to immune markers research, our research is more inclined to clinical application.
Our study also has some limitations. For example, there are insufficient large-scale lung cancer samples for calculation and analysis, and most of the samples are no more than 1000 (Chang et al., 2015). Our results need to be verified by more experiments and clinical observation, and on this basis, with the development and design of potential immune checkpoint inhibitors (Tanoue, 2010). In addition to tumor cell immune-related gene expression, the effects of immune checkpoint inhibitors in tumor treatments are related to the strength of tumor immune stimulation signals, the function of immune effector cells, and the activity of other immune suppression signaling pathways (Zhu et al., 2019). It is not easy to find a single, comprehensive curative effect prediction index. An effective predictive model combining ICG expression profiles would lead to more effective uses of immunotherapy. Researchers are currently working on multiple immune checkpoint inhibitors and targeted therapeutic drugs (Larsen et al., 2019). Based on the ICG expression profiles of lung cancer immune subgroups, we can identify patients more suitable for immunotherapy and thus help develop personalized immunotherapy.
Conclusion
In sum, our results showed that the combined gene signature of IDO1, CD274, CTLA4, and CD200R1 was a reliable tool for predicting prognosis, and it is of great significance to personalized treatment for patients with LUAD in terms of immunotherapy.
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
Author Contributions
BL designed the current study. GY and QZ analyzed and interpreted the data. BL, YJ, and LL wrote the manuscript. QT supervised the study. All authors read and approved the final version of the manuscript and agreed to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding
This work was supported by Special Funding for Guangxi Special Experts (Gui Rencai Tongzi [2019] No. 13); Guangxi Medical High-level backbone Talents Training 139 Project (Guiwei Science and Education [2018] No. 22).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank the numerous individuals who participated in this study. This paper was edited by Jessica Marino. Jessica's credentials and contact information can be found on her profile at editorworld.com.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2020.603701/full#supplementary-material
Supplementary Figure 1. (A) Heatmap of the correlation coefficients between ICGs and adaptive immune-resistance pathway genes in the GSE31210 cohort. The abscissa represents the genes of the adaptive immune resistance pathway, and the ordinate represents the ICGs; (B) −log10 (P-value) is tested for the correlation coefficients between ICGs and adaptive immune-resistance pathway genes. The redder the color, the greater the significance.
Supplementary Figure 2. (A) The expression patterns of nine ICGs on T stage, N stage and stage. (B) Expression heat map of nine ICGs.
Supplementary Figure 3. The expression distribution of three types of ICG genes in different data sets.
Supplementary Table 1. Forty seven immune checkpoint genes.
Supplementary Table 2. Enrollment treatment information of patients in TCGA cohort.
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Keywords: lung adenocarcinomas, immune checkpoint genes, immune subtypes, immune gene expression profile, prognosis
Citation: Ling B, Ye G, Zhao Q, Jiang Y, Liang L and Tang Q (2021) Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles. Front. Mol. Biosci. 7:603701. doi: 10.3389/fmolb.2020.603701
Received: 07 September 2020; Accepted: 03 December 2020;
Published: 11 January 2021.
Edited by:
Mahendra Pratap Kashyap, University of Alabama at Birmingham, United StatesReviewed by:
Rakesh Pathak, National Institutes of Health Clinical Center (NIH), United StatesRanganatha R. Somasagara, North Carolina Central University, United States
Copyright © 2021 Ling, Ye, Zhao, Jiang, Liang and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Qianli Tang, htmgx@163.com
†These authors have contributed equally to this work