Identification of a immune-related genes signature for survival prediction in stage I lung adenocarcinoma

doi:10.21203/rs.3.rs-4969773/v1

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Identification of a immune-related genes signature for survival prediction in stage I lung adenocarcinoma

https://doi.org/10.21203/rs.3.rs-4969773/v1

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Background

With the popularization of low-dose spiral computed tomography (CT), an increasing number of stage I lung cancers have been discovered. Although most of the patients with stage I lung adenocarcinoma have a favorable prognosis, some patients still have a poor prognosis of recurrence and metastasis after surgery. The immune system has been shown to play an important role in the development of cancer. Our aim is to identify a reliable prognostic signature of immune-related genes that can predict prognosis and help in the individualized management of patients with stage I lung adenocarcinoma.

Methods

In this study, the immune-related genes were first extracted by the ImmPort database. Subsequently, these genes were screened using univariate Cox regression and LASSO regression to identify prognostic signatures, followed by construction of a prognostic model using multivariate Cox regression. A nomogram was developed to predict the prognosis of stage I lung adenocarcinoma and to evaluate the nomogram differentiation and accuracy using the receiver operating characteristic curve, clinical decision analysis and calibration curve. The model was validated in two independent data sets: GSE31210 and GSE30219. Then, patients were stratified based on median risk scores, and differential expression genes between the two groups were analyzed. Finally, the enrichment analysis and the immune infiltration analysis were performed.

Results

In this study, we found 29 immune-related genes and developed a gene signature with poor prognosis in stage I lung adenocarcinoma. Our model was validated using two independent datasets and demonstrated robust performance with good AUC values and clinical utility. Enrichment analysis indicated that immune-related genes signature has multifaceted effects on stage I lung adenocarcinoma, especially related to tumor development, proliferation, and metastasis. Patients in the high-risk group had higher tumor purity, lower matrix, estimated and immune scores, suggesting a potentially immunosuppressive tumor microenvironment.

Conclusion

This study constructed a gene signature related to immunity in stage I lung adenocarcinoma and analyzed its impact on tumor development and its relationship with the tumor microenvironment. The findings can contribute to a more precise survival risk stratification and personalized clinical management for stage I lung adenocarcinoma patients. Furthermore, we provide related opinions for future immune-related research by elucidating potential underlying mechanisms.

Lung adenocarcinoma

IA stage

Prognostic signature

Immune-related genes

Immune infiltration

Lung cancer is the most common cancer worldwide in terms of incidence and mortality. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of cases, with lung adenocarcinoma (LUAD) being the predominant subtype [1]. The widespread use of low-dose spiral CT has led to the increased detection of early-stage lung cancer, resulting in improved prognosis [2]. Surgical resection is the preferred treatment for stage I lung cancer, achieving high cure rates, and adjuvant therapy is generally not recommended postoperatively. However, up to 20–30% of stage I lung cancer patients experience poor prognosis, with a five-year survival rate of only 68% for stage IB [3]. Research indicates that EGFR-mutated stage IB lung cancer patients can benefit from postoperative targeted therapy. However, for patients without driver gene mutations, neither postoperative targeted therapy nor chemotherapy achieves satisfactory results, while the effectiveness of immunotherapy remains largely unknown [4, 5]. In recent years, clinical application of cancer immunotherapy has transformed the treatment landscape and is considered the most promising approach to improve survival rates in lung cancer patients [6, 7]. By exploring the tumor immune microenvironment (TME), we find that immunotherapy can directly or indirectly impact tumor development, including promoting tumor angiogenesis, altering tumor biology, facilitating immune escape, and even modulating cancer stem cell (CSC) activity [8]. Components of the TME include immune cells, stromal cells, endothelial cells, inflammatory mediators, and extracellular matrix molecules. Immune cells play a crucial role in tumor initiation and progression [9]. These immune cells can form immune surveillance cells composed of various pro-inflammatory cells (such as M1 macrophages and CD8 + cytotoxic T cells) or contribute to tumor development (such as M2 macrophages, myeloid-derived suppressor cells, and CD4 + type 2 helper cells) [10, 11]. Additionally, specific immune checkpoint inhibitors (ICIs), such as anti-PD-1/L1 and anti-CTLA-4, have been widely used in current immunotherapy and have been shown to benefit LUAD patients with specific immune phenotypes. However, our understanding of the relationship between the tumor immune microenvironment and early-stage lung cancer prognosis remains limited. Some studies have reported that immune-related genes can predict the prognosis of LUAD patients and provide potential targets for immunotherapy [12]. Nevertheless, few prognostic models have focused on immune-related genes in stage I LUAD. In this study, we developed and validated an immune-related gene-based prognostic model for stage I LUAD using gene expression datasets from GEO and TCGA. This novel model, based on 29 genes, exhibits strong predictive ability for stage I LUAD, enabling clinicians to assess patient prognosis and potentially guide personalized follow-up and intervention strategies.

Data Collection

We extracted clinical and RNA sequencing data from 508 lung adenocarcinoma patients in The Cancer Genome Atlas (TCGA) database, including 284 patients with stage I lung adenocarcinoma. TCGA patients were defined as the training cohort, while we selected datasets from the Gene Expression Omnibus (GEO) database using the GEO query package as an external validation cohort to assess the stability of the immune-related prognostic model. The validation cohort included samples from the GSE31210 and GSE30219 datasets, both based on the GPL570 platform, comprising 168 and 92 stage I lung adenocarcinoma samples, respectively. Gene expression data from GEO and TCGA-LUAD datasets were standardized using the limma and DESeq2 packages.

Identification of Prognostic Immune Genes

We obtained a set of 1,793 immune-related genes from the ImmPort database. By intersecting RNA sequencing genes from the TCGA database with these immune-related genes, we identified 1,361 overlapping genes. Using the expression data from TCGA lung adenocarcinoma samples, we performed univariate Cox regression analysis on these 1,361 genes. Genes with adjusted p-values less than 0.05 and hazard ratios not equal to 1 were considered immune-related genes associated with prognosis.

Construction, Validation, and Evaluation of the Prognostic Model

We further selected genes with nonzero penalty coefficients using LASSO regression analysis (implemented via the “glmnet” R package) to refine the model. The final immune-related prognostic model was determined by multivariate Cox regression analysis, which provided the partial regression coefficients (β) for each gene. We calculated risk scores for each patient in the TCGA cohort and used the median risk score as the threshold to classify patients into low- and high-risk groups. We performed univariate and multivariate Cox regression analyses on risk score groups along with clinical information. Additionally, we created a nomogram based on the identified risk factors using the “rms”, “foreign” and “survival” packages in R Studio. The model’s consistency was assessed using calibration curves and decision curve analysis (DCA). We validated the model using independent LUAD datasets from the GEO database.

Survival Analysis

We constructed Kaplan-Meier survival curves using the “survival” R package to visualize overall survival (OS) in the high-risk and low-risk groups. Furthermore, we evaluated the predictive performance of the model for 1-year, 3-year, and 5-year survival probabilities using the “survivalROC” package, which generates ROC curves and calculates the area under the curve (AUC).

Functional Enrichment Analysis and Immune Correlation Analysis

Differential expression genes (DEGs) between high-risk and low-risk groups in the TCGA database were identified using the DESeq2 package. DEGs were selected based on adjusted p-values < 0.05 and absolute log fold change > 1. Heatmaps and volcano plots were generated using the “pheatmap” and “ggplot2” R packages, respectively. We explored the biological functions of DEGs using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Additionally, we estimated tumor purity, immune infiltration, and stromal infiltration using the ESTIMATE algorithm, which utilizes expression data. We also measured the sample-level abundance of 28 tumor-infiltrating immune cell types using single-sample gene set enrichment analysis (ssGSEA).

Statistical Analysis

All statistical analyses were performed using R software (version 4.3.1). We used t-tests to compare differences between two groups. Default parameters were used for unspecified settings. Relevant sections describe the specific statistical methods employed, with a significance threshold of P < 0.05.

Patient Characteristics and Collection of Immune-Related Genes

We obtained complete data from 508 lung adenocarcinoma patients in the TCGA database, including clinical information and RNA sequencing expression profiles. Among these, 284 patients had stage I lung adenocarcinoma data. Additionally, we retrieved clinical information and RNA sequencing expression profiles for 168 and 92 stage I lung adenocarcinoma patients from the GEO databases GSE31210 and GSE30219, respectively. The TCGA patient data served as the training set, while the GEO data served as the validation set. We obtained a total of 2,843 immune-related genes from the ImmPort database. After removing duplicate genes, we retained 1,793 genes for subsequent analysis. The integration of clinical data and the immune-related gene list is summarized in Tables 1 and S1.

Table 1

The clinical characteristics of three patient cohorts.
Variables	Numbers(N)	Constituent ratio (%)
TCGA	N = 284
Age(year)
< 65	130	45.8
>=65	154	54.2
Gender
Female	166	58.5
Male	118	41.5
Race
White	227	79.9
Others	57	20.1
Smoking
No	85	29.9
Yes	199	70.1
Stage
IA	134	47.2
IB	150	52.8
OS.status
Alive	227	75.7
Dead	57	24.3
GSE31210	N = 168
Age(year)
< 65	119	70.8
>=65	49	29.2
Gender
Female	97	57.7
Male	71	42.3
Smoking
No	94	56.0
Yes	74	44
Stage
IA	114	67.9
IB	54	32.1
OS.status
Alive	151	89.9
Dead	17	10.1
GSE30219	N = 92
Age(year)
< 65	55	59.8
>=65	37	40.2
Gender
Female	17	18.5
Male	75	81.5
Stage
IA	78	84.8
IB	14	15.2
OS.status
Alive	40	43.5
Dead	52	56.5

Construction of Immune-Related Genes as Prognostic Biomarkers

Among the 1,793 immune-related genes, only 1,361 genes overlapped with the RNA sequencing data for stage I lung adenocarcinoma. To further assess the prognostic value of these genes, we initially performed univariate Cox regression analysis, selecting 42 genes based on the criteria of adjusted p-value < 0.05 and hazard ratio not equal to 1. We then applied LASSO analysis to minimize overfitting (Fig. 2A, B). Finally, 29 genes were selected through multivariate Cox regression analysis (ANOS1, TNFSF13, VEGFD, APOBEC3F, HDGFL3, TLR7, XCL2, ROBO3, IL11, LHB, MCHR1, IL12B, PYY, DEFA3, ANGPTL5, LCN1, FGF8, IFNL1, GH2, TAFA4, TAFA1, IFNL3, GALP, IAPP, TRHR, FSHB, BMP15, MIF, HTR3D). The weighted sum of expression levels for each gene generated individual prognostic risk scores. Using the median risk score, we classified TCGA patients into high-risk (n = 142) and low-risk (n = 142) groups. The high-risk group exhibited significantly worse overall survival (OS) than the low-risk group (p < 0.0001) (Fig. 2C). Time-dependent receiver operating characteristic (ROC) curve analysis demonstrated good performance and efficiency of the DRG feature in predicting OS, with AUC values of 0.762, 0.802, and 0.733 for 1-year, 3-year, and 5-year survival rates, respectively (Fig. 2D). Calibration curves further confirmed the consistency between observed and predicted survival probabilities for 3-year and 5-year survival rates (Fig. 2E and 2F), demonstrating the robustness of our DRG feature in predicting adverse prognosis for LUAD patients. Immune-related genes were validated in two independent GEO datasets, where the high-risk group had higher mortality and worse survival outcomes compared to the low-risk group (Fig. 3A and 3B). Similar performance and efficiency in predicting OS were observed in the validation set (Fig. 3C and 3D). Overall, immune-related genes effectively predict survival outcomes in lung adenocarcinoma.

Immune-Related Gene Features as Independent Prognostic Factors for LUAD

To identify independent prognostic factors for lung adenocarcinoma patients, we performed univariate Cox regression analysis on risk scores and clinical features, including age, gender, stage, ethnicity, smoking status, and histological subtype, incorporating immune-related genes. Results revealed that risk score (HR = 5.9, 95% CI 3.3–11), ethnicity (HR = 0.32, 95% CI 0.15–0.69), and stage (HR = 1.7, 95% CI 1-2.7) were independent prognostic factors for LUAD patient OS. Further inclusion of these independent risk factors in multivariate Cox regression analysis identified risk score (HR = 5.72, 95% CI 3.09–10.58) and ethnicity (HR = 0.36, 95% CI 0.17, 0.75) as prognostic factors associated with outcomes (Table 2). We developed a clinical prognostic nomogram by considering ethnicity and risk score, predicting 3-year and 5-year OS for LUAD patients (Fig. 4A). ROC analysis evaluated the performance and efficiency of the nomogram, yielding AUC values of 0.712, 0.769, and 0.739 for 1-year, 3-year, and 5-year survival rates, respectively (Fig. 4B). Calibration curves demonstrated close alignment with the 45-degree diagonal, indicating consistent predicted survival probabilities with observed survival in the TCGA-LUAD cohort (Fig. 4C and 4D). Our nomogram model provides good clinical applicability for predicting OS in stage I lung adenocarcinoma patients. Our nomogram model provides good clinical applicability for predicting OS in stage I lung adenocarcinoma patients. Decision curve analysis (DCA) of recurrence-free survival (RFS) nomogram model is about prognostic factors for lung adenocarcinoma patients (Fig. 4E). “All” assumes all patients with stage IA lung adenocarcinoma are treated and“None” that all patients with stage IA lung adenocarcinoma are not treated. (f1 = race, f2 = ISP group, f3 = race + ISP group).

Table 2

**Multivariate Cox regression analysis identified risk score and ethnicity as prognostic factors associated with outcomes**.
Variables	Univariate		Multivariate
Variables	HR (95% CI)	P	HR (95% CI)	P
age		0.12
<65	Ref
≥65	1.5(0.91–2.4)
Smoking		0.47
No	Ref
Yes	1.2(0.71–2.1)
Stage		0.049
IA	Ref		Ref	0.883
IB	1.7(1-2.7)		0.96(0.57, 1.63)
Gender		0.33
Female	Ref
Male	0.79(0.48–1.3)
Race		0.0032
White	Ref		Ref	0.007
Others	0.32(0.15–0.69)		0.36(0.17, 0.75)
Risk score		< 0.001		< 0.001
Low	Ref		Ref
High	5.9(3.3–11)		5.72(3.09, 10.58)

Differential Gene Analysis Between High-Risk and Low-Risk Groups

We utilized the Deseq2 software package to analyze differential gene expression between high-risk and low-risk patients, identifying 859 differentially expressed genes (adjusted p-value < 0.05 and absolute log fold change > 1). Among these, 587 genes were upregulated, while 272 genes were downregulated. The volcano plot is shown in Fig. 5A, and a subset of differentially expressed genes (adjusted p-value < 0.05 and absolute log fold change > 2) is visualized in the heatmap (Fig. 5B). Subsequently, we performed GO and KEGG enrichment analyses on these differentially expressed genes. GO analysis revealed 24 immune-related functions associated with stage I lung adenocarcinoma (Fig. 5C). KEGG analysis indicated that pathways such as neuroactive ligand-receptor interactions, neutrophil extracellular trap formation, cell adhesion molecules, Th17 cell differentiation, and hematopoietic cell lineage may be relevant to the occurrence and development of stage I lung adenocarcinoma (Fig. 5D). These pathways involve aspects of the tumor microenvironment, intercellular signaling, inflammation, and angiogenesis.

Exploring the Association Between Immune-Related Gene Features and the Tumor Microenvironment

In this study, we employed the ESTIMATE and ssGSEA algorithms to investigate the relationship between immune-related gene features and the tumor microenvironment in stage I LUAD patients. Our findings revealed that, compared to the low-risk group, the high-risk group exhibited higher tumor purity but lower stromal and immune scores, with significant statistical differences (Fig. 6A, 6B, 6C, 6D). Furthermore, ssGSEA analysis demonstrated a significant reduction in various tumor-infiltrating immune cell types in the high-risk group (Fig. 6E and 6F). However, memory CD4 T cells showed an increase trend in the high-risk group (Fig. 6G). These results suggest that the tumor microenvironment in high-risk individuals may be immunosuppressive, compromising antitumor immunity and promoting tumor growth.

Previous studies have predominantly reported on prognostic biomarkers for intermediate and advanced-stage LUAD patients [13, 14]. However, with increased awareness of health check-ups and widespread use of CT scans, more early-stage lung adenocarcinomas are being detected, particularly stage I lung adenocarcinoma. The immune system has been demonstrated to play a crucial role in cancer initiation and progression [15]. Recent research has proposed the use of immune-related genes to predict the prognosis of lung adenocarcinoma [16, 17]. However, limited sample sizes, lack of external independent validation, or effective validation have hindered the predictive capabilities of these prognostic markers, and few studies have focused on the predictive value of immune-related genes for stage I lung adenocarcinoma.

In this study, we selected 29 key genes associated with the prognosis of stage I lung adenocarcinoma from immune-related genes. Subsequently, we constructed a prognostic risk model and evaluated its accuracy, validating it in two independent cohorts. Our results demonstrate that the prognostic risk model predicts the prognosis status of stage I lung adenocarcinoma patients with high accuracy across all included data, including GEO and TCGA datasets. Using nomograms to determine the survival probabilities for each stage I lung adenocarcinoma patient, we confirmed the clinical utility of the 29 immune-related prognostic genes. Based on median risk scores, we stratified patients with significantly different overall survival (OS) into high-risk and low-risk groups. The results indicate that higher risk scores correlate with poorer prognosis, while lower risk scores correlate with better prognosis. Additionally, to explore the distinct prognoses of high-risk and low-risk patients, we conducted differential gene analysis and further investigated the identified differentially expressed genes through enrichment analysis and immune infiltration analysis.

In GO analysis, the functions of these differentially expressed genes are significantly related to immunity, regardless of cellular components, molecular functions, or biological processes. KEGG enrichment analysis highlights the importance of pathways such as systemic lupus erythematosus (SLE) formation, neuroactive ligand-receptor interactions, neutrophil extracellular trap formation, and cell adhesion molecules. Interestingly, SLE ranks first. SLE is an autoimmune disease where the immune system erroneously attacks normal tissues and organs in the body. B cell dysfunction is a typical feature of SLE and B cells are major players in humoral immunity, suggesting a close association with humoral immunity in stage I LUAD patients [18]. Both tumors and immune cells have the ability to produce various neurotransmitters, which directly or indirectly impact tumor cell proliferation, migration, invasion, and cancer development [19, 20]. Research has found that neutrophil extracellular traps (NETs) can create a favorable environment for tumor cells, promoting tumor growth, invasion, and metastasis [21]. Cell adhesion can influence tumor cell growth in situ and their ability to spread to other sites. Some tumor-expressed adhesion molecules can enhance tumor cell adhesion to surrounding tissues, thereby promoting tumor growth and dissemination [22]. GSEA analysis reveals significant inhibition of inflammatory responses, complement, immune cells, interferon gamma response and cytokines. Interferon-gamma (IFN-γ) plays a critical role in cell-mediated immune activation and subsequent stimulation of antitumor immune responses, exerting inhibitory, pro-apoptotic, and antiproliferative effects on tumor cells. Additionally, IFN-γ may suppress tumor angiogenesis, induce regulatory T cell apoptosis, and stimulate M1 pro-inflammatory macrophage activity to inhibit tumor progression [23]. The complement system is one of the inflammatory mechanisms activated in the tumor microenvironment. Besides its antitumor mechanisms, such as complement-dependent cytotoxicity and phagocytosis induced by therapeutic monoclonal antibodies, the complement system can also promote immune suppression and tumor growth and invasiveness, particularly through targeting leukocytes and cancer cells with anaphylatoxins [24]. The IL-2/STAT5 signaling pathway plays a crucial role in tumor development and therapy. IL-2 can activate the STAT5 signaling pathway, promoting the activation and proliferation of immune cells, especially CD8 + T cells and natural killer (NK) cells [25]. These immune cells can recognize and eliminate tumor cells, thus inhibiting tumor growth and dissemination [26].

Furthermore, there is a correlation between immune-related genes and tumor immune infiltration. Tumor purity describes the proportion of actual cancer cells within a tumor. Higher purity tumors typically indicate a higher concentration of cancer cells, often necessitating more aggressive treatment strategies to control or eradicate these cancer cells [27]. Our study found that patients with high-risk scores had significantly higher tumor purity compared to those with low-risk scores, while the other three immune scores were significantly lower in high-risk patients, indicating a poorer prognosis. Combining enrichment analysis results, we discovered that immune-related genes impact lung adenocarcinoma in multiple ways, encompassing almost the entire process of tumor development. We further investigated the relationship between immune-related genes and immune cells, finding that tumors in high-risk individuals exert an immunosuppressive effect on the immune microenvironment, impairing the body’s antitumor capabilities and promoting tumor growth.

However, our study has limitations. Firstly, it is not a prospective study. All stage I lung adenocarcinoma patients were selected from public databases. Secondly, the limited number of clinicopathological variables reduces the statistical power of multivariate Cox regression analysis and the prognostic model. Lastly, the predictive performance of the model requires validation in large-scale multicenter cohorts and more patient samples, or through experimental studies to verify the related mechanisms.

In this study, we selected 29 immune-related genes to construct an effective, innovative, and reliable prognostic model for stage I lung adenocarcinoma. This model exhibits good predictive performance and has been validated in two independent datasets. It stratifies stage I lung adenocarcinoma patients into high-risk and low-risk groups, enabling clinicians to develop earlier and more rational postoperative follow-up and intervention plans, thereby improving prognosis and conserving medical resources. Additionally, we conducted enrichment analysis and investigated immune infiltration, revealing that the tumor microenvironment in high-risk individuals may promote tumor cell proliferation, invasion, and metastasis due to immunosuppression, which is relevant to tumor development.

CT, computed tomography. AUC, area under the curve. NSCLC, Non-small cell lung cancer. LUAD, lung adenocarcinoma. TME, tumor immune microenvironment. CSC, cancer stem cell. ICIs, immune checkpoint inhibitors. MDSC, myeloid-derived suppressor cells. TCGA, The Cancer Genome Atlas. GEO, Gene Expression Omnibus. DCA, decision curve analysis. OS, overall survival. DEGs, Differential expression genes. GO, gene ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. GSEA, gene set enrichment analysis. ssGSEA, single-sample gene set enrichment analysis. RFS, recurrence-free survival. SLE, systemic lupus erythematosus. NETs, neutrophil extracellular traps. IFN-γ, Interferon-gamma. NK, natural killer.

Funding

No funds, grants, or other support was received.

Ethical approval and consent to Participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Acknowledgements

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Authors' contributions

Conceptualization Xin Zhang; Data curation SiSheng Luo, Xinyu Liu and Xin Zhang; Methodology Yi Hu, Kongjia Luo and Leqi Zhong; Validation Yong Ao and Shining Li; Visualization Zhang Xin and Shining Li; Writing original draft Shining Li and Xin Zhang; Final editing and validation Shining Li and Xin Zhang. All authors reviewed the manuscript.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Identification of a immune-related genes signature for survival prediction in stage I lung adenocarcinoma

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and Methods

Data Collection

Identification of Prognostic Immune Genes

Construction, Validation, and Evaluation of the Prognostic Model

Survival Analysis

Functional Enrichment Analysis and Immune Correlation Analysis

Statistical Analysis

Results

Patient Characteristics and Collection of Immune-Related Genes

Construction of Immune-Related Genes as Prognostic Biomarkers

Immune-Related Gene Features as Independent Prognostic Factors for LUAD

Differential Gene Analysis Between High-Risk and Low-Risk Groups

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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