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Roles of naïve CD4+ T cells and their differentiated subtypes in lung adenocarcinoma and underlying potential regulatory pathways

Journal of Translational Medicine volume 22, Article number: 781 (2024) Cite this article

Abstract

Background

Naïve CD4+ T cells and their differentiated counterparts play a significant regulatory role in the tumor immune microenvironment, yet their effects on lung adenocarcinoma (LUAD) are not fully understood.

Methods

We utilized Mendelian randomization to assess the causal association between naïve CD4+ T cells and LUAD. Employing a modified single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm with The Cancer Genome Atlas (TCGA) database, we determined the infiltration levels of naïve CD4+ T cells and their differentiation subtypes and investigated their correlation with clinical characteristics. Potential regulatory pathways of T helper cells were identified through Mantel tests and Kyoto Encyclopedia of Genes and Genomes (KEGG) database enrichment analysis.

Results

Mendelian randomization analysis revealed an inhibitory effect of naïve CD4+ T cells on LUAD (false discovery rate < 0.05), which was corroborated by observational experiments using TCGA database. Specifically, T helper cell type 2 demonstrated a promotive effect on LUAD in terms of overall, disease-free, and progression-free survival (p < 0.05). Moreover, regulatory T cells exhibited a protective effect on LUAD in terms of disease-specific survival (p < 0.01). Concurrently, we explored the overall impact of naïve CD4+ T cell differentiation subtypes on LUAD, revealing upregulation in pathways such as neutrophil degranulation, MAPK family signaling pathways, and platelet activation, signaling, and aggregation.

Conclusion

Naïve CD4+ T cells and their differentiated counterparts play essential regulatory roles in the tumor immune microenvironment, demonstrating bidirectionality in their effects.Thus, elucidating the mechanisms and developing novel cell differentiation-inducing agents will benefit anti-cancer therapy.

Introduction

Lung adenocarcinoma (LUAD) is the most prevalent subtype of nonsmall cell lung cancer, whose progression is influenced by various factors, notably the tumor immune microenvironment [1, 2]. Among investigations into the tumor immune microenvironment, considerable attention has been focused on the regulatory function of naïve CD4+ T cells and their differentiated subtypes [3,4,5,6,7].

The tumor immune microenvironment constitutes a dynamic system surrounding the tumor, characterized by a complex network of diverse cell types, signaling molecules, and cytokine interactions [8]. Immunological cells, including naïve CD4+ T cells, differentiate into various subgroups and subtypes, displaying extensive diversity and intricate relationships [9]. The functional outcome of specific immune cell types can be significantly influenced by other cells or regulatory elements present in the surrounding milieu [10, 11]. Therefore, studying the role of immune cells and their populations in the tumor microenvironment holds considerable promise for tumor therapy.

In human tissues, naïve CD4+ T cells represent potential immune response cells that have not encountered antigen stimulation, playing roles in initiating and modulating immune responses. Upon activation through the specific T cell receptor [12], naïve CD4+ T cells can differentiate into various subgroups such as helper T cell type 1 (Th1), helper T cell type 2 (Th2), helper T cell type 17 (Th17), regulatory T cells (Treg), and follicular helper T cells (Tfh) [13]. Among these, Th1 cells release cytokines such as interferon-gamma, enhancing the activity of cytotoxic CD8+ T cells [14], thereby increasing their efficacy in identifying and eliminating tumor cells. Th1 cells also participate in chemokine production and inhibit tumor vasculature [15]. Th2 cells predominantly regulate antigen-specific immune responses, allergic reactions, and tissue homeostasis maintenance. Cytokines released by Th2 cells, such as interleukin (IL)-4, IL-5, and IL-13, regulate the balance of immune responses, potentially counterbalancing certain immune reactions, compared with Th1 cells [16, 17]. In some instances, Th2 cells may promote inflammatory responses around tumors, particularly those related to allergic reactions and tissue repair [15, 18]. Th17 cells primarily govern inflammation and immune responses [19], participating in immune responses by producing cytokines such as IL-17, which may exert specific effects on certain tumor immune responses [14]. Th17 cells may influence other immune cell infiltration through the production of chemokines, thereby promoting immune response against tumors [20]. The primary role of Treg cells within the immune system is to maintain immune balance and suppress excessive immune responses, preventing autoimmune diseases and inflammatory reactions [21]. However, in the tumor immune microenvironment, their role is more complex and may sometimes contribute to an immunosuppressive effect [22]. Existing research indicates that Treg cells inhibit the activity of other immune cells by releasing inhibitory cytokines (such as IL-10 and transforming growth factor-β [TGF-β]), weakening the immune response against tumors [21]. Additionally, these cells can directly suppress the activity of other immune cells, such as by inhibiting the function of CD8+ T cells and antigen-presenting cells, thereby reducing their ability to kill tumor cells and present antigens [23, 24]. Tfh cells represent a special type of CD4+ T cells, the primary role of which is to promote B cell activation, proliferation, and differentiation in lymphoid tissues, aiding in the generation of high-affinity antibodies [25]. The activity of immune cells in tumor immunity may be partly regulated by Tfh cells in lymphoid tissues [26].

Given the uncertainties surrounding the roles of naïve CD4+ T cells and their differentiated subgroups in tumors, elucidating their roles in LUAD using appropriate validation methods would greatly benefit the formulation of tumor immunotherapy strategies. The Genome-Wide Association Studies (GWAS) database serves as a valuable tool for conducting Mendelian randomization analyses by integrating summary estimates from prior GWAS studies on single nucleotide polymorphism (SNP) exposures and outcomes. Mendelian randomization allows for the estimation of causal effects of exposures on outcomes and is effective in mitigating the influence of confounding variables and reducing bias in experimental findings [27, 28]. Additionally, this approach ensures that the experimental and control groups possess similar characteristics and potential factors at the initial stage, making differences observed at the end of the experiment more likely attributable to treatment effects rather than other external influences [29]. Mantel tests is an easy-to-implement and flexible method to test for correlation between two distance matrices [30, 31]. The Cancer Genome Atlas (TCGA) database, which includes transcriptome data and clinical information for LUAD, is widely used for exploring potential mechanisms. This study aims to employ a combined approach of Mendelian randomization and observational research to investigate the roles of naïve CD4+ T cells and their differentiated subtypes in LUAD, seeking potential causes leading to these outcomes.The methodology in this work is illustrated in Figure S1.

Materials and methods

Selection of GWAS data for immune cells

Summary statistics for each immune trait were obtained from the GWAS database (Table S1). A total of 336 T cell immune phenotypes were included, categorized into four types: absolute cell count (n = 60, 17.8%), median fluorescence intensity (MFI; n = 140, 41.6%), morphological parameters (n = 16, 4.1%), and relative cell count (RC; n = 120, 35.7%).

Note

MFI measures fluorescence signal strength by determining the MFI within a cell population. RC indicates quantities relative to a specific condition or reference point, rather than absolute cell count.

Selection of GWAS data for LUAD

GWAS data for LUAD were sourced from the European Bioinformatics Institute (EBI) database with the specific GWAS ID (ebi-a-GCST004744) [32], published in 2017, constituting the largest and most recent GWAS study on LUAD to date, encompassing 63,053 Europeans (Ncase = 11,273, Ncontrol = 55,483).

Selection of instrumental variables (IVs)

The significance level for each immune trait’s IVs was set at 1 × 10− 5. Single-nucleotide polymorphisms were pruned within 10,000 kb linkage disequilibrium distance (r2 threshold: 0.001) using R software (version 4.3.1).

Selection of LUAD data for observational studies

mRNA and clinical feature data for LUAD were obtained from TCGA database [33], comprising 59 noncancerous and 535 cancerous tissue samples. Clinical features included overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), progress-free survival (PFS), clinical outcomes (dead/alive), and TNM staging. Data were downloaded in FPKM for subsequent analysis and standardized using the Limma package in R [34], removing duplicate genes and genes with zero expression.

Assessment of immune cell infiltration and selection of cell markers

Using the ssGSEA algorithm via the GSVA package in R [35], the degree of immune cell infiltration was computed for each sample. Immune cell-specific gene sets were sourced from the TISIDB (http://cis.hku.hk/TISIDB/) and CellMarker (http://xteam.xbio.top/CellMarker/) database (Table S2). The CellMarker database was also used to select T helper cell markers based on studies with a sample size of > 6. Furthermore, cell markers specifically expressed in the lung were selected.

Survival and cox regression analysis between different cells

Survival analysis was conducted using the survival and survminer packages in R to evaluate the relationship between the time of event occurrence and different cell types [36]. Cox regression analysis, calculating hazard ratios (HRs) and confidence intervals, was performed using the survival package in R. Results were visualized using the ggplot2 package [37].

Pathway enrichment analysis between different cells

Pathway enrichment analysis between samples was performed using GSEA (https://www.gsea-msigdb.org/gsea/index.jsp) [38]. Compared with individual gene analysis, GSEA considers overall changes in gene sets, thus providing a more comprehensive biological interpretation. The Reactome database (http://reactome.org/) was used for pathway data as it focuses on cell signaling, metabolic pathways, and gene regulation [39].

Statistical methods

Causal relationships between the 336 T cell immune phenotypes and LUAD were primarily evaluated using the TwoSampleMR package in R. Inverse variance weighted (IVW), weighted median, simple mode, and weighted mode methods were employed to assess the effects of immune cells on LUAD. The MR Egger and IVW methods were used to test heterogeneity among selected IVs. The MR-egger_intercept test was utilized to exclude horizontal pleiotropy effects. Scatter and funnel plots were utilized to explore data heterogeneity and robustness. Additionally, for observational experiments, parameter tests including independent t-tests and ANOVA were used to calculate differences between samples. Pearson correlation analysis was used to identify variables correlations. And Mantel tests were employed for matrix correlations.

Results

Naïve CD4+ T cells have a protective effect on LUAD

Using the IVW method with a significance threshold of p < 0.05, we screened 336 T cell phenotypes, identifying 12 subtypes of T cells (Fig. 1A). The calculated results of 12 subtypes are exhibited in Table S3, accompanying with their P-value and FDR values. These 12 T cell subtypes were derived from two trait types: MFI (n = 8) and RC (n = 4). The calculation results are summarized in Table 1.

Fig. 1
figure 1

. Role of T cells in LUAD. (A) Filtering of the 336 phenotypes of T cells using the IVW method revealed 12 T cell phenotypes showing significant difference (p < 0.05). (B) The forest plot displays the number of SNPs, hazard ratios (HRs), 95% confidence intervals, and FDR values under different methods

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Table 1 Calculation results for the 12 subtypes of T cells
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Applying the false discovery rate (FDR) method to correct original p-values, only CD4 on naïve CD4+ T cells showed significant differences (FDR < 0.05). The forest plot in Fig. 1B presents results from five calculation methods. Primary algorithms such as IVW and weighted median demonstrated significant differences with consistent HRs, indicating the inhibitory effect of CD4 on naïve CD4+ T cells on LUAD. Although secondary algorithms such as MR Egger, simple mode, and weighted mode did not exhibit significant differences, their HRs < 1.00 suggested a trend towards the inhibitory effect of CD4 on naïve CD4+ T cells on LUAD. Scatter and funnel plots for CD4 on naïve CD4+ T cells are provided in Figure S2.

Subsequently, we validated the Mendelian randomization results using LUAD mRNA data from TCGA database. Combining this with the CellMarker database, different infiltration levels of naïve CD4+ T cells in LUAD exhibited significant differences (p = 0.04) and demonstrated a tumor inhibitory effect (HR = 0.71; Fig. 2). However, since naïve CD4+ T cells have not been exposed to antigens, possess weak immune memory capacity, and have not undergone immune responses, analyzing the role of their specific subtypes in LUAD is of practical significance. Due to the absence of data on naïve CD4+ T cell differentiation cells in GWAS, we proceeded to explore the role of relevant cells in LUAD using TCGA database.

Fig. 2
figure 2

Survival curves and survival analysis results. The primary endpoint is overall survival (OS). The risk table displays the number of remaining risks, followed by the event count at each time point. The results suggest that high levels of naïve CD4+ T cell infiltration are associated with an extended OS in patients

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Subtype cells from naïve CD4+ T cell differentiation exhibit a dual effect on LUAD

We employed the ssGSEA algorithm to compute the infiltration scores of Th1, Th2, Th17, Treg, and Tfh in each sample. Detailed dataset characteristics are available in the Table S2. To investigate the relationship between these five cell types and clinical characteristics, we initially analyzed across four time dimensions: OS, DSS, DFS, and PFS. The results of this analysis across the four time dimensions are presented in Fig. 3.

Fig. 3
figure 3

Forest plot based on univariate Cox regression analysis results. Hazard ratios for different infiltration levels of Th1, Th2, Th17, Treg, and Tfh regarding the different study endpoints: (A) overall survival (OS); (B) disease-specific survival (DSS); (C) disease-free survival (DFS); (D) progression-free survival (PFS)

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When OS served as the primary study endpoint, univariate Cox regression analysis revealed that Th2 had a significant promoting effect on LUAD (p = 0.02; Fig. 3A). Similarly, when focusing on DFS or PFS as the study endpoint, the results indicated a significant promoting effect of Th2 on LUAD (p < 0.05; Fig. 3C-D). However, when DSS was the primary study endpoint, the results indicated that Treg had a significant inhibitory effect on LUAD (p = 0.03; Fig. 3B).

We further investigated the relationship between clinical staging, TNM, and Th2, as well as Treg, but found no statistically significant differences (Figure S3). To understand the reasons behind these observations, we conducted GSEA signal pathway enrichment analysis on the samples, the results of which are presented in Fig. 4.

Fig. 4
figure 4

GSEA pathway enrichment analysis and correlation analysis of key regulatory factors or cells. (A) GSEA demonstrates an association between increased Th2 infiltration levels and the Ub-specific processing proteases pathway. (B) Correlation analysis between Th2 infiltration levels and MDM2, MDM4, FOXO4, and TP53. (C) GSEA reveals an association between increased Treg infiltration levels and the neutrophil degranulation pathway. (D) Correlation analysis between Treg infiltration levels and IRF1, transcription factors BATF3, activated dendritic cells, and activated CD8+ T cell

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We classified different infiltration levels of Th2 and Treg using median infiltration scores, followed by GSEA. The results were ranked based on the Normalized Enrichment Score (NES) [40]. Analysis of high-level Th2 infiltration revealed enrichment of 168 genes in the Ub-specific processing proteases pathway (NES = 1.49, nominal [NOM] p = 0.04, FDR q-value = 1.00; Fig. 4A). Interestingly, Ub-specific processing proteases (USPs) are involved in the deubiquitination of oncoproteins such as MDM2, MDM4, TP53, and FOXO4 [41,42,43,44,45]. Correlation analysis between these four genes and Th2 cell infiltration levels showed the strong correlation of Th2 cells with MDM2 (R = 0.27, p < 0.001; Fig. 4B), while the remaining three genes showed no statistical differences. This suggests that increased Th2 cell infiltration might influence the deubiquitination of MDM2.

Analysis of high-level Treg cell infiltration revealed enrichment of 473 genes in neutrophil degranulation (NES = 2.51, NOM p < 0.01, FDR q-value = 0.01; Fig. 4C). Neutrophil degranulation is a significant part of neutrophil responses, with studies suggesting its involvement in tumor-killing processes [46]. However, this process requires the participation of transcription factors BATF3, cDC1 (activated dendritic cells), CD8+ T cells, and the transcriptional regulator and tumor suppressor IRF1 [46, 47]. Correlation analysis revealed the strong association of Treg cell infiltration with activated dendritic cells (R = 0.75, p < 0.001), followed by IRF1 (R = 0.55, p < 0.001), activated CD8+ T cells (R = 0.52, p < 0.001), and the transcription factor BATF3 (R = 0.40, p < 0.001; Fig. 4D). This suggests that increased Treg cell infiltration might enhance neutrophil responses for tumor clearance.

Utilizing CD4 to label subtypes of cells differentiated from naïve CD4+ T cells

To investigate the impact of T helper cell subtypes differentiated from naïve CD4+ T cells on LUAD, we assessed the correlation between these subtypes and cell markers using the Mantel test. Figure 5 A illustrates the strong correlation of T helper cell groups (Th1, Th2, Th17, Treg, and Tfh) with CD4 (R = 0.79, p < 0.01), followed by CD3E (R = 0.37, p < 0.01), CD3D (R = 0.37, p < 0.01), CCR4 (R = 0.34, p < 0.01), CXCR3 (R = 0.34, p < 0.01), GATA3 (R = 0.25, p < 0.01), CCR6 (R = 0.18, p < 0.01), GP1BA (R = 0.06, p < 0.01), and IL4 (R = 0.04, p > 0.05). Consequently, we employed CD4 to identify T helper cell groups, substituting CD4 expression levels for T helper cell infiltration, to further examine their role in LUAD.

Fig. 5
figure 5

Role of CD4-labeled T helper cell groups in LUAD. (A) Correlation of subtypes of cells (T helper cells) differentiated from naïve CD4+ T cells with cell markers. (B) Survival curves of different T helper cell infiltration levels when OS is the study endpoint. (C) GSEA pathway enrichment analysis. (D) Correlation of CD4-labeled T helper cells with four key regulatory genes or cells involved in neutrophil-mediated tumor-killing processes. (E) Correlation of CD4-labeled T helper cells with two key regulatory genes involved in the MAPK family signaling pathway. (F) Correlation of CD4-labeled T helper cells with two key regulatory genes or cells involved in platelet activation, signaling, and aggregation

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T helper cells demonstrated bidirectional regulatory effects on LUAD. Using median expression levels, CD4-labeled cells were categorized into high and low groups. When OS was the primary endpoint, survival analysis indicated a trend towards promotion by CD4-labeled T helper cells in LUAD (HR > 1.00; Fig. 5B). However, this result lacked statistical significance (p = 0.44). Subsequently, GSEA was conducted on CD4-labeled T helper cells, revealing enrichment of 473 genes in neutrophil degranulation (NES = 2.25, NOM p < 0.001, FDR q-value = 0.03), 323 genes in the MAPK family signaling pathway (NES = 1.82, NOM p < 0.001, FDR q-value = 0.11), and 259 genes in platelet activation, signaling, and aggregation (NES = 1.99, NOM p < 0.001, FDR q-value = 0.81; Fig. 5C).

To validate the impact of these pathways on LUAD, we analyzed the correlation between CD4-labeled T helper cells and key regulatory genes or cells. Among the four key regulatory genes or cells involved in neutrophil-mediated tumor eradication, CD4-labeled T helper cells exhibited the strong correlation with activated dendritic cells (R = 0.71, p < 0.001), followed by IRF1 (R = 0.53, p < 0.001), activated CD8+ T cells (R = 0.49, p < 0.001), and the transcription factor BATF3 (R = 0.25, p < 0.001), suggesting that increased T helper cell infiltration might enhance neutrophil response for tumor clearance (Fig. 5D).

Similarly, among the two key regulatory genes involved in the MAPK family signaling pathway, CD4-labeled T helper cells displayed the mild correlation with MAPK1 (R = 0.24, p < 0.001), followed by MAP2K1 (R = 0.22, p < 0.001; Fig. 5E), indicating that T helper cells might promote LUAD development by upregulating the MAPK family signaling pathway [48]. Regarding the two key regulatory genes or cells associated with platelet activation, signaling, and aggregation, CD4-labeled T helper cells exhibited the high correlation with platelets (R = 0.74, p < 0.001), followed by the key gene GP1BA for initiating platelet activation (R = 0.39, p < 0.001; Fig. 5F), suggesting that T helper cells might enhance platelet activation and adhesion processes to promote LUAD development.

Discussion

This study combined Mendelian randomization and observational studies to investigate the impact of naïve CD4+ T cells and their differentiation subtypes on LUAD, aiming to discern the influence of various cell types on clinical outcomes and explore potential regulatory pathways. Additionally, employing Mantel tests to identify the biomarkers of cell populations introduced a novel approach to studying the overall effect of cell populations on tumors.

Initially, Mendelian randomization was used to explore the causal relationship between 336 T cell immune phenotypes and LUAD. The results indicated an inhibitory effect of CD4 on naïve CD4+ T cells on LUAD, which was corroborated in observational experiments. However, naïve CD4+ T cells are not the ultimate effector cells; they primarily differentiate into Th1, Th2, Th17, Treg, and Tfh.

Assessing the effects of these five subtypes on LUAD across four time dimensions, clinical staging, and TNM, we found that Th2 cells promoted LUAD in three time dimensions (OS, DFS, and PFS). This promotion could be attributed to the activation of procarcinogenic effects as Th2 cell infiltration increased, ultimately leading to tumor growth. Consequently, GSEA pathway enrichment analysis was conducted to explore the impact of increased Th2 cell infiltration on signaling pathways. The results revealed a positive correlation between Th2 cell infiltration and the USP pathway, which is involved in the deubiquitination of proteins such as MDM2, MDM4, FOXO4, and TP53. Correlation analysis suggested a stronger correlation between Th2 cells and MDM2, a major negative regulator of TP53, and MDM2 can induce the degradation of the TP53 [49, 50]. Additionally, Th2 cells are positively correlated with MDM2, while the correlation test with TP53 did not report statistical significance. This implies that Th2 cells may weaken the antitumor effect of TP53 through MDM2 deubiquitination. Additionally, Th2 cells inhibit Th1 cells, which are known to activate antitumor immunity, while downregulating Th1 response [51]. In patients with malignant tumors, imbalance in the Th1/Th2 ratio (Th2 bias) has been associated with tumor progression [52]. However, further experiments are warranted to validate these findings at the protein level.

Interestingly, Treg cells exhibited an inhibitory effect on LUAD in one time dimension (DSS) in univariate Cox analysis, supported by Mendelian randomization results (p < 0.05). Combining the Mendelian randomization results, three immunophenotypes of Treg cells demonstrated a promoting effect on the tumor (CD25 on CD45RA+ CD4 not regulatory T cell, CD4 Treg cell %T cell, and CD25++ CD4+ regulatory T cell %T cell), whereas two cells showed inhibitory effects (CD25 on resting CD4 regulatory T cell and CD3 on CD39+ resting CD4 regulatory T cell), although their FDR values were > 0.05. To explore why Treg cells exert an antitumor effect, we used GSEA to analyze the signal pathways associated with increased Treg cell infiltration, which indicated a correlation between increased Treg cell infiltration and neutrophil degranulation. Neutrophil degranulation is a component of the neutrophil response, and IRF1 serves as a crucial upstream regulator in this response [46, 53]. Neutrophil response stimulates cDC1 cells to release cytokines, which in turn activate CD8+ T cells to exert antitumor actions [46]. Additionally, elevated BATF3 expression can promote classical dendritic cell differentiation into cDC1 cells [46]. Subsequently, the correlation analysis between Treg cells and the aforementioned four key regulatory genes or cells showed a strong positive correlation between Treg cells and activated DCs, activated CD8+ T cells, and IRF1 (R > 0.40, p < 0.001) and a moderately strong positive correlation between Treg cells and the transcription factor BATF3 (R = 0.25, p < 0.001). The above results demonstrated that increased Treg cell infiltration may favor tumor eradication by neutrophils. Previous studies have identified three types of Treg cells based on CD45RA and FOXP3 expression: resting (rTreg), activated (aTreg), and nonsuppressive (nTreg) Treg [54]. Increased inflammatory signals or antigens promote differentiation from rTreg to aTreg to secrete immunosuppressive factor TGF-β, thereby suppressing immune cell-mediated immune responses [55]. Our findings suggest that tumor inhibition may occur through neutrophil response before rTreg activation and subsequent TGF-β secretion. Thus, Treg cell subtypes may have a bidirectional effect on tumors.

Overall, the function of a single type of cell is easily influenced by other cells or factors within the tumor microenvironment, resulting in more complex cell functions. Furthermore, analyzing the impact of the entire T helper cell population on LUAD using the CellMarker database, we observed a strong positive correlation between CD4 and the T helper cell population (Mental’s r = 0.79, Fig. 5A). Therefore, CD4 was used as a marker for the T helper cell population, substituting its expression level for the degree of T helper cell infiltration. Although survival analysis showed a trend of promotion by the T helper cell population towards LUAD, this trend lacked statistical significance. This emphasizes the dual regulating function of T helper cell populations on LUAD. Subsequently, GSEA showed that neutrophil degranulation, MAPK family signaling pathways, and platelet activation, signaling, and aggregation were enriched (Fig. 5C). The MAPK family signaling pathways and platelet activation, signaling, and aggregation have been previously established to exert strong procarcinogenic effects [56, 57], while neutrophil degranulation exhibits the inhibition of cancer [46]. To investigate the roles of these three pathways in LUAD, we conducted correlation analysis of key regulatory genes and cells, which demonstrated an increase in the expression of crucial regulatory factors or cells within these pathways as T helper cell population infiltration increased (Fig. 5D-F). This indicates the presence of both positive and negative regulatory mechanisms within the tumor immune microenvironment of LUAD, supporting the findings of the survival analysis.The above results deepen our understanding of the role of naïve CD4+ T cells and their differentiated subtypes in LUAD.

Our study’s strength is derived from the appropriate integration of Mendelian randomization and observational studies, enhancing the reliability of experimental findings and introducing a novel research mode for future clinical oncology investigations. Additionally, our utilization of the Mantel Test to examine the correlation between cell populations and cell markers represents a pioneering effort, introducing a novel measurement technique and opening avenues for exploration of the impact of cell populations on cancer, which to the best of our knowledge has rarely been reported before. Furthermore, based on the data from the work, we found that distinct subtypes of Treg cells exhibit contrasting effects on LUAD (aTreg cells secrete TGF-β to promote tumorigenesis, whereas rTreg may inhibit tumorigenesis through neutrophil responses), offering novel perspectives for future research aimed at targeting Treg cells. Meantime, the experimental results outlined above will offer valuable insights for the development of novel cell differentiation-inducing agents, such as blocking the differentiation of some naïve CD4+ T cells and avoiding the conversion of rTreg cells to aTreg.

Despite the valuable insights provided by this study, there are some limitations: (1) Incomplete data in Mendelian randomization analysis, particularly lacking immunophenotype data such as Th1, Th2, Th17, and Th22. (2) Limitations of the ssGSEA algorithm in accurately estimating immune cell infiltration due to dependency on a limited number of characteristic genes, potentially leading to inaccuracies. (3) Lack of supporting experimental evidence for the promoting effect of Th2 cell immune infiltration on tumor growth, necessitating further experimental verification. (4) The correlation between T helper cell populations and cellular markers was not thoroughly consistent according to Mantel’s test, indicating the potential benefit of incorporating single-cell sequencing technology in future investigations. (5) The focus of our research primarily centers on cellular level, with limited investigation into the cytokines produced by immune cells and their interplay. This aspect will be further elucidated in subsequent studies.

Conclusion

In conclusion, the regulatory role of naïve CD4+ T cells and their differentiated subtypes in LUAD is complex and exhibits clear bidirectional effects. In addition to the presence of signaling pathways that promote cancer formation, there are active signaling pathways that prevent tumor growth. Developing novel cell differentiation-inducing agents that enhance anticarcinogenic signaling pathways at tumor sites and reverse potential procarcinogenic regulatory processes holds promise and potential benefits for cancer therapy.

Data availability

The information of GWAS summary statistics data were all publicly available in GWAS Catalog (https://www.ebi.ac.uk/gwas/home). The raw data used in section for observational experiment could be downloaded from the TCGA database (https://portal.gdc.cancer.gov/) .

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Acknowledgements

We acknowledge TopEdit LLC for the linguistic editing and proofreading during the preparation of this manuscript.

Funding

This work was supported by CSCO-Pilot Cancer Research Fund (NO.Y-2019AZZD-0352); Shandong Province Natural Science Foundation innovation and development joint fund project (NO. ZR2022LZL008).

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Authors and Affiliations

  1. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, 250117, China

    Runze Liu, Feihu Chen, Shuangqing Lu & Hui Zhu

  2. Department of Respiratory Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, 250117, China

    Guangjian Yang

  3. Department of Thoracic Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, 250117, China

    Hongbo Guo

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  1. Runze Liu
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  4. Feihu Chen
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Contributions

Liu RZ conceived and/or designed the work that led to the submission and drafted or revised the manuscript. Yang GJ acquired data, and played an important role in interpreting the results. Guo HB played an important role in interpreting the results. Chen FH acquired data. Lu SQ acquired data. Zhu H conceived and/or designed the work that led to the submission and approved the final version.

Corresponding author

Correspondence to Hui Zhu.

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Research ethics exemption was granted by the ethics committee/institutional review board of the Shandong Cancer Hospital and Institute (SDTHEC2024001027), because of the de-identified feature of the information collected from public database. For genome-wide association study (GWAS) datasets and TCGA database, ethical review and approval can be accessed in the original studies.

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Liu, R., Yang, G., Guo, H. et al. Roles of naïve CD4+ T cells and their differentiated subtypes in lung adenocarcinoma and underlying potential regulatory pathways. J Transl Med 22, 781 (2024). https://doi.org/10.1186/s12967-024-05530-y

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Journal of Translational Medicine

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