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Abstract

Objective

To investigate the mechanism of tumor-associated macrophages (TAM) in the invasive migration of lung adenocarcinoma (LUAD) cells.

Methods

The single-cell sequencing data of lung adenocarcinoma (GSE131907) were initially analyzed. Kaplan-Meier curve analysis, univariate as well as multivariate Cox analysis, and immunofluorescence staining were performed. The analysis of fibroblast-macrophage interactions using Single-cell CellChat revealed their relationship. Subsequently, we screened and validated the target proteins in macrophages that interact with SEMA3C. The effects of these interactions on lung cancer cell migration and invasion were evaluated in vitro through Western blot analysis to assess phenotypic changes in macrophages, as well as through Transwell migration and invasion assays.

Results

SEMA3C was predominantly expressed in fibroblasts of patients with high-grade lung adenocarcinoma at high levels. SEMA3C exhibited independent prognostic significance in determining the overall survival outcome among individuals diagnosed with lung adenocarcinoma. Lung adenocarcinoma fibroblasts had elevated SEMA3C. CellChat demonstrated enhanced interactions between TAM as well as T cells. A high expression of vascular non-inflammatory molecule 1 (VNN1) in fibroblast macrophages during Stage II-III, and this elevated VNN1 was also an independent prognostic factor. The interaction between cancer-associated fibroblasts (CAFs) and VNN1 on macrophage membranes mediated by SEMA3C. Furthermore, these experiments demonstrated that SEMA3C regulates the polarization of TAM through VNN1, thereby influencing lung cancer.

Conclusion

The phenotype of TAM is regulated by SEMA3C, which in turn influences the migration as well as invasion of lung cancer cells through VNN1.

Keywords

lung adenocarcinoma CAFs macrophage polarization SEMA3C VNN1

Introduction

In the past few years, there has been a continuous increase in the prevalence and fatality rates of lung cancer, with lung adenocarcinoma (LUAD) emerging as one of the frequently encountered malignant tumors affecting the lungs. Approximately 30%-55% of patients experience metastasis and recurrence post-surgery, which significantly impacts the long-term survival rate of individuals with this diseas.^1,2 Therefore, the investigation of the molecular mechanisms underlying the initiation, progression, and metastasis of LUAD, as well as the identification of novel early diagnostic markers, have emerged as prominent areas of focus in current tumor biology research.

The composition and dynamic changes of various types of cells in tumor microenvironment (TME) play a key role in tumor immunoregulation, which is a key factor affecting the effect and prognosis of tumor immunotherapy.³ The TME comprises various components, including cancer-associated fibroblasts (CAFs), which play a crucial role in tumor progression, invasion, metastasis, and response to treatment.⁴ The findings of various studies have demonstrated that CAFs not only facilitate the progression and recurrence of tumors through their interaction with tumor cells, but also contribute to the establishment of an immunosuppressive microenvironment and exhibit a strong association with resistance to immunotherapy.^5,6 Current studies have focused on the role of tumor-associated macrophages in lung adenocarcinoma. For example, SLC3A2 promotes tumor-associated macrophage polarization through metabolic reprogramming in lung cancer.⁷ An IL6-STAT3-C/ebp - il6 positive feedback loop in tumor-associated macrophages promotes EMT and metastasis in LUAD.⁸ Nevertheless, limited research has been carried out regarding the specific target cells and mechanism of action employed by CAFs within the tumor microenvironment in cases of lung adenocarcinoma.

The semaphorins (Sema) constitute a protein family with prevalent occurrence throughout the human body, characterized by shared structural domains.⁹ The secreted proteins of Sema primarily consist of Sema3a, Sema3b, Sema3c, Sema3d, Sema3e, Sema3f and other variants. Most of these Sema play a role in inhibiting tumor growth, such as Sema3a, Sema3b, Sema3e, Sema3f and so on. It has been observed that Sema3c exhibits significant upregulation in diverse tumor tissues and contributes to the facilitation of tumor cell growth as well as metastasis, such as gastric cancer, hepatocellular carcinoma, breast cancer, oral cavity tumors, etc..^10–14 Sema3c is also positively correlated with the prognosis of gastric cancer and breast cancer.^15,16 In in vitro cellular experiments, Sema3c was found to promote the proliferation, metastasis and vascularization of epithelial cells, and therefore is closely related to the formation of neovascularization in tumors.^17,18 A recent study found that full-length Sema3c inhibited the formation and metastasis of tumor lymphatic vessels. In summary, Sema3c may have different roles in different tumors, but its specific molecular regulatory mechanism and clinical significance remain to be investigated. There are no relevant literature reports on the role and mechanism of Sema3c in LUAD.

The objective of this study was to explore the attributes and medical importance of Sema3c in CAFs through analysis of their regulation on macrophages within the tumor microenvironment, as well as elucidating the underlying molecular mechanisms. CellChat analysis using single-cell data revealed that CAFs in lung adenocarcinoma exhibit stronger interactions with macrophages compared to normal tissues. Bioinformatics analysis was employed to screen the proteins targeted to macrophages in CAFs, and the impact of CAFs on macrophage polarization was further confirmed by analyzing clinical samples as well as conducting cellular experiments in vitro. This study aimed to elucidate the impact and regulatory role of Sema3c on CAFs in lung adenocarcinoma, specifically focusing on its effect on macrophages, migration, and invasion of LUAD cells. The above results provide novel insights for potential clinical treatments for lung adenocarcinoma.

Materials and methods

Data acquisition and analysis

Lung adenocarcinoma-related histologic data were collected through the TCGA (The Cancer Genome Atlas) data portal (https://cancergenome.nih.gov). The TCGA database was utilized to obtain the TCGA cohorts, RNA-seq data, and associated clinical details.

The curves depicting the levels of s and the corresponding survival probabilities in lung adenocarcinoma patients, as well as the levels of RCN3 and their associated survival probabilities in GC patients, were generated by analyzing clinical data. We employed the Kaplan-Meier method to perform survival analysis. Differentially expressed genes were identified using a significance threshold of P less than 0.001, and the screening criteria for differential expression included |log2(Fold Change)| more than 1 as well as P less than 0.001. For differential genes (DEGs) between controls and samples, limma R encapsulation was used to obtain differential genes with threshold log2FC >1 and P value <0.05. The single-cell data had a cell proportion threshold of 0 (thresh.pc = 0), a differential fold of 0 (thresh.fc = 0, only. pos = TRUE), and a p-value of 0.05 (thresh.p = 0.05).

Single-cell RNA sequencing analysis

Single-cell RNA sequencing data were acquired from the GEO database (Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/) for GSE131907 cohort details (comprising 8 cases at Stage I, 1 case at Stage II, and 2 cases at Stage III). The downstream analysis of scRNA-seq was conducted following the conventional procedure outlined in Seurat’s official repository (https://github.com/satijalab/seurat).

We utilized Seurat version 4.0 for the analysis and processing of scRNA-seq data. For quality control, we evaluated the raw gene-cell-barcode matrix of each cell based on three parameters: mitochondrial gene expression (capped at 20%), the number of unique molecular identifiers (UMIs), and the total gene count (set between 100 and 150,000 for UMIs and 200 to 10,000 for gene counts). Cells that did not meet the specified criteria were excluded. After performing quality control, we applied Seurat’s default normalization approach to normalize the data. Subsequently, we conducted dimensionality reduction on the normalized expression matrix using principal component analysis (PCA). The initial 30 principal components (PCs) were utilized as input for subsequent clustering analysis in Seurat. To mitigate batch effects across samples, we utilized the “Harmony” approach, which combines data from various conditions while maintaining biological diversity. Following harmonization, we conducted PCA again and determined the optimal number of principal components for clustering. We then applied Seurat’s graph-based clustering approach to perform unsupervised clustering on the dataset, with the resolution parameter configured at 0.4. We employed t-SNE and UMAP techniques for dimensionality reduction to visualize the clusters. Seurat’s FindMarkers function was utilized to determine the marker genes specific to each cluster. To pinpoint key genes, we conducted differential expression analysis among the clusters of particular interest.

Cell-cell communication analysis

To construct cell-cell interaction networks, the CellChat R package (version 2.1.2, available at https://github.com/sqjin/CellChat) was employed. For analyzing pathway information, the study referred to the “CellChatDB” database (version 2.0), which comprises a meticulously compiled set of documented ligand-receptor interactions and their corresponding signaling pathways. This database offers comprehensive annotations regarding cell-cell communication processes and their functional significance across different biological settings. First, the annotated Seurat object was transformed into a CellChat object. Next, the identifyOverExpressedGenes function was utilized to detect ligands and receptors that showed increased expression in particular cell types. Subsequently, the gene expression information was projected onto a protein interaction network through the use of the projectData function. To detect pertinent ligand-receptor interactions, we applied the identifyOverExpressedInteractions function to screen for interactions in which either the ligand or the receptor exhibited overexpression in at least one cell type. For every detected interaction, CellChat assigned a probability score to indicate how likely the interaction was to occur and evaluated the significance of this interaction. This probability estimation incorporated both the gene expression data and existing knowledge about known ligand-receptor interactions, thereby enabling a more reliable and biologically relevant analysis.

Experimental reagents

CD206 (PA5-101657) antibody, CD68 (14-0687-82) antibody, β-tubulin (ab7291) antibody, iNOS (ab178945) antibody, αSMA (ab5831) antibodies were purchased from abcam, USA. Fetal bovine serum (10099-141), RPMI 1640 medium (11875119) were purchased from Gibco, USA. SEMA3C (PA5-24997) antibody was purchased from Invitrogen, USA. Type IV collagenase (BS165) and type II collagenase (BS164) were purchased from Biosharp.

Cell lines

Human monocytic leukemia cells THP-1 were purchased from ATCC cell bank, and human lung adenocarcinoma cells A549 were purchased from Shanghai Cell Repository of Chinese Academy of Sciences. THP-1 cells as well as A549 cells was cultured in RPMI1640 (10% FBS, 1% double antibody) complete medium, 37°C, 5% CO2, respectively.

THP-1 monocyte polarization and establishment of TAM

Stimulation of THP-1 cells with PMA (10 ng/ml) was initially performed for 48 h to differentiate into macrophages, and co-culture with lung cancer A549 cells was performed for 48 h to differentiate into TAM.

Study population

In this study, 30 cases of CT-guided lung aspiration biopsy with pathologic diagnosis of lung adenocarcinoma were collected from the First Hospital of Qiqihar. The sections of LUAD patients’ primary foci that have been fixed with formalin and embedded in paraffin, and general information, clinical characteristics, imaging and follow-up data of the patients were obtained. The Ethics Committee of First Hospital of Qiqihar (2024-KY-25) granted approval for the acquisition of patient samples and data, with all patients providing their informed consent by signing appropriate forms. Patient efficacy evaluations were obtained by at least 2 hematology clinicians in the study according to the consensus of the Revised RECIST Guidelines for Criteria for Evaluation of Efficacy in Solid Tumors (version 1.1). The study was performed with reference to the outline of the Declaration of Helsinki.

RT-PCR

RNA was extracted using the TRIZOL method. Then, the extracted RNA was reverse-transcribed into cDNA, and mRNA was reverse-transcribed into cDNA using reverse transcriptase, followed by PCR amplification. The fluorescent signal was monitored in real time by adding a fluorescent dye, SYBR Green I (Solarbio, China), to the PCR reaction system. The levels of expression were determined by means of 2-(^ΔΔCt). The Ct value of each transcript was normalized to GAPDH, resulting in the calculation of an average threshold cycle. The q-PCR analysis employed primer sequences as listed in Table 1.

Table 1.

Primer sequences.

Gene	Forward primer,5′-3′	Reverse primer,5′-3′
Sema3C	CAAAGATCCCACACACGG	ACTTGGTCCTCTGATCTCCTC
CCL17	ATTACGTCCGTTACCGTCTGC	TCCCTGAAGGTTAGCAACACC
CCL22	ATCGCCTACAGACTGCACTC	GACGGTAACGGACGTAATCAC
IL-1β	CCTGAACCTTCCAAAGATGGC	TTCACCAGGCAAGTCTCCTCA
TNF-α	CCTCTCTCTAATCAGCCCTCTG	GAGGACCTGGGAGTAGATGAG
GAPDH	TTTGCGTGTTGGTTGGAGTAT	CTAGCCTCCCGGGTTTCTCT

Western-blot

The RIPA cell lysate was used to extract the contents of tissues/cells, and centrifugation was performed to collect the overall protein content present within cells/tissues. The quantification of protein concentration was performed using the BCA method. Then SDS-PAGE electrophoresis was conducted on each tissue/cell sample. A transfer device was used to transfer the separated proteins onto a PVDF membrane. Subsequently, the PVDF membrane was detached and sealed with 5% skimmed milk for 1 hour before being rinsed with TBS. The PVDF membrane was detached and sealed with 5% skim milk for 1 hour, followed by rinsing with TBS. The antibody specific to the marker protein was left to incubate overnight at a temperature of 4°C. Subsequently, the secondary antibody was added and left to incubate for a duration of an hour, using the recommended dilution of 1:2000-3000 as per instructions. Finally, chemiluminescence imaging system was employed to expose and detect signals from the PVDF membrane

Elisa

CCL17 Elisa kit (RAB0050), CCL22 Elisa kit (RAB0064) were purchased from sigma, IL-1β Elisa kit (ab217608), TNF-α Elisa kit (ab181421) were purchased from Abcam. The experimental instructions were adhered to during the implementation of the subsequent procedure. Dispense 50 μL of diluted standard as well as 50 μL of the sample under examination into the reaction wells, promptly followed by adding 50 μL of biotin-labeled antibody. Cover the template, gently agitate, and incubate for 1 hour at 37°C. Remove any excess liquid from the wells, proceed to fill each well with washing solution, and vigorously shake for a duration of 30 s. Eliminate the detergent by gently shaking and blotting with absorbent paper. Carry out this procedure thrice. Next, introduce 80 μL of affinity streptavidin-HRP into each well, gently agitate the mixture, and allow it to incubate at a temperature of 37°C for a duration of 30 min. Remove the excess liquid from the wells and rinse them thoroughly three times. Subsequently, gently introduce 50 μL of substrate A as well as B into each well, ensuring proper mixing, followed by incubation at a temperature of 37°C for a duration of 10 min to prevent excessive cell density. Swiftly adjust the position of the plate, introduce termination solution of 50 μL without delay, and promptly evaluate the results following the addition of termination solution while measuring the optical density (OD) value at the specified wavelength for each well.

Cell migration and invasion assay

The Corning chamber was utilized to perform Transwell migration and invasion assays. The upper chamber was inoculated with a cell concentration of 5 × 10⁴ in a medium containing 2% FBS, whereas the lower chamber was supplemented with medium containing 20% FBS. After incubating at a temperature of 37°C for a duration of 48 h, the migratory or invasive cells were immobilized using a solution containing 4% paraformaldehyde and subsequently subjected to staining with hematoxylin and eosin (H&E). Subsequently, cell enumeration and microscopic imaging were performed using an Olympus CKX53 microscope.

Immunoprecipitation

The IP protein lysate was introduced to the cells and kept on ice for a duration of 20-30 min, after which it underwent centrifugation at 12,000 g at a temperature of 4°C for 15 min. The protein concentration of the entire cellular lysate was determined by employing the BCA assay using the supernatant obtained. Following that, protein samples (200 μg) were subjected to overnight shaking at 4°C with SEMA3C magnetic beads or VNN1 antibody for conjugation. The protein complexes attached to magnetic or agarose beads underwent three rounds of washing using IP washing solution. Subsequently, an adequate quantity of 1× sample buffer was added and the mixture was subjected to boiling at 100°C for a duration of 5 minutes in order to prepare for immunoblotting investigations.

Immunofluorescence

Tissue sections were fixed with polymethanol, and then washed three times using PBS for a duration of 5 min per rinse. The tissue or cell sections were treated with a 2% BSA solution for a duration of 30 min, followed by two washes with PBS. Subsequently, the primary antibodies RCN3 (1:2000) and αSMA (1:2000) were introduced and allowed to incubate at ambient temperature for a duration of 60 min. Afterwards, the sections underwent three rounds of PBS washing, with each round lasting 5 min. The corresponding secondary antibody was then added, diluted according to the instruction manual at a ratio of 1:2000-3000. Following a 30-min incubation at room temperature, the sections underwent three rounds of PBS washing, with each round lasting 5 min. DAPI staining was applied for 10 min and subsequently washed three times with PBS for 5 min each time. Finally, the slices were sealed and photographed under a microscope.

Lung adenocarcinoma CAFs and normal fibroblasts (NFs) cell isolation

The adjacent non-cancerous tissues as well as tumor tissues from 30 patients who underwent surgery for lung adenocarcinoma were collected. The primary cell culture technique was employed to isolate CAFs and normal fibroblasts (NFs), which were subsequently passaged for further cultivation. The tissues were collected and subjected to collagenase digestion for the cultivation of lung adenocarcinoma CAFs and NFs. The tissue samples were subjected to digestion using DMEM medium supplemented with 0.2% collagenase type IV and 0.1% collagenase type II at a temperature of 37°C for a duration of 3 h. The mixture obtained was subsequently subjected to filtration using a sterile filter having a mesh size of 200, and then underwent centrifugation at a speed of 1000 revolutions per minute for a duration of 5 min. After discarding the supernatant, the sediment was resuspended in PBS and subjected to two additional rounds of centrifugation. Finally, the sediment was resuspended in culture medium supplemented with 10% fetal bovine serum and placed in an appropriate culture environment. The precipitate was resuspended with medium containing 10% fetal bovine serum in a petri dish, and the medium was changed after 24 h, and the medium was changed once every 1 day.

Statistical analysis

GraphPad Prism9 software was applied for statistical analysis of data. The data were presented as mean ± SD. The means of two groups were compared using a t-test to identify any statistical differences, while a one-way ANOVA was employed to compare multiple groups for statistical distinctions. A significance level of p < 0.05 indicated the presence of statistically significant differences.

Results

SEMA3C is significantly upregulated in lung cancer

To explore the expression of SEMA3C in lung cancer, we downloaded the single-cell sequencing data GSE131907 for lung adenocarcinoma and conducted an analysis. The dataset consisted of 8 samples at Stage I, 2 samples at Stage III, as well as 1 sample at Stage II. Therefore, we grouped the data into Stage II-III versus Stage I. This grouping was also used in the TCGA dataset. Through the analysis, a total of 9 distinct cell subpopulations including Macrophage, B cell, CD8 + T cells, Epithelial cells, Fibroblasts, MAST cells, CD4 + T cells, Endothelial cells, and NK cells were identified (Figure 1(a)). SEMA3C was predominantly expressed in fibroblasts (Figure 1(b), and (c)) and it was highly expressed in fibroblasts in Stage II-III (t = 4.0369, p-value = 5.879e-05; Figure 1(d)). TCGA data showed that SEMA3C was up-regulated in patients with Stage II-III lung cancers (t = 2.4755, p-value = 0.01371; Figure 1(e)).

Figure 1.

Analysis of the lung adenocarcinoma data set. (A) UMAP plot of the single-cell sequencing data. (B) SEMA3C expression in various cell populations vlnplot plot. (C) Dot plot of SEMA3C expression in each cell population. (D) Violin plot of the SEMA3C. (E) Boxplot of the SEMA3C.*p less than 0.05,***p less than 0.001.

SEMA3C overexpression is linked to unfavorable prognosis

After analyzing the correlation between SEMA3C and various clinicopathological features, a significant association between high expression of SEMA3C and age (p = 0.044), N-stage (p = 0.005), T-stage (p = 0.075), as well as tumor stage (p = 0.002) (Table 2) was found. The Kaplan-Meier curves demonstrated that patients with high SEMA3C expression had a significantly shorter survival time compared to those with low SEMA3C expression (p = 0.00022; Figure 2(a)). The univariate and multivariate Cox analysis revealed that SEMA3C (HR = 1.5, p = 1.6e-02) independently served as a prognostic factor for overall survival in LUAD patients. Based on the data presented, it can be inferred that a correlation exists between elevated levels of SEMA3C expression and unfavorable prognosis.

Table 2.

SEMA3C and clinicopathological characteristics.

Characteristic	SEMA3C		χ²	p-value
Characteristic	High (N = 157)	Low (N = 157)	χ²	p-value
Age			4.057	0.044
≥60	67 (64%)	282 (74%)
<60	37 (36%)	97 (26%)
Gender			0.103	0.748
female	57 (55%)	201 (53%)
male	47 (45%)	178 (47%)
T			6.911	0.075
T1	27 (26%)	137 (36%)
T2	59 (57%)	198 (52%)
T3	11 (11%)	34 (9.0%)
T4	7 (6.7%)	10 (2.6%)
M			0.656	0.721
M0	71 (68%)	251 (66%)
M1	2 (1.9%)	13 (3.4%)
Mx	31 (30%)	115 (30%)
N			12.767	0.005
N0	58 (56%)	265 (70%)
N1	20 (19%)	70 (18%)
N2	25 (24%)	43 (11%)
N3	1 (1.0%)	1 (0.3%)
Stage			15.327	0.002
Stage I	49 (47%)	216 (57%)
Stage II	22 (21%)	96 (25%)
Stage III	30 (29%)	49 (13%)
Stage IV	3 (2.9%)	18 (4.7%)

Figure 2.

SEMA3C overexpression is linked to unfavorable prognosis. (A). Kaplan-Meier curves. (B) Univariate Cox analysis.(C) Multivariate Cox analysis.

Clinical validation of SEMA3C as a secreted protein highly expressed in lung CAFs

Tissue samples from clinical lung cancer patients were collected for immunofluorescence analysis, and it was revealed SEMA3C was up-regulated in the tissues of LUAD patients and with a major concentration in fibroblasts (Figure 3(a)). Literature search revealed that SEMA3C is a secreted protein, so we hypothesized that fibroblasts may deliver SEMA3C to cellular tissues. The lung cancer cells was co-cultured with fibroblasts. The isolated CAFs and NFs indicated the protein as well as mRNA expression levels of Sema3c in CAFs (Figure 3(b), and (c)). The protein of SEMA3C was also increased in the cell supernatants before and after co-culture and in the supernatant after co-culture (Figure 3(d)). Importantly, survival curve analysis showed that high expression of SEMA3C was associated with poor prognosis (Figure 3(e)).

Figure 3.

SEMA3C is highly expressed in lung CAFs. (A). Immunofluorescence plots of lung adenocarcinoma clinical samples. (B). RT-PCR assay to detect SEMA3C in each group. (C). Western blot for the protein of SEMA3C. (D). Elisa analysis of the expression of SEMA3C in the supernatants of cells. E. Survival analysis of SEMA3C levels versus survival probability curves for lung cancer patients. n = 3, **P less than 0.01.

CAFs influence macrophage polarization by secreting SEMA3C

To explore the specific molecular mechanisms of sema3c involvement in lung cancer, we performed cellchat analysis using single-cell data and found more cross-linking with normal control fibroblasts and with T cells and macrophages (Figure 4(a), and (b)). We overexpressed sema3c in fibroblasts (Figures 4(c), and (d)) and co-cultured these cells with THP-1 macrophages, which were differentiated from human monocytic leukemia cells using phorbol ester (PMA). We subsequently analyzed the expression of CD206, a surface marker for M2-type macrophages, and iNOS, a common marker for M1-type macrophages, using Western blot. Our results demonstrated that sema3c overexpression led to increased CD206 expression and decreased iNOS expression, indicating a shift in macrophage polarization (Figure 4(e)).

Figure 4.

High expression of Sema3c in fibroblasts regulates macrophage polarization. (A). CellChat interaction analysis. (B) CellChat visualizes cell communication networks. (C) qRT-PCR for the mRNA of Sema3c in each group. (D) Western blot assay for the protein of Sema3c. (E) Western blot for the protein of CD68, CD206 as well as INOS. (F) qRT-PCR for the mRNA of CCL17, IL-1β, CCL22 as well as TNF-α. (G) ELISA for TNF-α, IL-1β, CCL17, as well as CCL22 in macrophage supernatant. n = 3, **P less than 0.01.

The expression levels of IL-1β and TNF-α, which serve as markers for M1-type macrophages, and CCL17 and CCL22, which are indicators of M2-type macrophages, were assessed in macrophages following Sema3c overexpression using qRT-PCR.. The findings revealed a significant reduction in the expression of IL-1β and TNF-α, key markers of M1-type macrophages, while a notable increase was observed in the expression of CCL17 and CCL22, which are characteristic markers of M2-type macrophages. (Figure 4(e)). Then, we assessed the secretion and expression of M1-type macrophages and M2-type macrophages using ELISA, revealing an elevated secretion of M2-type macrophage markers CCL17 as well as CCL22 compared to macrophages co-cultured with normal fibroblasts. Conversely, there was a decrease in the secretion of M1-type macrophage markers TNF-α as well as IL-1β(Figure 4(f)). By employing three experimental methods, we have discovered that fibroblasts exert an influence on macrophage polarization through the secretion of SEMA3C. Moreover, the overexpression of sema3c in fibroblasts induces a shift in macrophage polarization towards M2-type macrophages while mitigating M1-type macrophage polarization.

SEMA3C interacts with macrophage VNN1 to affect macrophage polarization

To investigate the interaction between SEMA3C and macrophages, we conducted a comprehensive analysis by integrating 53 proteins associated with SEMA3C and intersecting them with SEMA3C-related genes (p < 0.05, |r|>0.3) and genes that showed statistical significance (p < 0.05) in univariate Cox regression. Through this analysis, five overlapping genes: NRP2, ANLN, SEMA3A, SEMA3D, and VNN1 were identified (Figure 5(a)). Notably, ANLN and VNN1 showed significant overexpression specifically in macrophages (Figures 5(b)–5(f)), while VNN1 was predominantly localized to the cell membrane. Meanwhile, the mRNA of ANLN and VNN1 was significantly higher than NRP2, SEMA3A and SEMA3D (Figure 5(g)). VNN1 is highly expressed in fibroblast macrophages in Stage II-III(6.7092, p-value = 2.188e-11; Figure 5(h)). The Kaplan-Meier curves demonstrated a significantly shorter survival time for the VNN1 high expression group compared to the VNN1 low expression group (p = 0.00071; Figure 5(i)). The expression of VNN1 (HR = 1.9, p = 3.5e-03) was identified as an autonomous prognostic indicator for the overall survival of individuals diagnosed with lung adenocarcinoma, according to both univariate and multivariate Cox analyses (Figures 5(j), and 5(k)).

Figure 5.

SEMA3C regulates macrophage polarization through VNN1. (A) Wayne diagram analysis of proteins or genes interacting with SEMA3C. (B) VNN1 expression by cell. (C). ANLN expression by cell. (D). SEMA3A expression by cell. (E). SEMA3D expression by cell. (F). NRP2 expression by cell. (G). Real-time PCR assay for the expression of NRP2, ANLN, SEMA3A, SEMA3D, VNN1. (H) Violin plot of VNN1 expression. (I) Kaplan-Meier curve analysis. (J) One-way Cox analysis. (K) Multifactorial Cox analysis. n = 3, *P less than 0.05, ***P less than 0.001.

SEMA3C regulates tumor-associated macrophage phenotype and influences lung cancer cell migration and invasion through VNN1

The investigation of SEMA3C’s impact on macrophage polarization through its interaction with VNN1 in macrophages was conducted using Co-IP experiments, revealing a significant association between SEMA3C and VNN1 proteins (Figure 6(a)). And macrophages knocking down VNN1 were constructed (Figure 6(b), and (c)). We extracted the supernatants of overexpressing SEMA3C fibroblasts and cultured them with macrophages knocking down VNN1. We found that knocking down VNN1 could reverse the effect of overexpressing SEMA3C on macrophage differentiation. Western blot experiments showed that the expression of CD206 was diminished, and that the expression of INOS was enhanced (Figure 6(d)). Both PCR and Elisa showed that the expression of IL-1β and TNF- α expression was increased, while CCL17 and CCL22 expression was decreased (Figure 6(e), and 6(f)). The impact of tumor-associated macrophages on the migratory as well as invasive capabilities of LUAD cells was indicated. The findings demonstrated a notable decrease in the migratory rate of the si-VNN1 group relative to the CON group (Figure 6(g)). VNN1 group had weakened invasion ability (Figure 6(h)). The above results showed that the migration rate as well as invasion ability of co-cultured A549 cells were weakened after inhibiting VNN1 in tumor-associated macrophages.

Figure 6.

VNN1 regulates the phenotype of tumor-associated macrophages and affects the migration and invasion of lung cancer cells. (A) Co-IP assay of SEMA3C with VNN1. (B) qRT-PCR for the Sema3c in each group of cells. (C) Western blot for the SEMA3C in each group of cells. (D) Western blot for the protein of CD68, CD206 as well as INOS. (E) qRT-PCR for the mRNA of CCL17, IL-1β, CCL22 as well as TNF-α. F. ELISA for TNF-α, IL-1β, CCL17, as well as CCL22 in macrophage supernatant. (G) Transwell migration assay to detect the secretion levels of CCL17, TNF-α, IL-1β, CCL22. (H) Western blotting for the SEMA3C in macrophage THP-1 cells. n = 3, *p less than 0.05, **p less than 0.01.

Discussion

The prevalence and fatality rates of LUAD continue to be consistently elevated in both China and the United States, with the latter witnessing higher mortality rates among males and females.¹⁹ The tumor epithelial cells of lung cancer and lung cancer cells have the ability to interact with each other, resulting in a highly heterogeneous tumor microenvironment in which they can survive.²⁰ The development of immunotherapeutic drugs for LUAD will be facilitated by a more comprehensive understanding of the lung cancer microenvironment, as we enter the era of targeted therapy. The presence of tumor-associated macrophages plays a crucial role in the intricate interplay of lung adenocarcinoma processes, encompassing its onset, progression, spread to other parts of the body, and therapeutic approaches. Immunotherapy has witnessed rapid advancements in recent years, offering new hope and a ray of light to patients battling advanced lung cancer by significantly extending their survival period. Immunotherapy is an integral component of the comprehensive treatment regimen for lung cancer, and it assumes a pivotal role in the management of this disease, encompassing preoperative neoadjuvant immunotherapy to postoperative adjuvant immunotherapy. Through conducting more comprehensive research on the mechanism of TAMs in lung cancer, we will be able to unravel their intricate interplay with the development of lung cancer and offer innovative perspectives and tactics to the underlying mechanisms and treatment approaches for this disease.

This study examined the effects of SEMA3C in CAFs on the survival outcomes of patients with lung adenocarcinoma and its impact on the immune microenvironment. This was achieved by analyzing TCGA data for lung adenocarcinoma using methods such as variance testing, survival analysis, and single-cell CellChat analysis. In addition, it was discovered that SEMA3C present in CAFs has the ability to induce polarization of M2 by targeting VNN1. This subsequently influences the migratory and invasive potential of lung carcinoma cells.

Discovered in 1992, Semaphorins proteins are a family of neural-directed substitution-giving proteins. Initially believed to primarily regulate nerve growth cones for directing axonal growth direction, recent research findings have brought to light their participation in the regulation of diverse cellular activities, including proliferation, cell cycle control, adhesion, and movement. Furthermore, they also contribute significantly to the modulation of the immune system, promotion of blood vessel formation, and facilitation of tumor growth through their regulation of immunomodulatory processes, vascular neogenesis, and tumorigenesis.^21–23 Within this group, neuropilin2 is initially targeted by sema3c, leading to the activation of intracellular signaling pathways via plexinA, and influences downstream molecules.²⁴ Neuropilin is a receptor for VEGF, suggesting that sema3c may also be related to tumor angiogenesis. Sema3c may compete with VEGF for the receptor, suggesting that it may also be associated with tumor angiogenesis. Sema3c can compete with VEGF for the receptor and inhibit angiogenesis by activating the inhibitory signaling pathway to cut off the signaling pathway of pro-angiogenic factors. Furthermore, the presence of Sema3c has the potential to cause a breakdown in the structure of gonadotropin endothelial cells and hinder their ability to multiply through the inhibition of the VEGF pathway. The implications of these findings suggest that sema3c has the potential to act as a facilitator in promoting tumor growth. Lung cancer cell lines exhibiting elevated levels of sema3c tend to generate metastatic foci more frequently in animal experiments.^25,26 It is widely recognized that angiogenesis plays an indispensable role in tumor progression, with its fundamental mechanism rooted in the inflammatory response. Macrophages play a crucial role in the inflammatory response, and their pro-angiogenic capabilities are predominantly manifested through phenotypic changes following polarization. The role of M1-type macrophages in angiogenesis remains controversial, with their angiogenic effects varying significantly based on the cell type and timing of the inflammatory response. In contrast, M2 macrophages have been consistently shown to promote angiogenesis, fibroblast proliferation, and extracellular matrix deposition. Consequently, macrophage polarization plays a critical role in influencing tumor progression. The abnormal expression of sema3c has been increasingly observed in various types of tumors, including glioma, prostate cancer, and gastric cancer, as supported by a growing body of research. Moreover, it has been proposed that this deviant expression may potentially facilitate the facilitation of tumor growth.^27–29 However, the precise diagnostic significance of sema3c in lung adenocarcinoma and the underlying molecular mechanisms responsible for its abnormal expression remain unclear.

In this investigation, single-cell data indicated a significant upregulation of SEMA3C expression in LUAD, which was strongly associated with unfavorable prognosis. Subsequently, our clinical validation demonstrated the abundant secretion of SEMA3C as a protein by lung CAFs, highlighting the crucial role of M2-type macrophage-derived cytokines in promoting invasive metastasis and predicting poor outcomes for LUAD patients. Considering the correlation between SEMA3C and unfavorable prognosis in lung adenocarcinoma, our hypothesis is that SEMA3C plays a pro-lung cancer invasive and metastatic role by participating in the phenotypic shift of macrophages to the M2 type. Through experimental validation, we verified that SEMA3C acting with macrophage VNN1 affects macrophage polarization to the M2 type and influences the ability of lung cancer cells to migrate and invade.

The present study highlighted SEMA3C as a central factor in the microenvironment, predominantly regulated by macrophages and CAFs. Through cell co-culture and phenotypic validation, we demonstrate that CAF-mediated SEMA3C promotes M2 macrophage polarization, ultimately impacting LUAD migration and invasion. The above findings indicated a theoretical foundation for treating lung adenocarcinoma and developing novel therapeutic agents. However, this study still has several areas that warrant further exploration. For instance, incorporating animal experiments would facilitate more in-depth in vivo investigations into the role of SEMA3C in CAFs on lung cancer macrophages. Additionally, it remains unclear whether the SEMA3C-VNN1 interaction influences other stromal or immune cell types. Nonetheless, VNN1 inhibitors may offer novel insights for developing therapeutic drugs for lung cancer.

The primary function of VNN1 is attributed to its ubiquitin thiolesterase activity, which catalyzes the hydrolysis of ubiquitin into pantothenic acid and cysteamine. Currently, several Vanin-1 inhibitors are being proposed for development to address clinical indications including inflammatory bowel disease (IBD).³⁰ However, the biological significance and therapeutic potential of pharmacologically inhibiting Vanin-1 in the context of renal diseases remain to be elucidated.³¹ Despite the growing attention towards the indirect approach of utilizing VNN1 inhibitors to counteract oxidative stress via enzymatic reactions as a potential therapeutic strategy, studies in mouse renal ischemia-reperfusion injury and human proximal tubular cell hypoxia-reoxygenation models in vitro have shown that treatment with Vanin-1 inhibitors effectively inhibited enzymatic activity both in vitro and in vivo. However, this inhibition did not restore metabolic and redox homeostasis, thereby influencing disease progression. Consequently, the development of VNN1 inhibitors as anticancer agents is highly dependent on tissue and cell source specificity, with the mechanisms of cancer inhibition being heterogeneous. Significant challenges remain in their drug development process.

Conclusion

The present study employed bioinformatic analysis as well as experimental validation to identify the upregulation of SEMA3C in lung adenocarcinoma. Moreover, by integrating clinical samples and cellular experiments, we investigated the roles and molecular mechanisms of CAFs in mediating macrophage activation during immunometabolic disorders in lung adenocarcinoma through SEMA3C. These findings provide a solid experimental foundation for identifying novel drug targets aimed at treating lung adenocarcinoma.

Footnotes

ORCID iD

Yan Yan

Ethical approval

The study was approved by the Ethics Committee of the First Hospital of Qiqihar (NO. 2024-KY-25).

Authors’ Contributions

Renlong Liu designed the manuscript. Tiebo Yang provided the administrative support. Renlong Liu, Tao Sun and Chunyang Wang provided the materials. Yan Yan collected the data. Renlong Liu and Jie Lian analyzed the data.All authors wrote the manuscript. All authors reviewed the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.*

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