Exosomal LINC01614 from Gastric Cancer Cells Drives Treg Differentiation

Summary

In this study, LINC01614 was delivered via exosomes to investigate its mechanism in regulating Treg differentiation and M2 macrophage polarization, thereby remodeling the immune microenvironment in gastric cancer.

Abstract

This study aimed to investigate the potential mechanism by which LINC01614, a long non-coding RNA (lncRNA), regulates the immune microenvironment of gastric cancer through exosomes. Methodologically, exosomes were isolated by ultracentrifugation (100,000 × g, 70 min) using a transwell co-culture system (human gastric cancer cell lines BGC823 and NCI-N87 were co-cultured with peripheral blood CD4 T cells for 24 h) and identified by transmission electron microscopy (80 kV) and immunoblotting (CD63, CD81, TSG101). Flow cytometry (Foxp3, CD25 antibodies) showed that gastric cancer exosomes induced the differentiation of CD4 T cells into regulatory T cells (Tregs). LINC01614 was identified as a key molecule by integrating GEO (GSE95667), TCGA, and exoRBase2.0 databases (screening criteria: logFC > 1), and its high expression in gastric cancer cells and exosomes was confirmed by qRT-PCR (internal reference GAPDH, 2-ΔΔCq method). Functional experiments were performed using lentivirus-mediated overexpression/silencing of LINC01614 to treat BGC823 cells. ELISA revealed that LINC01614 overexpression of exosomes promoted IL-4 secretion by Tregs (p < 0.05), and drove THP-1-derived macrophages to M2 phenotype polarization and enhanced TGF-β release through the JAK-STAT3 pathway (immunoblotting antibodies p-JAK, p-STAT3). CCK-8 and colony formation assay showed that this process together promoted gastric cancer cell proliferation (p < 0.05). At the mechanistic level, the RIP assay (anti-IL-4 antibody pull-down) suggests that LINC01614 may directly interact with IL-4. In summary, this study preliminarily reveals the mechanism by which LINC01614 promotes Treg differentiation and M2 polarization through the exosome pathway to regulate the immune microenvironment of gastric cancer, providing a theoretical basis for targeting LINC01614 to enhance immunotherapy.

Introduction

Gastric cancer (GC) remains a significant global health challenge, with over 1 million new cases and approximately 769,000 deaths reported in 2020, ranking fifth in global incidence and fourth in mortality¹. Due to the asymptomatic nature of early-stage GC, the 5-year survival rate for most GC patients is between 20-40%. Approximately 40% of GC patients develop metastasis, and their 5-year survival rate drops to a mere 5%². Surgery and chemotherapy continue to be the primary treatments for GC. Studies have shown that systemic chemotherapy can improve survival rates in GC patients with peritoneal metastasis, but the median survival remains only 4 months³. Immunotherapy has shown promising results in many cancers⁴, but in GC, the objective response rate is less than 15%^5,6. Previous research suggests that the heterogeneity of tumor cells and the tumor microenvironment may diminish the efficacy of immunotherapy⁷.Thus, understanding the mechanisms through which GC cells influence the tumor microenvironment is clinically important.

Tumor cells can exchange non-coding RNAs, proteins, and other molecules via exosomes, facilitating tumorigenesis and progression⁸. While exosomes play roles in organ-specific metastasis and angiogenesis⁹, most studies have focused on their generic functions. In contrast, our understanding of how specific exosomal lncRNAs regulate immune polarization remains limited, highlighting a critical knowledge gap. For instance, in GC, epithelial-mesenchymal transition (EMT) is a key prognostic factor, with SNAI2 repressing ELF3 and ELF3-AS1 to form a feedback loop driving progression¹⁰. Similarly, lncRNA NR2F1-AS1 promotes metastasis via miR-29a sponging¹¹. Beyond intracellular roles, non-coding RNAs in exosomes regulate tumor immunity: M2 macrophage-derived lncRNA CRNDE and miR-487a induce cisplatin resistance in GC^12,13, while breast cancer exosomal SNHG16 promotes Treg differentiation via miR-16-5p/SMAD5¹⁴. However, these findings often emphasize isolated pathways without integrating multi-cell interactions. Notably, the lncRNA/miR-29c axis promotes M2 polarization and immune escape in GC¹⁵, but mechanisms linking exosomal lncRNAs to Treg-driven immunosuppression are underexplored. LINC01614 is a lncRNA associated with progression in osteosarcoma ¹⁶ and papillary thyroid carcinoma¹⁷. In GC, high LINC01614 expression correlates with poor prognosis and serves as an independent prognostic biomarker, promoting proliferation, migration, and metastasis^18,19,20. However, the potential involvement of LINC01614 in exosome-mediated immune regulation has not yet been fully elucidated, and our study aims to contribute to this emerging area of research.

Treg cells, a CD4+ subpopulation defined by Foxp3, are crucial for suppressing antitumor immunity and promoting immune escape^21,22. In GC, tumor cells recruit Tregs to inhibit immune responses, with high FOXP3+ Treg infiltration predicting poor prognosis^23,24. Macrophages also influence Treg activation via protein secretion. An elevated M2/Treg ratio in the tumor microenvironment often correlates with immunosuppression and poor outcomes, but the mechanistic drivers of this crosstalk are poorly defined. Most studies describe generic immune cell interactions without elucidating how exosomal lncRNAs directly orchestrate these processes.

In this study, we found that GC cells promote the transformation of CD4+ T cells to Treg cells via exosomal LINC01614. Additionally, the transformed Treg cells promote macrophage polarization toward the M2 type by secreting IL-4, thereby enhancing GC cell proliferation. These findings elucidate the specific mechanism by which LINC01614 facilitates immune escape in GC cells through a coordinated exosome-mediated Treg-macrophage axis, providing a novel integrated perspective beyond existing research. Based on cell line models, our work demonstrates potential applicability for understanding GC immunosuppression; however, limitations include the lack of patient-derived validation, which warrants further investigation to confirm clinical relevance.

Protocol

This study utilized established human cell lines for in vitro experiments only, with no animal subjects involved. Therefore, Institutional Animal Care and Use Committee (IACUC) approval is not required. All cell culture procedures complied with the biosafety regulations of Zunyi Medical University and were conducted in BSL-2 laboratories.

1. Cell culture

1. Culture the gastric cancer cell lines (MGC803, BGC823, SGC7901, AGS, NCI-N87) and THP-1 cells in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin (P/S). Culture CD4⁺ T cells in RPMI-1640 medium with 10% FBS, 1% P/S, 50 mM β-mercaptoethanol, and 100 U/mL interleukin-2 (IL-2).
2. To induce differentiation into macrophages (M0), stimulate THP-1 cells with 50 ng/mL phorbol 12-myristate 13-acetate (PMA) for 48 h.

2. Cell co-culture system

1. Set up a Transwell co-culture system using 6-well plates with 0.4 µM pore size inserts. Seed gastric cancer cells (BGC823 or NCI-N87) into the upper chamber at a density of 1 × 10⁵ cells per well. Seed peripheral blood-derived CD4⁺ T cells into the lower chamber at a density of 5 × 10⁵ cells per well, resulting in a GC cell to CD4⁺ T cell ratio of 1:5.
2. Establish the following experimental groups in the lower chamber: (1) CD4⁺ T cells alone; (2) GC cells co-cultured with CD4⁺ T cells; (3) GC cells co-cultured with CD4⁺ T cells in the presence of GW4869; and (4) CD4⁺ T cells treated with GC cell-derived exosomes. For GW4869 treatment, add GW4869 (20 µM) to the upper chamber containing GC cells. For exosome treatment, add 50 µg of GC cell-derived exosomes directly to the lower chamber containing CD4⁺ T cells.
3. Maintain the co-culture system at 37 °C in a humidified incubator with 5% CO₂ for 24 h. Collect CD4⁺ T cells and culture supernatants from the lower chamber for subsequent analyses.

3. Bioinformatics analysis

1. Perform a comprehensive bioinformatics analysis to identify differentially expressed long non-coding RNAs (lncRNAs) in gastric cancer (GC) using publicly available datasets. Obtain gene expression data from the Gene Expression Omnibus (GEO; GSE95667), The Cancer Genome Atlas (TCGA, accessed via the GDC Data Portal), and ExoRBase2.0 (http://www.exorbase.org/exoRBaseV2/download/toIndex). 
2. Conduct differential expression analysis for the GEO dataset using the GEO2R online analysis tool. Download TCGA transcriptome profiling data and corresponding clinical information from the GDC Data Portal. Retrieve exosomal lncRNA expression data from ExoRBase2.0 using the lncRNA browsing and download functions.
3. Exclude lncRNA entries from ExoRBase2.0 that show tissue-specific expression only, incomplete annotation, or missing expression values. Retain only lncRNAs with a log fold change (logFC) greater than 1 from the GEO and TCGA datasets. Process the filtered expression matrices in R to generate a candidate lncRNA list for downstream analyses.
4. Evaluate the expression levels of candidate lncRNAs identified from the TCGA dataset and examine their association with patient survival outcomes. Dichotomize patients into high- and low-expression groups based on median expression levels and assess overall survival using Kaplan-Meier analysis with log-rank testing.
5. Predict the potential interaction between LINC01614 and IL-4 using the RPISeq web server (http://pridb.gdcb.iastate.edu/RPISeq/) by inputting the corresponding RNA and protein sequences and applying the default prediction models.

4. Exosome isolation and identification

1. Use GES-1 cells as non-malignant controls to evaluate exosome content in gastric cancer cell lines, including MGC803, BGC823, SGC7901, and AGS.
2. Culture GES-1, MGC803, BGC823, SGC7901, and AGS cells for 48-72 h. Collect conditioned media and isolate exosomes by differential centrifugation followed by ultracentrifugation.
   1. Centrifuge the conditioned media at 300 × g for 10 min to remove cells, followed by centrifugation at 2,000 × g for 10 min and 10,000 × g for 30 min to remove cell debris and large vesicles.
   2. Wash the clarified supernatant with PBS and ultracentrifuge at 100,000 × g for 70 min to pellet exosomes. Resuspend the exosomal pellet in PBS for downstream analyses. Fix a portion of the resuspended exosomes in 2% glutaraldehyde prepared in 0.1 M phosphate buffer (pH 7.4) for transmission electron microscopy (TEM).
      1. Use the glutaraldehyde-fixed exosome suspensions for TEM analysis. Place fixed exosome samples onto 100-mesh carbon- and formvar-coated nickel grids pretreated with poly-L-lysine for approximately 30 min. Wash the grids three times with drops of PBS.
      2. Normalize protein input by determining total protein concentrations of exosome preparations and corresponding cell lysates using a BCA protein assay. Adjust sample volumes to ensure equal protein loading for subsequent analyses.
      3. Perform immunoblotting to detect exosome marker proteins CD63, CD81, and TSG101. Mix exosome proteins with reducing SDS sample buffer and heat to denature proteins.
      4. Separate proteins by SDS-PAGE and transfer them onto PVDF membranes. Block membranes with 5% non-fat milk in TBST at room temperature for 2 h. Incubate membranes with primary antibodies against CD63, CD81, and TSG101 at 4 °C overnight.
      5. Wash membranes three times with TBST and incubate with appropriate HRP-conjugated secondary antibodies at room temperature for 2 h. Wash membranes three times with TBST and visualize protein bands using chemiluminescent detection.
      6. Extract lncRNAs from exosome preparations using a column-based RNA isolation method. Perform reverse transcription to synthesize cDNA, followed by quantitative reverse transcription polymerase chain reaction (qRT-PCR).
      7. Conduct PCR amplification using the following thermocycling conditions: initial denaturation at 95 °C for 30 s; 40 cycles of denaturation at 95 °C for 5 s and annealing/extension at 60 °C for 30 s.
      8. Perform melting curve analysis after amplification to confirm product specificity. Normalize lncRNA expression levels to GAPDH and calculate relative expression using the 2^−ΔΔCq method. Run all reactions in technical triplicate.
3. Using the exosome samples previously fixed in 2% glutaraldehyde in 0.1 M phosphate buffer (pH 7.4), incubate the grids on drops of buffered 1% glutaraldehyde for 5 min. Wash the grids three times with drops of distilled water.
4. Negatively stain the grids with filtered aqueous 4% uranyl acetate for 5 min. Remove excess stain using filter paper and allow the grids to air dry at room temperature.
5. Examine the grids using a transmission electron microscope operated at an accelerating voltage of 80 kV. Acquire digital images for morphological analysis of exosomes.

5. Visualization of exosomes

1. Label exosome preparations with a green lipophilic membrane dye (PKH67) at a final concentration of 2 × 10⁻⁶ M.
   1. Prepare a 2× dye working solution (4 × 10⁻⁶ M) in the provided diluent and mix equal volumes of the 2× dye solution and a 2× exosome suspension to achieve the final dye concentration; incubate the mixture for 1-5 min at room temperature with gentle mixing to allow dye partitioning into exosomal membranes.
   2. Quench unincorporated dye by adding an equal volume of 1% bovine serum albumin (or serum) and then remove free dye by washing the labeled exosomes via ultracentrifugation (100,000 × g, 70 min); resuspend the labeled exosome pellet in PBS. Incubate labeled BGC823- or NCI-N87-derived exosomes with CD4⁺ T cells for 2 h before downstream analysis.
2. Fix cells after exosome incubation with 4% paraformaldehyde for 30 min at room temperature. Wash fixed cells three times with PBS, stain nuclei with DAPI according to standard practice, and observe exosome uptake by fluorescence microscopy.

6. Flow cytometry

1. Harvest cells when confluence reaches 80-90%. Collect 1 × 10⁶ cells per sample into centrifuge tubes and centrifuge to pellet cells. Wash cells twice with staining buffer consisting of 1× PBS, 0.5% bovine serum albumin, and 2 mM EDTA.
2. Resuspend cells in staining buffer and incubate with primary antibodies in the dark at 4 °C for 30 min.
3. Stain intracellular Foxp3 using a fluorophore-conjugated anti-Foxp3 antibody (PE-labeled, 1 µg per test) without secondary antibody incubation. Stain surface CD25 using a primary anti-CD25 antibody (1:100), followed by incubation with a FITC-conjugated secondary antibody for 30 min at 4 °C in the dark.
4. Wash cells three times with cold PBS after each staining step and resuspend cells in PBS for analysis.
5. Acquire stained cells by flow cytometry, collecting at least 10,000 events per sample.
   1. Perform data analysis by first gating on lymphocytes based on forward scatter (FSC) and side scatter (SSC) to exclude debris. Exclude cell doublets using FSC-area versus FSC-height gating.
   2. Identify CD25-positive cells in the FITC channel and Foxp3-positive cells in the PE channel. Define Tregs as CD25⁺Foxp3⁺ cells within the lymphocyte population.
   3. Analyze and quantify populations using standard flow cytometry analysis software.

7. Enzyme-linked immunosorbent assay (ELISA)

1. Collect the supernatants from both the BGC823/NCI-N87 and CD4+ T cell co-culture systems and the exosome and CD4+ T cell co-culture system. Then, determine the levels of IL-4 and TGF-β using their respective ELISA kits.

8. Western blotting

1. Extract total cellular or exosomal proteins using radioimmunoprecipitation assay (RIPA) lysis buffer. Determine protein concentrations by a bicinchoninic acid (BCA) assay and adjust sample volumes so that approximately 10 µg total protein is loaded per lane.
2. Prepare samples in reducing SDS sample buffer and denature by heating at 95 °C for 5 min. Separate proteins by SDS-PAGE using standard Tris-glycine-SDS running buffer (25 mM Tris, 192 mM glycine, 0.1% SDS). Run gels at 80 V for 30 min (stacking gel), followed by 120 V for 70 min (separating gel).
3. Transfer proteins from the gel to polyvinylidene fluoride (PVDF) membranes by wet transfer in an ice bath at a constant current of 300 mA for 60 min.
4. Block membranes with 5% skimmed milk in TBST at room temperature for 2 h.
5. Incubate membranes with primary antibodies at 4 °C overnight using the following dilutions: CD63 1:1000, CD81 1:500, TSG101 1:1000, Tubulin 1:5000, p-JAK 1:1000, JAK 1:1000, p-STAT3 1:1000, STAT3 1:1000, and IL-4 1:1000.
6. Wash membranes three times with Tris-buffered saline with Tween 20 (TBST). Incubate membranes with appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies at the manufacturer-recommended dilution for 2 h at room temperature. Wash membranes three times with TBST prior to detection.
7. Visualize protein bands by chemiluminescent detection and capture images using an appropriate digital imaging system.

9. qRT-PCR

1. Extract total RNA from cultured cells using TRIzol reagent.
2. Reverse-transcribe lncRNAs using random primers and oligo(dT) primers according to the reverse transcription kit protocol. Perform reverse transcription at 37 °C for 15 min, followed by 85 °C for 5 s.
3. Perform quantitative PCR using TB Green-based chemistry on a real-time PCR system. Set the cycling conditions as follows: 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s and 60 °C for 30 s. Perform melting curve analysis at 95 °C for 10 s and 65 °C for 5 s. Use GAPDH as the internal reference gene. Calculate relative expression levels using the 2^−ΔΔCq method. Perform each experiment in triplicate.
4. Perform quantitative PCR using TB Green-based chemistry on a real-time PCR system. Set the cycling conditions as follows: 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s and 60 °C for 30 s. Perform melting curve analysis at 95 °C for 10 s and 65 °C for 5 s. Use GAPDH as the internal reference gene. Calculate relative expression levels using the 2^−ΔΔCq method²⁵. Perform each experiment in triplicate.
   NOTE: The PCR primers used were as follows: LINC01614-forward 5'-TGACATAATCTGGGTTCTT-3', LINC01614-reverse 5'-CTGGAGGAGTAAGGTTTG-3'1); GAPDH-forward 5'-GGAGCGAGATCCCTCCAAAAT-3', GAPDH-reverse 5'-GGCTGTTGTCATACTTCTCATGG-3'.

10. Plasmid construction and siRNA silencing

1. To overexpress LINC01614, clone the sequence construct into a lentiviral vector. Package recombinant lentiviruses by co-transfecting 293T cells with the vector and packaging plasmids (psPAX2 and pMD2.G) using a lipid-based transfection reagent.
2. Transduce target gastric cancer cells with the harvested lentivirus supernatant. At 48 h post-transduction, begin selection with 1 mg/mL G418. Maintain selection for 14 d to establish stable polyclonal populations.
3. To silence LINC01614, design a short hairpin RNA (shRNA) targeting LINC01614 (target sequence: 5'-GAGGGTTTCTCCTATTAAATT-3'). Clone this shRNA sequence into a commercial shRNA expression vector.

11. RNA immunoprecipitation (RIP) assay

1. Transfect CD4+ T cells with LINC01614 overexpression or knockdown vectors for 48 h prior to the assay. Perform the RIP assay using a magnetic bead-based RIP kit.
2. For each condition, lyse 1 10⁷ cells in 100 µL of complete RIP lysis buffer containing protease and RNase inhibitors. Incubate 100 µg of cleared lysate with 5 µg of anti-IL-4 antibody or normal IgG-coated magnetic beads overnight at 4 °C with rotation.
3. Wash the bead complexes stringently with RIP wash buffer six times. Isolate the co-precipitated RNA according to the manufacturer's instructions. Analyze the enrichment of LINC01614 by qRT-PCR. Normalize results to the input control.

12. CCK-8 assay

1. Seed cells in a 96-well plate at a density of 3 × 10³ cells per well. For co-culture groups, seed gastric cancer cells and immune cells at a 1:5 ratio. Apply the corresponding treatments, including exosomes at a concentration of 20 µg/mL and a neutralizing anti-IL-4 antibody at 10 µg/mL.
2. Incubate the plates at 37 °C. At 24, 48, and 72 h, add 10 µL of CCK-8 reagent directly to each well. Continue incubation for 2 to 4 h.
3. Measure the absorbance at 450 nm using a microplate reader. Calculate cell viability relative to the control group at the zero-hour time point.

13. Colony formation assay

1. Seed BGC823, BGC823 + THP-1, BGC823 + M2-type THP-1, THP-1, BGC823 Exo + CD4⁺ T cells, and BGC823 Exo + CD4⁺ T cells + anti-IL4 into well plates at a density of 500 cells per well. Culture the plates for 14 days.
2. Fix cells in each well with 4% paraformaldehyde for 30 min at room temperature. Stain wells with 0.2% crystal violet at room temperature for 30 min, then rinse and allow plates to air dry.
3. Count colonies containing more than 50 cells and record the colony numbers for each condition.

14. EdU assay

1. Seed transfected cells into 6-well plates and add 10 µM EdU to each well.
2. Incubate cells at 37 °C for 2 h. Fix cells with 4% paraformaldehyde, wash three times with PBS containing 3% BSA, and permeabilize with PBS containing 0.3% Triton X-100. Wash cells an additional three times with PBS containing 3% BSA.
3. Add 500 µL click additive solution and incubate for 30 min at room temperature in the dark.
4. Stain cells with DAPI at room temperature in the dark for 10 min. Acquire images using a fluorescence microscope.

15. Statistical analysis

1. Perform all experiments with three biological replicates (n = 3) and present data as mean ± SEM.
2. Compare differences between two independent groups using an unpaired Student's t-test, and analyze variations among three or more groups using one-way ANOVA. Consider p < 0.05 statistically significant.
   NOTE: All statistical analyses were performed using GraphPad Prism 8.0.

Results

We followed the above experimental approach and found that exosomes secreted by GC cells promote the transformation of CD4+T cells into Tregs (Figure 1): GC cell (BGC823, NCI-N87) exosomes induced CD4+ T-to-Treg conversion (Foxp3, CD25 upregulated; Figure 1A,C), were taken up by CD4+ T cells (Figure 1B,D), promoted supernatant IL-4 (inhibition reversed; Figure 1E,F), and BGC823-Exo had higher CD63/CD81/TSG101 (Figure 1G,H), regulating tumor immunosuppression. Combined GEO/TCGA/exoRBase2.0 analysis screened 11 co-upregulated LncRNAs (Figure 2A-D); TCGA identified LINC01614 as prognosis-related (Figure 2E,F), which was highly expressed in GC cells/exosomes (Figure 2G,H), and exosome morphology was observed (Figure 2I). LINC01614 overexpression in GC cells enhanced exosome-induced Treg conversion (Figure 3A,C) and IL-4 (Figure 3B,D), while interference reversed this. LINC01614 overexpression/exosome groups promoted macrophage M2 polarization and TGF-β (Figure 4A-H, Figure 5A-H). This is consistent with the conclusion that IL-4 blockade inhibited M2 polarization in related studies²⁶. Further mechanistic studies showed that IL-4 inhibition reduced M2 polarization (Figure 6A-D), and Tregs activated JAK-STAT3 via IL-4 (p-JAK/p-STAT3 up; Figure 6E-G). LINC01614 bound IL-4 (Figure 7A), regulated IL-4 expression (Figure 7B,C), and promoted GC proliferation via Treg/M2 (Figure 7D-I). Overall, this study reveals that gastric cancer cells drive CD4 T cells to differentiate into Treg cells through exosome LINC01614, which in turn mediates macrophage M2 polarization by IL-4 and jointly promotes tumor proliferation; this mechanism provides new ideas for gastric cancer immunotherapy, but specific molecular interactions and in vivo effects need to be further verified by animal experiments.

Figure 1: Exosomes secreted by GC cells promote the transformation of CD4+T cells into Tregs. (A,C) Cells were collected from the lower chamber, and the ratio of Tregs was determined using flow cytometry. (B,D) Exosomes were labeled using fluorescence staining, and their uptake in the co-culture system was observed using fluorescence microscopy (scale bar = 20 µm). (E,F) The expression levels of IL-4 were quantified using the ELISA method. (G, H) The expression levels of exosomes were analyzed using Western Blot. ^###p < 0.001. Data are presented as mean ± SEM, n = 3. Please click here to view a larger version of this figure.

Figure 2: Differential expression of LncRNA in GC. (A,B) Differentially expressed LncRNAs in GCs from the GEO database. (C) LncRNAs with a logFC greater than 1 were selected from the TCGA database for further screening. (D) Data with specific expression and unannotated LncRNA genes were excluded from ExoRBase2.0. (E,F) TCGA database analysis of LINC01614 expression in GC and its correlation with survival. (G,H) RT-qPCR analysis of LINC01614 expression levels in different GC cell lines and their exosomes. (I) Transmission electron microscopy analysis of exosomes (scale bar = 500 nm). ^###p < 0.001. Data are presented as mean ± SEM, n = 3. Please click here to view a larger version of this figure.

Figure 3: GC cells promote the conversion of CD4+ T cells to Tregs through exosomes carrying LINC01614. (A,C) Detection of Treg transformation levels by flow cytometry. (B,D) Expression levels of IL-4 in the supernatant were measured using the ELISA method. ^###p < 0.001. Data are presented as mean ± SEM, n = 3. Please click here to view a larger version of this figure.

Figure 4: BGC823 cells carrying LINC01614 promote Treg transformation and M2 macrophage polarization. (A-G) RT-qPCR detection of M1 and M2 macrophage markers. (H) ELISA detection of M2 macrophage marker TGF-β levels. ^###p < 0.001. Data are presented as mean ± SEM, n=3. Please click here to view a larger version of this figure.

Figure 5: NCI-N87 cells carrying LINC01614 promote Treg transformation and M2 macrophage polarization. (A-G) RT-qPCR detection of M1 and M2 macrophage markers. (H) ELISA detection of M2 macrophage marker TGF-β levels. ^#p < 0.05, ^##p < 0.01, ^###p < 0.001. Data are presented as mean ± SEM, n = 3. Please click here to view a larger version of this figure.

Figure 6: Treg cells secrete IL-4 and promote M2 macrophage polarization through JAK-STAT3 activation. (A-D) RT-qPCR detection of M2 macrophage markers. (E-G) Western blot detection of JAK-STAT3 signaling pathway activation. ^###p < 0.001. Data are presented as mean ± SEM, n = 3. Please click here to view a larger version of this figure.

Figure 7: LINC01614 promotes IL-4 secretion and M2 polarization through Treg transformation, enhancing GC cell proliferation. (A) RIP analysis of the interaction between IL-4 and LINC01614. (B,C) Western blot detection of IL-4 expression levels. (D,E) CCK8 detects cell proliferation viability. (F,G) Cell proliferation assessed by EdU assay. (H,I) Cell proliferation assessed by the colony formation assay. ^##p < 0.01, ^###p < 0.001. Data are displayed as mean ± SEM, n = 3. Please click here to view a larger version of this figure.

Discussion

Immunotherapy is a primary therapeutic approach for tumor metastasis, aiming to prevent tumor recurrence by restoring anti-tumor immunity within the tumor microenvironment. Regulatory T cells (Tregs) and M2 macrophage polarization can induce immunosuppressive effects, which are significant factors in tumor progression and recurrence^27,28.

In recent years, with the in-depth study of exosomes, increasing attention has been paid to their role in mediating communication within the tumor immune microenvironment. As research focuses more on the pathophysiological regulatory mechanisms of tumorigenesis, the significance of small-molecule-regulated signaling pathways in cellular or intercellular communication, such as binding DNA, RNA, or proteins, has garnered greater interest. Among these, the potential role of non-coding RNAs in bridging tumor immunity has become a hotspot of tumor immunity research^29,30. Exosomes, as crucial carriers of non-coding RNAs, are extensively involved in mediating tumor immunomodulation. Exosomes from tumor cells, such as the exosome circRELL1 from gastric cancer (GC) cells, can act as miR-637 sponges to regulate GC progression by modulating autophagy activation³¹. Additionally, the GC cell exosome lncRNA HCG18 promotes M2 macrophage polarization by decreasing miR-875-3p levels in macrophages, thereby increasing KLF4 expression³². Tang et al. found that GC-derived lncRNA RP11-357H14.17 plays an oncogenic role in GC by activating Treg cells³³. Furthermore, GC cells carrying the long non-coding RNA SND1 intron transcript SND1-IT1 induce malignant transformation of gastric mucosal cells by promoting the expression of ubiquitin-specific protease 3 (USP3) through competitive adsorption to miR-1245b-5p³⁴. GC cell exosome miR-92a-3p-mediated PD-L1 expression in lung macrophages promotes GC lung metastasis³⁵. These findings suggest that exosomes regulate communication between tumors and various immune cells by carrying non-coding RNAs, which is an important pathway leading to immune imbalance in the tumor microenvironment of GC. In our study, we found for the first time that GC cells can promote the transformation of CD4+ T cells into Treg cells via exosomes carrying the long-stranded non-coding RNA LINC01614.

Most of the Treg cells expressed in gastric cancer (GC) tissues may be transformed by TGF-β1 induction³⁶. In the presence of interleukin (IL)-6 or IL-21 along with TGF-β, CD4+ T cells differentiate into Th17 cells; in the absence of pro-inflammatory cytokines, TGF-β drives differentiation into Treg cells³⁷. Within the tumor immune microenvironment, tumor-infiltrating Th17 cells can also be differentiated into Treg cells via TCR, and Th17-derived Treg cells do not revert back to Th17 cells even under conditions that favor Th17 differentiation, resulting in a strong immunosuppressive effect³⁸. In acute myeloid leukemia, the proportion of Treg cells in the peripheral circulation of patients is significantly increased, with strong suppression mediated through IL-35, IL-10, TGF-β, and contact-dependent mechanisms³⁹. Conversely, in chronic lymphocytic leukemia, Treg cells are activated through the Galectin-9/Tim3 signaling pathway, which inhibits Th1 effector function and promotes tumor development⁴⁰. Qiu et al. found that CCL5 promotes the proliferation of Treg cells by binding to CCR5, thereby blocking the tumor-killing function of CD8+ T cells and leading to axillary lymph node metastasis of breast cancer⁴¹. Cervical cancer-derived exosomes activate STING signaling in tumor-infiltrating T cells by releasing TGF-β, cyclic GMP-AMP synthase, and 2'-3'-cGAMP, leading to Treg cell amplification and suppression of anti-tumor immunity in CD8+ T cells⁴². Immunotherapy targeting Treg cells has also shown theoretical promise. For instance, using CD25 monoclonal antibody to clear Treg cells resulted in an increased proportion of CD8+ T cells in mouse tumor tissues and enhanced specific tumor-killing activity⁴³. The combination of nivolumab and ipilimumab (anti-CTLA-4 monoclonal antibody) targeting Treg cells can reduce tumor overgrowth in patients with malignant melanoma^44,45. Several studies have found that FOXP3+ T cell infiltration in colorectal cancer indicates a better prognosis⁴⁶. These phenomena do not conflict with the immunosuppressive role of Treg cells. In the early stages of a tumor, Treg cells control inflammation and inhibit early tumor formation, correlating with a good prognosis. However, in advanced tumors, Treg cells inhibit the tumor-killing effects of effector T cells, exerting a negative effect. Given that Treg cells are classified into many subtypes within the tumor microenvironment, and broad-spectrum Treg markers do not fully identify their specific acting cell types, it is necessary to clarify the specific markers of relevant subtypes to obtain prognostically relevant Treg subpopulations. Meanwhile, our study did not elucidate the specific mechanism by which LINC01614 promotes Treg transformation, which still requires further research.

Recent studies have confirmed the intercellular transfer of molecules between Treg cells and their target cells through endocytosis and the release of exosomes. Treg cells can produce exosomes that inhibit T cell proliferation in a dose-dependent manner. These vesicles also alter the cytokine profile of effector T cells (Teffs), leading to increased production of IL-4 and IL-10, while decreasing levels of IL-6, IL-2, and IFNγ⁴⁷. Similarly, we found that Treg cells converted by gastric cancer (GC) cells via exosome LINC01614 can secrete large amounts of IL-4, which in turn promotes the polarization of M2 macrophages. Recent studies indicate that IL-4 can also directly modulate the phenotype and suppressive function of regulatory T cells. High IL-4 signaling has been reported to reduce CTLA-4 expression and impair canonical suppressive activity of Tregs, and IL-4/STAT6-dependent signaling can favor a shift toward a Th2-like Treg program in inflammatory settings^48,49. These findings suggest that exosome-induced IL-4 secretion may not only promote M2 polarization but also affect Treg stability and functional state within the tumor microenvironment, thereby reshaping the overall immunosuppressive landscape.

From a methodological perspective, accurate interpretation of exosome-mediated immunomodulatory effects depends on rigorous control of critical protocol steps. Key pre-analytical variables include choice and handling of starting material, avoidance of platelet activation in blood-derived samples, and consistent storage conditions, during isolation, parameters such as rotor type, relative centrifugal force (RCF), duration, and wash steps must be standardized, and exosome identity should be confirmed by orthogonal methods (electron microscopy, nanoparticle tracking analysis, and marker detection) to ensure reproducibility^50,51.

Practical modifications and troubleshooting can mitigate common technical issues. When ultracentrifugation leads to co-sedimentation or aggregation, incorporating density gradients (e.g., iodixanol) or combining with size-exclusion chromatography (SEC) improves purity, reducing rotor braking; adding wash steps can decrease protein contamination; while careful dye-labeling protocols and removal of unincorporated dye prevent false-positive uptake signals. These adjustments have been recommended in recent methodological studies to preserve vesicle integrity and functional readouts⁵¹.

Limitations intrinsic to exosome techniques should be acknowledged. Exosome heterogeneity, potential co-isolation of lipoproteins or protein aggregates, and effects of isolation forces on cargo integrity can confound functional attribution. Additionally, reliance on in vitro cell line models limits direct clinical translation and necessitates validation using patient-derived samples and in vivo models. Careful experimental controls (mock isolations, secretion inhibitors, and orthogonal cargo profiling) are therefore essential when inferring exosome-specific biological effects⁵⁰.

Significance and future applications. Despite these constraints, standardized and reproducible exosome isolation and characterization frameworks enable robust downstream molecular analyses and translational prospects, including biomarker discovery and engineered exosome therapeutics. Integration of microfluidic and high-throughput isolation platforms may further enhance clinical scalability. Our protocol emphasizes reproducibility and accessibility, making it a useful starting point for mechanistic studies and future clinical validation in gastric cancer.

This positive feedback pathway leads to the continuous deterioration of GC and may be one of the reasons for its negative correlation with the prognosis of GC, providing a new target for the diagnosis and treatment of GC. In the inflammatory response, Treg-derived IL-13 induces macrophages to release IL-10, which affects the guanine nucleotide exchange factor Vav1 and GTPase Rac1 via the autocrine-paracrine pathway, promoting the phagocytosis of apoptotic cells by macrophages⁵². Similarly, Treg-derived exosomes inhibit the expression of M1 macrophage markers and promote the expression of M2 macrophage markers in cardiomyocytes during myocardial infarction⁵³. Treg cells also affect the function of CD8+ T cells by inhibiting their IFN-γ production. This inhibition blocks macrophage conversion to the M1 phenotype, mitigates the inhibitory effect of IFN-γ on sterol regulatory element-binding protein 1 (SREBP1), and restores fatty acid synthesis⁵⁴. Melanoma cell-derived exosomes can inhibit CD4+ T cell immune function and promote macrophage M2 polarization by carrying mucin structural domain 3 (TIM-3)⁵⁵. Currently, there are few studies on the mechanism of Treg action on tumor-associated macrophages, with most research focusing on inflammatory responses and autoimmune diseases. However, the synergistic effect of Treg cells and macrophages in the tumor microenvironment suggests the existence of relevant signaling pathways that promote immune escape from tumors. Further research is needed to elucidate these pathways and their potential as therapeutic targets.

Meanwhile, the role of macrophages targeting Treg cells cannot be ignored. For example, in epithelial ovarian cancer, IL-10 secreted by tumor-associated macrophages (TAMs) increases the ratio of Treg cells and promotes tumor progression by activating Foxp3 during T cell differentiation⁵⁶. In gastric cancer (GC), tumor-associated macrophages better present H. pylori antigens and promote Tim-3 expression on Treg cells, leading to a poor prognosis in GC⁵⁷. Yang et al. found that in hepatocellular carcinoma, co-culture of macrophages with hepatocellular carcinoma cells significantly upregulated IL-10 and CCL22, enhancing Treg recruitment and leading to immune escape⁵⁸. This M2 macrophage-Treg loop contributes to immunosuppression, resulting in a poor tumor prognosis⁵⁹. In recent years, the role of TAM/M2 polarization in GC has received much attention. For instance, epithelial-mesenchymal transition (EMT)-mediated secretion of tumor-associated fibroblasts in the GC microenvironment by insulin-like growth factor-binding protein (IGFBP7) promotes the infiltration of M2/TAM macrophages through the FGF2/FGFR1/PI3K/AKT axis, contributing to the poor prognosis of GC⁶⁰. Similarly, the serine protease PRSS23 can promote infiltration of TAMs by regulating FGF2 expression⁶¹. The remodeling of tumor immunity through targeted therapy against TAMs and corresponding cytokines has garnered attention. For example, targeting Marco or IL37 receptor (IL37R) repolarizes TAMs, restores the killing activity and anti-tumor capacity of NK and T cells, and downregulates Treg activity⁶².

In this study, we found that non-coding RNAs delivered by exosomes from GC can promote Treg cell transformation and M2 macrophage polarization, and the specific mechanism remains to be deeply explored, which is consistent with the conclusion that inhibition of exosome-specific molecules can inhibit tumor growth and metastasis in preclinical models^63,64. Compared with existing research methods, which often lack standardization, the core contribution of this study is to establish a reproducible and easy-to-use exosomal RNA interaction analysis framework, which provides standardized technical support for similar research. However, there are significant limitations in the study: only cell line models are dependent, patient-derived exosomes (e.g., peripheral blood, tumor tissue sources) are not included for validation, and clinical applicability is limited; and there are potential technical variations in exosome isolation and RNA analysis links, which may affect the stability of the results. Future studies need to preferentially validate the mechanism in patient-derived exosomes and GC animal models (e.g., xenograft models), while relying on this framework to develop exosome diagnostic tools for early gastric cancer screening or efficacy monitoring, and the reproducibility and ease of use of this framework will further assist in the clinical translational application of exosome lncRNAs.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
4% paraformaldehyde	Biosharp, Inc	BL105A
A green fluorescent membrane dye	Sigma, USA	PKH67GL
AMT Imaging System	Advanced Microscopy Techniques Corp., Danvers, MA	HT7700 80kv
Anti-CD25	Invitrogen, USA	25-0251-82
Anti-Foxp3	Invitrogen, USA	12-5773-82
Anti-IgG antibody	Service Biotech Ltd	GB111738
Anti-IL-4 antibody	Protein Technology Group Ltd	221041AP
BCA Protein Assay Kit	Beyotime, China	P0012S
BeyoExo Exosome Labeling and Tracking Kit (PKH67)	Beyotime, China	C3635S
Bio-Rad 7500 PCR System	Bio-Rad Laboratories, Inc.	CFX96
CCK8 Cell Proliferation and Cytotoxicity Assay Kit	HYCEZMBIO	HYCCK8-500T
CD63 Antibody	Abcam, UK	ab134045
CD81 Antibody	Abcam, UK	ab79559
Clean bench	SuZhou AnTai Air Tech CO.,LTD.,China	SW-CJ-1FD
Click additive solution	Epizyme, Inc	CX002L
ECL Chemiluminescence Imaging System	Hangzhou Shenhua Technology Co., Ltd.,China	SH-523
Fetal bovine serum (FBS)	GIBCO, USA	10099-141
Flow cytometer	Beckmancoulter	cytoFLEX
FlowJo software	Treestar, USA	FlowJo vX
Fluorescence microscopy	Nikon	Nikon DS-Fi3
HRP-conjugated secondary antibodies	Wuhan Lingjiesi Biotechnology Co., LTD	LJS-S-0001
IL-4 Antibody	Abcam, UK	ab62351
IL-4 ELISA kits	WuHan LingSi Biotechnology Co., Ltd., China	LSSW-I-1087
JAK Antibody	WuHan LingSi Biotechnology Co., Ltd., China	LSSW-K-2354
JEM 1010 transmission electron microscope	Japan Electron Optics Laboratory Co., Ltd.,Japan	JEM-1010
LINC01614 overexpressing lentivirus and interfering lentivirus	GenePharma Co., Ltd.,China
LncRNeasy Mini reagent kit	TIANGEN	FP402
MGC803, BGC823, SGC7901, AGS, NCI-N87 and THP-1 cell lines	the Typical Cultures Preservation Committee Cell Bank of the Chinese Academy of Sciences,China
Penicillin/streptomycin (P/S)	WuHan LingSi Biotechnology Co., China	LSSW-Z-2011
Phorbol 12-myristate 13-acetate (PMA)	Sigmaaldrich, USA	P1585
p-JAK Antibody	WuHan LingSi Biotechnology Co., Ltd., China	LSSW-K-7856
p-STAT3 Antibody	WuHan LingSi Biotechnology Co., Ltd., China	LSSW-L-1218
Reverse Transcription Kit	Takara Bio, Inc.	RR037A
RIPA Lysing Solution	Beyotime, China	P0013B
RNA immunoprecipitation kit	Life Technologies, USA	20164
RPMI-1640	GIBCO, USA	11875119
STAT3 Antibody	WuHan LingSi Biotechnology Co., Ltd., China	LSSW-L-2121
Table-top centrifuge	Thermo Fisher	Pico17
Tanon 5200 system	Tanon, China	Tanon 5200
TB Green Premix Ex Taq II	Takara Bio, Inc	RR820A
TGF-β ELISA kits	WuHan LingSi Biotechnology Co., Ltd., China	LSSW-T-2121
Transwell cell plate	Corning, China	3460
TRIzol reagent	Life Technologies, USA	15596018
TSG101 Antibody	Abcam, UK	ab125011
Tubulin Antibody	Proteintech, China	10068-1-AP