Shuangshen granules enhance anti-PD1 therapy efficacy in lung adenocarcinoma by modulating myeloid-derived suppressor cell-induced T cell exhaustion

Shuangshen granules enhance anti-PD1 therapy efficacy in lung adenocarcinoma by modulating myeloid-derived suppressor cell-induced T cell exhaustion | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Shuangshen granules enhance anti-PD1 therapy efficacy in lung adenocarcinoma by modulating myeloid-derived suppressor cell-induced T cell exhaustion Zhong-ning He, Qi Huang, Yi Li, Jia-qi Hu, Tong-tong Liu, Yu-wei Zhao, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7686641/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Background Worldwide, lung cancer is the most common cause of cancer-related deaths. Molecular targeted therapies and immunotherapies for non-small-cell lung cancer (NSCLC) have improved outcomes markedly over the past two decades. However, the vast majority of advanced NSCLCs become resistant to current treatments and eventually progress. A traditional Chinese medicine (TCM) formula of Shuangshen granules (SSG) has demonstrated potential in alleviating cancer side effects and imporving survival rate. Despite clinical evidence supporting its benefit, there is still insufficient understanding of the active compounds in SSG and their underlying mechanisms, which limits its broader clinical application. Objective This study aims to identify the key active ingredients in SSG and explore their mechanisms, particularly through modulating myeloid-derived suppressor cell (MDSC)- induced T cell exhaustion, to provide a scientific basis for its application in cancer treatment. Methods The primary active compounds, potential therapeutic targets and intervening signaling pathways, which SSG might inhibit lung adenocarcinoma (LUAD) were predicted by network pharmacology and molecular docking. Subsequently, Lewis lung carcinoma (LLC) tumor-bearing mouse model was established to assess the efficacy of combined SSG and anti-PD-1 therapy in vivo, and MDSCs and CD8 + T cells were isolated for in vitro co-culture experiments, while pathological examination was conducted using hematoxylin and eosin (HE). The expression of PD-1, TIM-3, CTLA-4, LAG-3 Arg-1, IDO, iNOS, PD-L1 and Gal-9 was detected using immunohistochemistry (IHC), immunofluorescence, and flow cytometry and Western blotting. The expression of IL-2, TNF-α and IFN-γ were detected by reverse transcription-quantitative polymerase chain reaction (qPCR). Concentrations of IL-10 and TGF-β were measured by enzyme-linked immunosorbent assay (ELISA) Results We obtained 19 active ingredients of SSG and predicted 37 potential targets through network pharmacology analysis, among which MOL001792, MOL000449, MOL000358 and MOL000098 were selected as core drug ingredients, and EGFR, IL1B, IL6 and TNF were identified and included into the range of core targets. GO and KEGG analyses suggested that the TNF signaling pathway might hold a crucial role in lung cancer by reducing MDSC and T-cell exhaustion. In the animal experiment, SSG increased TNF-α levels and reduction of T cell exhaustion markers and the down-modulation of MDSC suppressive mediators. Conclusion This investigation identifies MOL001792, MOL000449, MOL000358 and MOL000098 as critical active ingredients in SSG, impacting key biomarkers such as EGFR, IL1B, IL6 and TNF. These substances effectively modulate the TNF signaling pathway, alleviating MDSC-induced T cell exhaustion and restoring anti-tumor immune function. Shuangshen granules lung adenocarcinoma myeloid-derived suppressor cells immunotherapy T cell exhaustion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Lung cancer remains the most commonly diagnosed malignancy and the leading cause of cancer-related death worldwide. In 2022, it accounted for nearly 2.5 million new cases and more than 1.8 million deaths, representing approximately one in eight of all cancer diagnoses (12.4%) and almost one in five cancer deaths (18.7%) globally. Despite advances in surgery, radiotherapy, chemotherapy, and targeted therapies, the overall prognosis of lung cancer patients remains poor, underscoring the urgent need for more effective treatment strategies [ 1 ]. In recent years, immune checkpoint inhibitors (ICIs), particularly antibodies targeting the programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) axis, have revolutionized the treatment landscape of advanced non-small cell lung cancer (NSCLC) [ 2 , 3 ]. These agents can restore antitumor T cell activity and prolong survival in a subset of patients [ 4 , 5 ]. However, the clinical benefit of PD-1/PD-L1 blockade is limited, as only a fraction of patients achieves durable responses. A major obstacle to immunotherapy efficacy is the immunosuppressive tumor microenvironment (TME), which promotes T cell dysfunction and exhaustion [ 6 , 7 ]. T cell exhaustion is a state of T cell dysfunction that arises during chronic infection and tumor progression. It is characterized by the upregulation and co-expression of multiple inhibitory receptors and a concomitant reduction in the cytotoxic activity of effector T cells [ 8 ]. This exhausted state weakens the anti-tumor immune function of T cells and also limits the effectiveness of ICIs and other immunotherapies [ 9 ]. Among the key regulators of this immunosuppressive milieu are myeloid-derived suppressor cells (MDSCs), a heterogeneous population of immature myeloid cells that expand in cancer and inhibit antitumor immunity [ 10 ]. MDSCs suppress CD8⁺ T cell function through the expression of arginase-1, inducible nitric oxide synthase, and immunoregulatory cytokines, while also driving the upregulation of multiple exhaustion markers, including PD-1, TIM-3, and LAG-3. This culminates in the impaired function of tissue-resident memory T cells (TRMs), which are critical for sustained local immune surveillance and antitumor responses [ 11 – 13 ]. Therefore, targeting MDSCs within the immunosuppressive tumor microenvironment is considered a promising approach to reverse T cell exhaustion [ 14 , 15 ]. Traditional Chinese medicine (TCM) has long been used as an adjuvant approach in oncology, and accumulating evidence suggests that specific herbal formulations may modulate immune responses and improve therapeutic outcomes due to its complex composition and multi-target mechanisms. Feiyuping ointment, which contains Shuangshen granules (SSG) as the primary component, is commonly used in clinical practice for NSCLC patients and has shown promise as an effective TCM therapy. Studies have demonstrated that SSG can significantly improve quality of life and extend survival in patients with NSCLC [ 16 , 17 ]. SSG is a formulation developed at Guang’anmen Hospital (patent No. 201310091864.4) composed of Panax quinquefolium L., Panax notoginseng, and Cordyceps sinensis, and it has undergone extensive pharmacological and toxicological testing. For nearly a decade, SSG has shown notable therapeutic benefits in the clinical management of lung adenocarcinoma. Previous research suggests that SSG inhibits the differentiation of myeloid cells into MDSCs, thereby limiting lung cancer metastasis [ 18 , 19 ]. It has shown potential in restoring immune balance and enhancing the efficacy of conventional therapies. Nevertheless, its precise effects on MDSC-mediated immunosuppression and TRM cell exhaustion in the context of PD-1 blockade remain unclear. Therefore, in this study, we investigated the impact of SSG on tumor growth, immune cell composition, and functional cytokine expression in a murine model of lung adenocarcinoma. We further explored the potential of SSG to alleviate MDSC-induced T cell exhaustion and augment the efficacy of anti-PD-1 therapy, providing new mechanistic insights and translational implications for combining TCM with modern immunotherapy. 2. Materials and methods 2.1 Materials, reagents, and cell lines Panax quinquefolium L. (Batch No. 23011602), Panax notoginseng (Batch No. 21030903), and Cordyceps sinensis (Batch No. 230360511) were obtained from Guang’anmen Hospital of the China Academy of Chinese Medical Sciences (Beijing, China) (see Table 1 for details). Ginsenoside Rg1 (No. 110703–202235), ginsenoside Rb1 (No. 110704–202331), ginsenoside Rd (No. 111818–202104), and notoginsenoside R1 (No. 110745–202322) were obtained from the China Institute of Food and Drug Control (Beijing, China). RIPA lysis buffer was from Promega (Madison, WI, USA), RPMI-1640 medium was from Solarbio (Beijing, China), and DMEM and fetal bovine serum (FBS) were from HyClone (Logan, UT, USA). Primary antibodies against Arg-1 (arginase-1, #93668), IDO (#51851), PD-L1 (#64988), TIM-3 (#83882), CTLA-4 (#53560), LAG-3 (#80282), and β-actin (#3700) were purchased from Cell Signaling Technology (CST, Boston, MA, USA). Antibodies against iNOS (ab178945), Galectin-9 (Gal-9, ab69630), PD-1 (ab214421), and BTLA (ab216505) were from Abcam (Cambridge, UK). An anti-mouse PD-1 monoclonal antibody (clone RMP1-14, BE0273) for in vivo use was obtained from BioXcell (West Lebanon, NH, USA). Fluorophore-conjugated antibodies for flow cytometry were purchased from BioLegend (San Diego, CA, USA), including FITC–anti-CD11b (Cat# 101205), PerCP/Cyanine5.5–anti-Gr-1 (Cat#108425), PE–anti-PD-L1 (Cat#155403), PE/Cyanine7–anti-Gal-9 (Cat#137913), FITC–anti-CD3 (Cat#100203), PerCP/Cyanine5.5–anti-CD8a (Cat#100733), PE–anti-PD-1 (Cat#114117), and Brilliant Violet 605–anti-TIM-3 (Cat# 119721). ELISA kits for IL-10, TNF-α, IL-2, IFN-γ, and TGF-β were from NeoBioscience (Shenzhen, China). Recombinant GM-CSF (Cat#315-03) and IL-6 (Cat#216 − 16) were obtained from PeproTech (Rocky Hill, NJ, USA). The Lewis lung carcinoma (LLC) cell line was obtained from the National Infrastructure of Cell Line Resource (Beijing, China) and maintained in DMEM supplemented with 10% FBS at 37°C in a humidified 5% CO 2 incubator. Table 1 The information pertaining to the Chinese medicines found in the SSG-derived herb Chinese Name Latin name Family Place of origin (province) Used part Major effective compound in modern pharmacology study Xi Yang Shen Panax quinquefolium Araliaceae Ji Lin Rhizome Ginsenosides San Qi Panax notoginseng Araliaceae Yun Nan Rhizome Notoginsenosides Dong Chong Xia Cao Cordyceps sinensis Clavicipitaceae Tibet Stroma–larva complex a Nucleosides, polysaccharides, and cordycepin a The dried complex of the stromata of the fungus and the dead bodies of the larvae of insects in the family Bat Moths 2.2 Network pharmacological analysis of SSG Active compounds in SSG were identified from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) based on criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 [ 20 ]. Predicted targets of these compounds were intersected with immune-related genes (from the ImmPort database) and lung adenocarcinoma–related genes (from the GeneCards database) to identify common targets. (Lung adenocarcinoma targets from GeneCards were filtered by relevance score using two rounds of median cutoff to improve reliability.) The overlapping targets among SSG compounds, immune-related genes, and LUAD-related genes were determined using Venny 2.1.0 and were subsequently used to construct a drug–target protein-protein interaction (PPI) network via the STRING database (v12.0). A compound–target–disease network was visualized using Cytoscape v3.9.1. Functional enrichment analysis of the common targets was performed using Metascape (for Homo sapiens) with the criteria of a minimum count of 3, P 1.5. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to elucidate the biological functions and pathways associated with the targets [ 21 ]. 2.3 Molecular docking study Molecular docking was carried out using AutoDock Vina v1.5.6 to evaluate potential interactions between key active SSG compounds and high-priority protein targets identified from the network analysis. The crystal structures of target proteins were obtained from the Protein Data Bank ( http://www1.rcsb.org/ ) and prepared by removing water molecules using PyMOL. Chemical structures of the major active components were sketched and optimized to low-energy three-dimensional conformations using ChemDraw. Each compound was then docked to its respective target protein using AutoDock Vina, and the binding pose with the lowest binding free energy was recorded for analysis. 2.4 Establishment of the tumor-bearing mouse model and SSG treatment SSG was prepared from authenticated raw herbal materials (Panax quinquefolius, Panax notoginseng, and Cordyceps sinensis) by pulverizing the herbs and mixing them in a 1:6:1 (w/w/w) ratio. Quality control for the raw herbs and the final formulation followed protocols from our previous studies [ 18 , 22 ]. Male C57BL/6J mice (4–5 weeks old) were acclimated for 7 days prior to tumor implantation. To establish the tumor model, LLC cells (5×10 ^ 6 in 100 µL PBS) were injected subcutaneously into the left flank of each mouse [ 23 ]. To determine the optimal SSG dosage, tumor-bearing mice were randomly divided into four groups (n = 5 per group): Control, low-dose SSG (SSG-L), medium-dose SSG (SSG-M), and high-dose SSG (SSG-H). The SSG-L dose was based on the human equivalent dose of the herbal formula (~ 6 g per 70 kg adult), which corresponds to approximately 18.0 mg/day for a ~ 20 g mouse using a body surface area conversion factor (mouse:human ratio of 9.01). SSG-M and SSG-H were set at two times (36.0 mg/day) and three times (54.0 mg/day) the SSG-L dose, respectively. SSG was administered orally once daily for 21 days, while the Control group received an equal volume of water. After 21 days, the mice were euthanized and tumor tissues were excised for analysis by H&E staining and IHC. Based on the extent of tumor inhibition observed, the high dose of SSG was selected as the optimal dose for subsequent experiments. For the combination therapy experiment, another set of LLC tumor-bearing mice were randomly assigned to four groups (n = 8 per group): Control, SSG alone, anti-PD-1 alone, and SSG + anti-PD-1. Mice in the SSG group received the optimal dose of SSG orally each day for 21 days. Mice in the anti-PD-1 group received intraperitoneal injections of anti-mouse PD-1 antibody (200 µg per mouse, every other day) starting on day 7 post-tumor inoculation, for a total of 7 injections (14 days). Control group mice received oral water and i.p. saline on matched schedules. On day 21, all mice were euthanized, and tumor tissues, peripheral blood, and spleens were collected for further analysis. All animal procedures were approved by the Animal Ethics Committee of Guang’anmen Hospital, China Academy of Chinese Medical Sciences (Approval No. IACUC-GAMH-2022-011). The experimental timeline is illustrated in Fig. 1 . 2.5. HE and IHC Excised tumor tissues were fixed in 4% neutral buffered formalin, embedded in paraffin, and sectioned into 4–6 µm slices. For H&E staining, sections were deparaffinized, rehydrated, and stained with hematoxylin followed by eosin. For IHC, tissue sections underwent antigen retrieval in sodium citrate buffer (pH 6.0) and quenching of endogenous peroxidase activity with 3% H 2 O 2 in methanol. Sections were then blocked with 5% BSA and incubated with primary antibodies overnight at 4°C. After washing, HRP-conjugated secondary antibodies were applied, and immunoreactivity was visualized using a DAB chromogen. Sections were counterstained (with hematoxylin), mounted with neutral resin, and examined under a light microscope for imaging and scoring. 2.6. Immunofluorescence Paraffin-embedded tumor sections were deparaffinized and subjected to antigen retrieval as above. After blocking with 5% BSA for 1 h at room temperature, sections were incubated with primary antibodies against CD3, PD-1, CD11b, or PD-L1. Subsequently, sections were incubated with species-appropriate Alexa Fluor–conjugated secondary antibodies. Nuclei were counterstained with DAPI. Stained sections were observed and imaged using a laser scanning confocal microscope (Leica Microsystems, Wetzlar, Germany). 2.7. Flow cytometry Single-cell suspensions from mouse spleens or cultured cells were prepared and washed with PBS. Cells were first incubated with an anti-mouse CD16/32 Fc-blocker for 15 min on ice to prevent nonspecific antibody binding. Two antibody staining panels were then applied: one panel (for myeloid-derived cells) included FITC-anti-CD11b, PerCP/Cyanine5.5-anti-Gr-1, PE-anti-PD-L1, and PE/Cyanine7-anti-Gal-9; the other panel (for T cells) included FITC-anti-CD3, PerCP/Cyanine5.5-anti-CD8a, PE-anti-PD-1, and BV605-anti-TIM-3. Staining was performed in the dark at 4°C for 25 min. After staining, cells were washed (1500 rpm, 5 min) and resuspended in FACS buffer (PBS with 2% FBS). Data acquisition was done on a BD LSR II flow cytometer (BD Biosciences), and the data were analyzed using FlowJo v10 software. 2.8. Western blotting Tumor tissue samples or cultured cells were lysed in ice-cold RIPA buffer containing protease and phosphatase inhibitors (Topscience, Shanghai, China) for 30 min. Lysates were clarified by centrifugation at 12,000 rpm for 15 min at 4°C, and supernatants were collected. Equal amounts of protein (determined by BCA assay) were separated by SDS-PAGE and transferred onto PVDF membranes. Membranes were blocked with 5% BSA in TBST for 1 h at room temperature and then incubated with primary antibodies at 4°C overnight. After washing, membranes were probed with HRP-conjugated secondary antibodies (ZSGB-Bio, Beijing, China) for 1 h at room temperature. Immunoreactive bands were detected using enhanced chemiluminescence (ECL) reagents and imaged. Band intensities were quantified using ImageJ software (v1.53a). 2.9. Enzyme-linked immunosorbent assay Peripheral blood samples and cell culture supernatants were centrifuged at 12,000 rpm for 20 min at 4°C to obtain cell-free supernatants. The concentrations of IL-10 and TGF-β in these supernatants were measured using specific ELISA kits according to the manufacturers’ protocols. Absorbance at the appropriate wavelength was read using a GloMax Discover microplate reader (Promega, Madison, WI, USA), and cytokine concentrations were calculated from standard curves. 2.10. Reverse transcription-quantitative polymerase chain reaction Total ribonucleic acid (RNA) was extracted from mouse tumor tissues using a Rapid RNA Purification Kit (ES Science, Shanghai, China). cDNA was synthesized with the RevertAid reverse transcription kit (Yeasen, Shanghai, China), followed by reverse transcription-quantitative polymerase chain reaction on an applied biosystems (Bio-Rad T100, USA) to quantify IL-2, IFN-γ, and TNF-α mRNA levels. Gene expression was normalized using GAPDH as a stably expressed reference gene. Triplicate experiments ensured reproducibility, and primers were designed via primer-basic local alignment search tool (National Center for Biotechnology Information, National Institutes of Health, USA) (Table 2 ). Table 2 Primer sequences for reverse transcription-quantitative polymerase chain reaction. Gene Primer sequence (5’–3’) IL-2 Forward GGCCAGCTGTGAGTGTTTCTTTGG Reverse CTCGCTTCATCTTCCCTCTTGGG IFN-γ Forward TGAACGCTACACACTGCATCTTGG Reverse CTCCTTTTCCGCTTCCTGAG TNF-α Forward CCCTCACACTCAGATCATCTTCT Reverse GCTACGACGTGGGCTACAG GAPDH Forward GTGGACCTGACCTGCCGTCT Reverse GGAGGAGTGGGTGTCGCTGT 2.11. Preparation of SSG drug-containing serum Male Wistar rats with initial body weights of (180–200) g received SSG extract (62.5 mg/kg, intragastrically, BID) for 7 days. This dose was based on HED conversion from a 6 g/70 kg human dose (rat:human ratio 6.25). Two hours after the final administration, the blood was collected from the aorta and refrigerated overnight at 4°C. Following centrifugation at 3,000 rpm for 15 min, sera were collected and stored at -20°C. The experimental protocol was approved by the Animal Welfare and Ethics Committee of Guang’anmen Hospital, China Academy of Chinese Medical Sciences and conducted in compliance with the International Council for Laboratory Animal Science guidelines (approval No. IACUC-GAMH-2024-004-SQ). 2.12. Component analysis for SSG drug-containing serum The major chemical constituents in SSG-containing serum were analyzed using high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). The analysis was performed on an ExionLC-20AD HPLC system coupled to an AB Sciex mass spectrometer, following a previously described protocol [ 24 ]. Optimized multiple reaction monitoring (MRM) parameters for each ginsenoside are listed in Table 3 . Prior to analysis, 100 µL of serum was mixed with 300 µL of methanol–acetonitrile (4:1, v/v), vortexed at 6000 rpm for 5 min, and then centrifuged at 20,000 × g for 10 min at 4°C. The supernatant was collected and evaporated to dryness under a stream of nitrogen. The residue was re-dissolved in methanol to a concentration of 10 mg/mL. A stock solution of mixed standard compounds (1 mg/mL each in methanol) was prepared and serially diluted to generate calibration curves. LC-MS/MS data were acquired in both positive and negative ion modes over an m/z range of 30–1300, with a scan time of 0.4 s. Data acquisition and analysis were performed using MassLynx v4.1 (Waters Corp. Milford, MA, USA) and its Elemental Composition tool (Waters). Table 3 The optimized HPLC-MS parameters of each component Component Quantitative ion pair Dwell time /ms Collision energy /eV Declustering Potential /V Retention /min Ginsenoside Rg1 823.2/643.3 20 56 129 9.33 Ginsenoside Rb1 1131.7/789.3 20 75 260 10.71 Ginsenoside Rd 969.7/789.6 20 61 270 11.22 Notoginsenoside R1 955.3/775.4 20 46 135 9.07 2.13. Isolation of MDSCs and CD8 + T cells Bone marrow cells were harvested from the femurs and tibias of 4–5-week-old C57BL/6J mice. After red blood cell lysis and washing with PBS, the bone marrow cells were cultured in RPMI-1640 medium containing 10% FBS and supplemented with 100 ng/mL GM-CSF and 100 ng/mL IL-6 to induce the differentiation and expansion of MDSCs. After an incubation period (allowing sufficient MDSC generation), cells were collected and adjusted to 5 × 10^6 cells/mL. The cells were labeled with anti-CD11b and anti-Gr-1 antibodies and analyzed by flow cytometry to confirm the successful enrichment of MDSCs. The Gr-1 antibody recognizes epitopes on both Ly6G and Ly6C, thereby labeling the entire MDSC population [ 25 ]. The MDSC-enriched cells were then treated with culture medium containing SSG-containing rat serum for 24 h. After treatment, the MDSCs and their culture supernatants were collected for analysis (flow cytometry, Western blotting, and ELISAs) to evaluate changes in their immunosuppressive features. For CD8 + T cell isolation, spleens were collected from 4–5-week-old C57BL/6J mice. Single-cell suspensions were prepared by grinding spleen tissues through 70 µm cell strainers. After red cell lysis, CD8 + T cells were isolated using a magnetic CD8a + T Cell Isolation Kit (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s instructions. Briefly, splenocytes were incubated with anti-CD8a magnetic microbeads and passed through MS columns in a magnetic field; the enriched CD8 + T cells were then eluted and verified by flow cytometry for purity. MDSCs and CD8 + T cells (both pretreated with SSG-containing serum as described above) were co-cultured using a Transwell system to assess functional interactions. CD8 + T cells were placed in the lower chamber of a Transwell plate, and MDSCs were added to the upper insert (pore size ~ 0.4 µm, which permits exchange of factors but prevents cell migration). The two cell populations were co-cultured for the durations specified in subsequent assays. After co-culture, cells (and conditioned media) were collected for flow cytometry, Western blotting, and ELISA analyses, as described earlier. 2.14. CFSE proliferation assay Proliferation of CD8 + T cells was assessed using a CFSE dilution method. Isolated CD8 + T cells were labeled with 5 µM CFSE (Carboxyfluorescein diacetate succinimidyl ester; BD Biosciences) in the dark at 37°C for 15 min. Staining was quenched by adding five volumes of cold RPMI-1640 medium. The cells were washed and then resuspended in RPMI-1640 complete medium containing 200 ng/mL IL-2, 10 µg/mL anti-CD3ε, and 10 µg/mL anti-CD28 to provide costimulatory signals. CFSE-labeled CD8 + T cells (5 × 10^5) were co-cultured with MDSCs (also pretreated with SSG serum) at a 1:1 ratio in 96-well plates for 72 h. After 72 h, cells were harvested and the dilution of CFSE intensity in CD8 + T cells was measured by flow cytometry to determine T cell proliferation (a decrease in CFSE fluorescence indicates cell division). 2.15. Apoptosis assay CD8 + T cell apoptosis was evaluated after co-culture with MDSCs using an Annexin V-FITC/PI apoptosis detection kit (BD Biosciences, Cat# 556547). CD8 + T cells and MDSCs were co-cultured for 24 h in a Transwell setup (CD8 + T cells in the lower well, MDSCs in the upper insert). After 24 h, CD8 + T cells were collected, washed with cold PBS, and resuspended in 500 µL of binding buffer. The cells were then stained with 5 µL of Annexin V–FITC and 5 µL of propidium iodide (PI) and incubated in the dark for 30 min at room temperature. Early and late apoptosis of CD8 + T cells were analyzed by flow cytometry, with Annexin V + PI − cells considered early apoptotic and Annexin V + PI + cells considered late apoptotic. 2.16. Single-cell RNA sequencing and data preprocessing Fresh tumor tissues from each group were dissociated into single-cell suspensions using enzymatic digestion according to the manufacturer’s protocol (Miltenyi Biotec, Germany). Viable single cells were counted and loaded onto the 10x Genomics Chromium platform (10x Genomics, Pleasanton, CA, USA) to generate single-cell gel beads-in-emulsion (GEMs). Libraries were constructed using the Chromium Single Cell 3′ Reagent Kit (v3.1) and sequenced on the Illumina NovaSeq 6000 platform with paired-end reads. Raw sequencing data were processed with the Cell Ranger pipeline (v6.0, 10x Genomics) for demultiplexing, alignment to the mouse reference genome (mm10), and gene expression quantification. Cells with fewer than 200 detected genes or with > 10% mitochondrial transcripts were excluded. Genes expressed in fewer than three cells were filtered out. Quality-controlled expression matrices were analyzed using the Seurat R package (v4.3.0). Normalization was performed by the “LogNormalize” method, and highly variable genes were identified. Principal component analysis (PCA) was conducted, followed by Uniform Manifold Approximation and Projection (UMAP) for visualization. Cell clusters were identified using the Louvain algorithm with a resolution parameter of 0.6. Cluster annotation was based on canonical marker genes for T-cell subtypes, including Cd4, Cd8a, and Treg. Differentially expressed genes (DEGs) between clusters and groups were identified using the Wilcoxon rank-sum test, with adjusted p < 0.05 considered significant. Expression of immune checkpoint molecules Pdcd1 PD-1, CTLA-4, TIM-3, LAG-3, and TIGIT was evaluated across T-cell subsets. Proportions of checkpoint-positive CD8 + T cells were quantified and compared between groups. Visualization was performed with heatmaps, feature plots, and UMAP distribution plots using Seurat and ggplot2 packages. 2.17. Statistical analysis Normality and variance homogeneity were assessed prior to analysis. For normally distributed parameters with equal variances, data are expressed as mean ± standard deviation and analyzed by parametric one-way analysis of variance (ANOVA), with post-hoc comparisons adjusted via False Discovery Rate test. When normality or variance assumptions were violated, non-normally distributed parameters are presented as median (interquartile range) and analyzed using the Kruskal-Wallis test (non-parametric equivalent of ANOVA), followed by Dunn's post-hoc comparisons. All statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software, USA), with statistical significance defined as a two-tailed P < 0.05. 3. Results 3.1. Potential therapeutic targets of SSG for LUAD, PPI, enrichment analysis, network diagram construction and Molecular docking A total of 26 active ingredients of SSG were identified for network pharmacology analysis, including 7 compounds from Cordyceps sinensis and 8 from Panax notoginseng. According to TCMSP data, 19 of these 26 ingredients have reported potential anticancer properties. Using Venny 2.1.0, we identified 37 common gene targets that overlapped among SSG’s active compounds, immune-related genes, and lung adenocarcinoma–related genes (Fig. 2 A). On the basis of the discussion on the ref erences information and network analysis conclusions, beta-sitosterol, papaverine, Daidzein-7-O-β-D-glucoside, Stigmasterol and quercetin were chosen as the core ingredients in Table 4 . A herb–compound–target–disease network was constructed to visualize these interactions (Fig. 2 B), where nodes represent herbs, chemical components, targets, or diseases, and edges indicate their relationships. Network topology analysis (node degree) highlighted the most influential compounds and targets in this network. In the protein–protein interaction (PPI) network of SSG targets, we applied a minimum confidence score of 0.7 to filter meaningful interactions (Fig. 2 C). This analysis identified several hub targets, including IL-6, IL-1β, TNF, EGFR, and JUN, which may play central roles in SSG’s pharmacological effects. Pathway enrichment results further underscored the immune relevance of these targets. The top 20 KEGG pathways enriched by the common targets (Fig. 2 D) were predominantly immune-related, suggesting that SSG’s effects are strongly tied to immune modulation and checkpoint pathways. GO enrichment analysis (Fig. 2 E) indicated that the targets are involved in numerous biological processes and molecular functions, such as regulation of inflammatory responses, nuclear receptor activity, signaling receptor activity, and cytokine activity. Notably, many of these pathways are critically involved in controlling MDSC differentiation and function, as well as T cell exhaustion in the tumor microenvironment. Molecular docking simulations provided additional support for these network findings. Key active compounds of SSG (e.g., quercetin, luteolin, kaempferol, isorhamnetin) were docked with representative hub targets (TNF, IL-6, IL-1β, EGFR). The docking results showed favorable binding interactions; for instance, quercetin was predicted to bind strongly to IL-1β (estimated binding free energy ≈ − 8.0 kcal/mol) (Fig. 2 F). A heatmap of docking affinities (see Supplementary Figure) indicated generally strong binding between the major SSG compounds and the core targets identified, lending credence to the importance of these compound–target interactions. Table 4 The core compounds of SSG against LUAD ranked top 5 Molecular ID compounds Herbs OB(%) DL MOL000358 beta-sitosterol Xi Yang Shen 36.91 0.75 MOL006980 papaverine Xi Yang Shen 64.04 0.38 MOL001792 Daidzein-7-O-β-D-glucoside San Qi 32.76 0.18 MOL000449 Stigmasterol San Qi 43.83 0.76 MOL000098 quercetin San Qi 46.43 0.28 3.2. SSG inhibits tumor growth in a xenograft mouse model We first evaluated the effect of different SSG doses on tumor growth using the LLC xenograft mouse model. SSG treatment significantly suppressed tumor growth in a dose-dependent fashion, with higher doses of SSG producing more pronounced tumor inhibition (Fig. 3 A–C). Throughout the 21-day treatment, SSG was well tolerated: all groups of mice showed stable body weights with no significant differences among the control and SSG-treated groups (Fig. 3 D). Histological examination of tumor tissues revealed that higher doses of SSG led to a noticeable reduction in the proportion of abnormally shaped (irregular) tumor cells (Fig. 3 E). Consistently, IHC staining showed that tumors from the high-dose SSG group had markedly lower expression of proliferation markers (CD34 and Ki-67) compared to tumors from control mice (Fig. 3 F). These findings demonstrate that SSG exerts antitumor effects in vivo, and that a high dose of SSG is most effective at suppressing tumor growth and tumor cell proliferation. Accordingly, we selected the high-dose SSG regimen for subsequent experiments. 3.3. SSG enhances the efficacy of anti-PD-1 therapy in tumor-bearing mice We next examined whether SSG could augment the therapeutic efficacy of PD-1 blockade. In LLC tumor-bearing mice, SSG alone significantly inhibited tumor growth, and the combination of SSG with an anti-PD-1 antibody resulted in the greatest tumor suppression (Fig. 4 A–C). By the end of the experiment, the combination treatment (SSG + anti-PD-1) achieved the smallest tumor volumes among all groups. During the treatment period, we monitored the body weights of the mice as an indicator of systemic effects. Mice receiving SSG or SSG + anti-PD-1 exhibited a slight divergence in weight gain starting around day 14, resulting in marginally lower body weights than control mice by day 21 (Fig. 4 D). However, the weight differences were not accompanied by any overt signs of toxicity or distress. Histopathology provided further evidence of enhanced efficacy in the combination group. H&E-stained tumor sections from the SSG + anti-PD-1 group showed more uniform, regular tumor cell morphology (cells were predominantly round or ovoid) compared to the more pleomorphic cells observed in controls (Fig. 4 E). Moreover, IHC analysis demonstrated that the combination therapy greatly reduced the expression of proliferative markers in tumor tissues. CD34 and Ki-67 levels were dramatically lower in the SSG + anti-PD-1 group relative to both the control group and either monotherapy group (Fig. 4 F). Importantly, the expression of T cell exhaustion markers in tumors was also lowest in the combination group: PD-1, TIM-3, CTLA-4, and LAG-3 staining in tumor-infiltrating lymphocytes was substantially reduced with SSG + anti-PD-1 treatment (Fig. 4 F). These data indicate that SSG can potentiate anti-PD-1 immunotherapy, leading to stronger tumor growth inhibition and a more favorable tumor immune milieu than anti-PD-1 therapy alone. 3.4. SSG enhances T cell infiltration and reduces T cell exhaustion in vivo To assess SSG’s impact on T cells in the context of PD-1 therapy, we analyzed immune cell populations and cytokines in treated mice. Flow cytometry of spleen samples showed that anti-PD-1 monotherapy slightly increased certain immune responses but was paradoxically associated with a reduction in the proportion of splenic CD8 + T cells (possibly reflecting T cell redistribution or negative feedback). In contrast, mice treated with SSG had a significantly higher percentage of CD8 + T cells in the spleen, and the combination of SSG + anti-PD-1 restored CD8 + T cell frequencies to levels comparable to or higher than control mice (Fig. 5 A). We next examined T cell “exhaustion” status by measuring key inhibitory receptors on CD8 + T cells. SSG treatment led to lower expression of exhaustion markers, and the addition of SSG to anti-PD-1 therapy had an additive effect. In the combination group, surface levels of PD-1 and TIM-3 on CD8 + T cells were markedly reduced compared to either SSG or anti-PD-1 alone (Fig. 5 B). Similarly, the combination group showed the lowest expression of other inhibitory receptors (e.g., CTLA-4, BTLA, LAG-3) on T cells (data not shown in figure, see Western blot results below). Consistent with the improved T cell profiles, SSG-treated mice (especially when combined with PD-1 blockade) had significantly elevated levels of Th1-type cytokines. ELISA results from serum (or spleen homogenates) demonstrated that IL-2, TNF-α, and IFN-γ concentrations were higher in the SSG and SSG + anti-PD-1 groups than in controls, with the combination therapy yielding the highest cytokine levels (Fig. 5 D). These findings suggest that SSG enhances anti-tumor immunity in vivo by expanding the CD8 + T cell pool, relieving T cell exhaustion, and promoting a pro-inflammatory cytokine environment. Double immunofluorescence staining of tumor sections provided visual confirmation of these immune changes. In control tumors, there was strong co-localization of PD-1 with CD3 + T cells, indicating that many tumor-infiltrating T cells were PD-1 high (exhausted). In contrast, SSG-treated tumors (especially those also treated with PD-1 antibody) showed markedly fewer PD-1 + T cells (reduced PD-1/CD3 co-localization). We did not observe significant co-localization of PD-L1 with CD11b in control tumors, suggesting that in this model, PD-L1 may be expressed more on tumor or other cells than on MDSCs. Notably, SSG treatment (± anti-PD-1) appeared to reduce overall PD-L1 expression in the tumor tissue, with the most pronounced reduction seen in the combination group (as qualitatively shown in Fig. 6 ). These microscopy results align with the flow cytometry and IHC data, indicating that SSG contributes to an immune-permissive tumor environment by modulating both T cells and suppressive myeloid cells. 3.5. SSG reduces MDSC accumulation and suppresses MDSC immunosuppressive functions in vivo We then investigated how SSG affects MDSCs in tumor-bearing mice. Flow cytometric analysis of splenocytes revealed that mice treated with SSG had significantly fewer MDSCs (identified as CD11b + Gr-1 + cells) compared to control mice (Fig. 7 A). In control mice, splenic MDSCs expressed high levels of PD-L1 and Galectin-9 on their surface. Treatment with SSG led to a notable downregulation of these exhaustion-inducing ligands on MDSCs, and the combination of SSG with anti-PD-1 resulted in the most significant reductions in PD-L1 and Gal-9 levels (Fig. 7 B). These data suggest that SSG not only reduces the number of MDSCs but also impairs the ability of any remaining MDSCs to inhibit T cells. Key MDSC-associated enzymes that mediate immune suppression—Arg-1, IDO, and iNOS—were all markedly downregulated in the SSG-treated groups, with the greatest decrease observed in the combination therapy group (Fig. 7 C). In addition, the combination group showed substantially lower levels of PD-L1 and Gal-9 proteins in tumor tissue (reflecting contributions from both MDSCs and tumor cells) compared to controls (Fig. 7 D). SSG treatment alone also reduced PD-L1 and Gal-9 levels relative to control, consistent with an overall less suppressive microenvironment. Functionally, we assessed whether SSG’s effects on MDSCs translated into changes in cytokine production. ELISA measurements indicated that MDSCs from SSG-treated mice secreted significantly lower amounts of IL-10 and TGF-β—two potent immunosuppressive cytokines—compared to MDSCs from control mice (Fig. 7 E). Again, the combination of SSG with anti-PD-1 had the strongest effect, virtually normalizing the levels of these cytokines to baseline. Collectively, these results demonstrate that SSG can attenuate both the abundance and the suppressive activity of MDSCs in vivo, which likely contributes to the improved T cell responses and anti-tumor effects observed with SSG treatment. 3.6. Chemical components identified in SSG-containing serum To facilitate the in vitro assays, we prepared SSG-containing serum from rats and confirmed the presence of key active compounds in the serum. Using UPLC-MS/MS in line with Chinese Pharmacopoeia guidelines (2015 edition), we verified that the prepared serum contained the expected ginsenosides within pharmacopoeial standards. HPLC-MS/MS analysis identified the primary constituents of the SSG-containing serum, and representative MRM chromatograms for each compound are shown in Fig. 8 . Standard curves (Table 5 ) were generated for quantification, and the concentrations of major ginsenosides in the serum were determined by correlating chromatographic peak areas with these standard curves. As summarized in Table 6 , the serum from SSG-treated rats contained approximately 12.92 ng/mL of ginsenoside Rg1, 1723.06 ng/mL of ginsenoside Rb1, 412.56 ng/mL of ginsenoside Rd, and 13.37 ng/mL of notoginsenoside R1. These data confirm that oral administration of SSG leads to systemic exposure of its active ingredients (at least in metabolite form), which provides a rationale for our in vitro experiments using drug-containing serum. While using serum from SSG-treated animals makes the in vitro findings more physiologically relevant, it should be noted that detailed pharmacokinetic studies were not conducted here. In future studies, a more comprehensive pharmacokinetic profiling of SSG’s constituents would be valuable to correlate specific component levels with biological effects, thereby guiding dose optimization and translational prospects. Table 5 Results of standard curve determination of main components in SSG-containing serum Component Linear regression equation Correlation coefficient Linear range ng/mL Ginsenoside Rg1 Y = 4.65*104X-2.85*105 0.9968 4.88 ~ 156.25 Ginsenoside Rb1 y = 1.21*103X-1.31*105 0.9970 9.76 ~ 5000 Ginsenoside Rd y = 7.79*103X-8.24*105 0.9918 9.76 ~ 5000 Notoginsenoside R1 y = 1.155*104X-8.42*104 0.9964 4.88 ~ 156.25 Table 6 Concentrations of main components in SSG-containing serum Component Concentration (ng/mL) Ginsenoside Rg1 12.9237 Ginsenoside Rb1 1723.0593 Ginsenoside Rd 412.5584 Notoginsenoside R1 13.3738 3.7. SSG-containing serum suppresses MDSC immunosuppressive activity and reverses T cell exhaustion in vitro We modeled the interaction between MDSCs and T cells in vitro to further dissect the mechanism of SSG. MDSCs were derived from mouse bone marrow in culture (yielding a > 95% pure CD11b + Gr-1 + population, Fig. 9 A) and then treated with SSG-containing serum. This treatment significantly diminished the immunosuppressive features of the MDSCs. Specifically, after exposure to SSG-conditioned serum, MDSCs showed markedly lower expression of Arg-1, IDO, and iNOS compared to untreated MDSCs (Fig. 9 B). Flow cytometry also revealed that the surface expression of PD-L1 and Gal-9 on MDSCs was greatly reduced following SSG serum treatment (Fig. 9 C). Correspondingly, the amounts of IL-10 and TGF-β released by MDSCs into the culture medium were significantly decreased (as measured by ELISA, Fig. 9 D). These results are in line with our in vivo observations, indicating that SSG can directly impair MDSC-derived suppressive factors. Western blotting of MDSC lysates provided further confirmation: protein levels of PD-L1 and Gal-9 were lower in MDSCs treated with SSG-containing serum than in control MDSCs (Fig. 9 E). Next, we investigated whether these changes in MDSCs could alleviate T cell exhaustion. Purified CD8 + T cells were co-cultured with MDSCs in a Transwell system. In co-cultures where MDSCs had been pretreated with SSG-containing serum, we observed a clear improvement in T cell indicators compared to co-cultures with untreated MDSCs. Flow cytometry showed that CD8 + T cells co-cultured with SSG-treated MDSCs had significantly lower levels of PD-1 and TIM-3 on their surface (Fig. 10 B), suggesting reduced exhaustion. Consistently, Western blot analysis of these CD8 + T cells indicated reduced expression of other exhaustion markers (CTLA-4, BTLA, LAG-3) when the T cells were influenced by SSG-treated MDSCs (Fig. 10 C). Functionally, T cells in the presence of SSG-treated MDSCs were more active: the production of key anti-tumor cytokines (IL-2, IFN-γ, TNF-α) was higher in these co-cultures ( Fig. 10 D). Moreover, the CD8 + T cells co-cultured with treated MDSCs showed lower apoptosis rates and higher proliferation (as evidenced by Annexin V/PI staining and CFSE dilution, respectively) compared to T cells co-cultured with control MDSCs (Fig. 10 E, F). Taken together, these in vitro findings demonstrate that SSG (via factors present in drug-containing serum) can relieve MDSC-induced T cell suppression—by both disabling MDSC suppressive mechanisms and protecting T cells from exhaustion—thereby restoring T cell proliferative capacity and survival. 3.8. Single-cell RNA sequencing reveals attenuation of T-cell exhaustion by SSG treatment Using single-cell RNA sequencing, we comprehensively characterized the immune landscape of T lymphocytes. Uniform Manifold Approximation and Projection (UMAP) revealed distinct cellular clusters, highlighting transcriptional heterogeneity among T cells (Fig. 11 A). Clustering analysis demonstrated clear separation of subsets, suggesting potential functional specialization within the T-cell compartment. Annotation of clusters identified major T-cell populations, including CD4 + T cells, CD8 + T cells, memory T cells, and regulatory T cells (Fig. 11 B). When stratified by experimental groups, differential distribution patterns of T cells were observed between the Model and SSG groups (Fig. 11 C), indicating a group-specific immune response. The expression profiles of canonical immune checkpoint molecules, including PD-1, CTLA-4, TIM-3, LAG-3, and TIGIT, were further examined. Heatmap analysis illustrated marked differences in expression across T-cell subtypes (Fig. 11 D). These findings support the existence of functional heterogeneity among T-cell subsets. Cells positive for PD-1, CTLA-4, TIM-3, LAG-3, and TIGIT were visualized on the UMAP plot (Fig. 11 E), which demonstrated their spatial distribution within the T-cell landscape. When categorized by experimental groups, distinct patterns of checkpoint-positive T cells emerged (Fig. 11 F), further emphasizing the divergent immunological states between groups. We next quantified the proportion of CD8 + T cells expressing checkpoint molecules. Compared with the SSG group, the Model group exhibited a higher proportion of CTLA-4 + , TIM-3 + , LAG-3 + , and TIGIT + CD8 + T cells (Fig. 11 G). This suggests that T-cell exhaustion was more pronounced in the Model group, whereas the SSG group displayed attenuated exhaustion signatures. Collectively, these findings indicate thatCD8 + T cells exhibit distinct transcriptional states and checkpoint expression patterns under different experimental conditions. The enrichment of exhausted CD8 + T cells in the Model group highlights potential mechanisms of immune dysfunction, while the reduction of such populations in the SSG group suggests an immunomodulatory effect. 4. Discussion There is increasing recognition of the value of integrating traditional Chinese medicine (TCM) with contemporary cancer therapies, largely due to its immunomodulatory properties [ 26 ]. Within TCM theory, tumor-induced immunosuppression is often conceptualized as a deficiency of “vital Qi” [ 27 ]. Therapies that “strengthen vital Qi to combat malignancy” are considered to restore immune vigor. Translating this into modern immunology, such interventions are expected to enhance the activity and infiltration of effector immune cells (e.g., cytotoxic T lymphocytes, dendritic cells, natural killer cells) while restraining or reprogramming immunosuppressive populations (e.g., regulatory T cells, tumor-associated macrophages, myeloid-derived suppressor cells, MDSCs) [ 28 ]. Our findings are consistent with this principle, showing that Shuangshen granules (SSG) enhanced antitumor immune responses while attenuating tumor-promoting immunosuppressive mechanisms. Combining TCM formulations with immune checkpoint inhibitors (ICIs) has shown synergistic potential in multiple cancer models. SSG, which contains Panax quinquefolius (American ginseng), Panax notoginseng, and Cordyceps sinensis, has been clinically used as an adjuvant to improve treatment tolerance. Ginseng saponins and cordycepin are known to enhance immunity and exhibit anti-tumor effects [ 29 , 30 ]. We previously reported that SSG inhibited lung cancer metastasis by interfering with MDSC differentiation [ 18 ]. The present study extends these observations by demonstrating that SSG not only reduces markers of T cell exhaustion but also improves the efficacy of PD-1 blockade, effectively reshaping the tumor microenvironment toward immune-mediated control. Notably, mice receiving high-dose SSG showed a trend of body weight loss toward the end of treatment. Although modest and not accompanied by overt toxicity, this observation suggests that prolonged or high-dose exposure may induce mild metabolic alterations. This finding underscores the importance of dose optimization. Establishing an optimal therapeutic window—where efficacy is maintained while minimizing adverse effects—will be critical for the future clinical translation of SSG. Future studies should assess whether reduced dosing or shortened treatment schedules maintain efficacy while minimizing side effects. For clinical translation, dose-escalation studies and close monitoring of body weight and general health will be essential when integrating SSG with ICIs. Our results further underscore the pivotal role of MDSCs in immunotherapy resistance. Elevated MDSC frequencies have been associated with poor responses to PD-1/PD-L1 inhibitors in NSCLC patients [ 31 ]. These cells foster a suppressive milieu by limiting T cell activation and promoting additional immunosuppressive subsets [ 32 ]. We focused on MDSC-derived mediators—Arg-1, IDO, iNOS, PD-L1, and Galectin-9—because they represent major effector arms of suppression. Arg-1 and IDO deplete amino acids required for T cell proliferation [ 33 , 34 ], while iNOS produces nitric oxide that impairs T cell function [ 35 ]. In parallel, PD-L1 and Gal-9 interact with inhibitory receptors such as PD-1 and TIM-3, reinforcing T cell exhaustion [ 13 ]. MDSCs secrete factors such as TGF-β, IL-10, and IDO, which boost the immunosuppressive activity of effector T cells, facilitate immune evasion, and contribute to T cell exhaustion [ 36 – 39 ]. Our findings that SSG reduced these mediators indicate that it can disrupt multiple layers of MDSC-driven suppression. Importantly, SSG treatment not only diminished immunosuppressive factors but also enhanced cytokine production by CD8⁺T cells (IL-2, IFN-γ, TNF-α). The elevation of TNF-α requires careful interpretation. While chronic TNF-α signaling within the tumor microenvironment has been implicated in promoting immunosuppression and tumor progression via NF-κB activation in myeloid cells [ 40 , 41 ], transient increases in T cell–derived TNF-α reflect restored effector function [ 42 ]. In our study, the concurrent reduction of exhaustion markers alongside increased pro-inflammatory cytokines strongly suggests that SSG reinvigorates antitumor T cell activity rather than aggravating immunosuppression. This dual role of TNF-α highlights the complexity of cytokine biology in cancer immunity and warrants further mechanistic dissection of its temporal and cellular contexts. In addition to bulk immunological assays, our single-cell transcriptomic profiling provided mechanistic depth by resolving T cell heterogeneity at high resolution. The analysis was performed on lung tissues from the Model group and the high-dose SSG (SSG-H) group, enabling direct comparison of untreated versus SSG-treated immune landscapes. Consistent with previous reports that exhausted CD8 + T cells are characterized by co-expression of multiple checkpoint receptors [ 43 , 44 ], we observed that the untreated tumor microenvironment was enriched in such dysfunctional subsets. Importantly, SSG treatment attenuated exhaustion signatures and restored functional CD8 + T cell populations. These findings complement our MDSC analyses, suggesting that SSG may alleviate immunosuppression not only by limiting MDSC abundance and function, but also by reversing downstream T cell dysfunction. Compared with prior studies focusing mainly on bulk T-cell markers [ 45 ], our single-cell data highlight the value of integrating high-dimensional approaches to capture the dynamic and heterogeneous immune responses modulated by TCM formulations. Nevertheless, an important limitation of our single-cell analysis is that it was restricted to Model and SSG-H groups. While this design provided clear mechanistic insight into the effect of SSG, future studies including PD-1 blockade and lower-dose SSG groups will be necessary to fully elucidate dose-response relationships and potential interactions between SSG and ICIs at the single-cell level. In terms of mechanistic insights, our results suggest that SSG acts on multiple signaling pathways associated with MDSC activity and T cell exhaustion. Future studies should focus on identifying the precise bioactive constituents—such as ginsenosides (e.g., Rg3, Rh2) and cordycepin—that are known to regulate immune function [ 46 , 47 ]. Mechanistic validation of how these compounds influence canonical signaling pathways including NF-κB, STAT3, and TGF-β/SMAD will be critical for elucidating their contributions to the observed immunomodulatory effects. Such studies will provide direct mechanistic evidence linking SSG’s phytochemical composition with its immunological activity. Finally, the translational implications of our findings are significant. MDSCs are increasingly recognized as predictive biomarkers for immunotherapy outcomes in NSCLC, with elevated frequencies correlating with poor responses to PD-1/PD-L1 blockade [ 48 , 49 ]. By demonstrating that SSG reduces both MDSC abundance and their suppressive mediators, our study suggests that SSG may not only enhance ICI efficacy but also support the use of MDSC levels as a predictive biomarker to guide patient stratification. This dual therapeutic and diagnostic potential reinforces the rationale for integrating herbal adjuvants such as SSG with immune checkpoint therapy in lung cancer. 5. Conclusion In conclusion, this study provides experimental evidence that Shuangshen granules (SSG) enhance antitumor immunity in a lung adenocarcinoma model by modulating the tumor microenvironment. SSG reduced the frequency and suppressive mediators of MDSCs, alleviated T cell exhaustion, and improved the efficacy of anti-PD-1 checkpoint therapy. These findings highlight the therapeutic potential of concurrently targeting myeloid immunosuppression and T cell dysfunction to overcome immunotherapy resistance. SSG represents a promising adjunctive strategy, though future studies are required to determine the active components, define safe and effective dosing regimens, and validate these immunological effects in clinical settings. Declarations Ethics approval and consent to participate All animal experiments were approved by the Animal Ethics Committee of Guang’anmen Hospital, China Academy of Chinese Medical Sciences (Approval No. IACUC-GAMH-2022-011). and carried out in accordance with the institutional guidelines. Competing interests The authors declare no competing interests. Funding This study was supported by the National Natural Science Foundation of China (No. 82205226,82174465 ), the Fundamental Research Funds for the Central Public Welfare Research Institutes (No. ZZ18-XRZ-028, ZZ17-XRZ-023), The Special Training of Scientific and Technological Talents, China Academy of Chinese Medical Sciences (No. ZZ13-YQ-028, ZZ13-YQ-023), Natural Science Foundation of Beijing Municipality (No. 7232310), Central High-level Hospital of Traditional Chinese Medicine Clinical Research and Achievement Transformation Capability Improvement Project (No. HLCMHPP2023101), Capital’s Funds for Health Improvement and Research (No. 2024-2-4153). Author Contribution All the listed authors contributed to the conception of the study and its design. Zhongning He、Qi Huang: Conceptualization, Methodology, Writing – original draft. Jiaqi Hu、Yi Li: Visualization, Investigation. Tongtong Liu: Conceptualization, Methodology. Yuwei Zhao: Visualization, Investigation. Xiaoling Ren: Data analysis, Resources. Shulin He: Experiment. Yue Li: Experiment. Rui Liu: Conceptualization, Methodology. Qiujun Guo: Data analysis. Xing Zhang: Resources. Bolun Shi: Experiment. Jie He: Experiment. Runzhi Qi: Methodology, Validation, Writing – review, and editing. Zhan Shi、Baojin Hua: Resources, Writing-Review and Editing. Data Availability The datasets used and/or investigated during the current study are available from the corresponding author upon reasonable request. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. https://doi.org/10.3322/caac.21834 . Zhou F, Qiao M, Zhou C. The cutting-edge progress of immune-checkpoint blockade in lung cancer. Cell Mol Immunol. 2021;18(2):279–93. https://doi.org/10.1038/s41423-020-00577-5 . 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20:16:46","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80294,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/b1150cb6a014430726166069.png"},{"id":93527877,"identity":"97b992b8-7bee-41d9-a94f-a4abda178025","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33136,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/743154e5d42dbaab03c987dc.png"},{"id":93527868,"identity":"54a2c3dd-c237-4682-9782-23e0df44f808","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71108,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/51fbdd1bf7a33ce6c153bbb5.png"},{"id":93527878,"identity":"66a07792-2ade-4fb4-b70b-91dfee680ced","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"xml","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174652,"visible":true,"origin":"","legend":"","description":"","filename":"3c6bfae2e3da4ed28ea2a68b2b9400741structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/e29b22a422f933110a6d7c41.xml"},{"id":93527881,"identity":"fabd8216-8053-423b-bc0d-83e9d9a86e7f","added_by":"auto","created_at":"2025-10-14 20:16:46","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184785,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/1c89aec1a3ff12601fff3c59.html"},{"id":93528365,"identity":"87321d99-fff0-4610-af80-4be23c108241","added_by":"auto","created_at":"2025-10-14 20:24:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":360077,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline of the animal experiments. (A) Timeline of the SSG dose-optimization experiment. (B) Timeline of the treatment schedule for control, SSG, anti-PD-1, and combination therapy groups in the main in vivo study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/0e3f09e0f53731f768d829b2.png"},{"id":93527838,"identity":"306dddb1-f679-4b33-93cc-24c47f32818d","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1109029,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork pharmacology analysis of SSG’s immune-related mechanisms. (A) Overlap between SSG compound targets, lung adenocarcinoma (LUAD) targets, and immune-related genes (Venn diagram). (B) Herb–component–target–disease interaction network for SSG. (C) Protein–protein interaction (PPI) network of common targets (minimum confidence score 0.7). (D) Top 20 KEGG pathways enriched by SSG targets. (E) GO enrichment results for SSG targets (biological process, molecular function, cellular component). (F) Molecular docking of key SSG compounds with core targets (illustration of one compound–target pair).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/b4bc9be247e4abf8d645e757.png"},{"id":93527840,"identity":"8f7a46e3-dcfe-4ae9-b99f-18502b48a596","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":569142,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different SSG doses on tumor growth and proliferation in LLC tumor-bearing mice. (A) Tumor volume growth curves for Control vs. low (L), medium (M), and high (H) SSG dose groups (P \u0026lt; 0.05, ***P \u0026lt; 0.0001 for SSG-H vs Control). (B) Photographs of excised tumors from each group at endpoint. (C) Tumor weights at endpoint for each group (*P \u0026lt; 0.01 for SSG-H vs Control). (D) Body weight curves of mice in each group (P \u0026lt; 0.05, *P \u0026lt; 0.01 for SSG groups vs Control at day 21). (E) H\u0026amp;E staining of tumor sections (representative images; high-dose SSG group shows fewer irregular tumor cells). (F) IHC staining of proliferation markers (CD34, Ki-67) in tumor tissues of each group, Scale bar, 50 μm (representative images; **P \u0026lt; 0.001, ***P \u0026lt; 0.0001 for high-dose SSG vs Control).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/2c9043ac17762036cacd4bd5.png"},{"id":93528675,"identity":"efa0869e-98fb-438d-b37b-580b33f4ff35","added_by":"auto","created_at":"2025-10-14 20:32:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":960945,"visible":true,"origin":"","legend":"\u003cp\u003eSSG boosts the anti-tumor efficacy of PD-1 blockade and reduces T cell exhaustion markers in vivo. (A) Tumor volume curves for Control, SSG, anti-PD-1, and SSG+anti-PD-1 combination groups (P \u0026lt; 0.05, *P \u0026lt; 0.01, **P \u0026lt; 0.001, ***P \u0026lt; 0.0001 for combination vs Control at day 21). (B) Images of tumors excised from each group at the end of treatment. (C) Tumor weights at endpoint for each group (P \u0026lt; 0.05, ***P \u0026lt; 0.0001 for combination vs Control). (D) Body weight changes over time (combination group shows a slight decrease relative to Control by day 21, P \u0026lt; 0.05). (E) H\u0026amp;E staining of tumor tissues from each group (combo group shows more uniform tumor cell morphology), Scale bar, 50 μm. (F) IHC staining of proliferation markers (CD34, Ki-67) and T cell exhaustion markers (PD-1, TIM-3, CTLA-4, LAG-3) in tumor tissues of each group, Scale bar, 50 μm (*P \u0026lt; 0.01, **P \u0026lt; 0.001, ***P \u0026lt; 0.0001 for combination vs Control).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/85a74307b98c73d829781b60.png"},{"id":93527845,"identity":"8a22fcc5-4467-444e-b026-f93821460cac","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":468771,"visible":true,"origin":"","legend":"\u003cp\u003eSSG reduces T cell exhaustion in vivo.\u003cstrong\u003e \u003c/strong\u003e(A) Proportion of CD8\u003csup\u003e+\u003c/sup\u003eT cells in spleens (flow cytometry; **P \u0026lt;\u0026nbsp;0.001, ***P \u0026lt;\u0026nbsp;0.0001 for SSG vs Control). (B) Mean fluorescence intensity (MFI) of PD-1 and TIM-3 on splenic CD8\u003csup\u003e+\u003c/sup\u003eT cells (*P \u0026lt;\u0026nbsp;0.01, **P \u0026lt;\u0026nbsp;0.001 for combination vs monotherapy). (C) Western blot analysis of exhaustion-related proteins (PD-1, TIM-3, CTLA-4, BTLA, LAG-3) in tumor-infiltrating T cells (representative blots, showing lower levels in SSG and combination groups). (D) Levels of IL-2, TNF-α, and IFN-γ in serum or spleen homogenates of each group (*P \u0026lt;\u0026nbsp;0.01, **P \u0026lt;\u0026nbsp;0.001 for SSG and combo vs Control).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/92001ed1ac60013af2af3f31.png"},{"id":93528370,"identity":"d414a38e-48d0-4806-95b5-7b43fe284441","added_by":"auto","created_at":"2025-10-14 20:24:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":636818,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eImmunofluorescence analysis of PD-1/PD-L1 in tumor tissues. (A) Representative confocal images of PD-1 (green) and CD3 (red) co-staining in tumors from each group (nuclei in blue). In the Control group, many CD3\u003csup\u003e+\u003c/sup\u003eT cells co-express PD-1 (yellow overlay), whereas in the combination group, far fewer PD-1\u003csup\u003e+\u003c/sup\u003eT cells are seen. (B) Representative images of PD-L1 (green) and CD11b (red) staining. PD-L1 is present in tumor tissues but shows minimal overlap with CD11b\u003csup\u003e+\u003c/sup\u003emyeloid cells, and overall PD-L1 signal is reduced in the SSG and combination groups, Scale bar, 50 μm.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/8ea2b1a41b5016a747b5d360.png"},{"id":93527858,"identity":"2102ddd1-c4f4-436d-9603-a2de89ded186","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":517910,"visible":true,"origin":"","legend":"\u003cp\u003eSSG inhibits MDSC accumulation and function in vivo. (A) Frequency of MDSCs (CD11b\u003csup\u003e+\u003c/sup\u003eGr-1\u003csup\u003e+\u003c/sup\u003ecells) in spleens of each group, measured by flow cytometry (P \u0026lt;\u0026nbsp;0.05, *P \u0026lt;\u0026nbsp;0.01 vs Control). (B) Expression of PD-L1 and Gal-9 on splenic MDSCs (MFI values); *P \u0026lt;\u0026nbsp;0.01, **P \u0026lt;\u0026nbsp;0.001, ***P \u0026lt;\u0026nbsp;0.0001 vs Control, showing significant reductions in combination group. (C) Western blot of MDSC-related enzymes (Arg-1, IDO, iNOS) in tumor tissue; SSG, especially with anti-PD-1, greatly reduces these proteins. (D) Western blot of exhaustion ligands (PD-L1, Gal-9) in tumor tissues of each group, showing lowest levels in SSG+anti-PD-1 group. (E) IL-10 and TGF-β concentrations in serum (or tumor microenvironment) of each group, measured by ELISA (P \u0026lt;\u0026nbsp;0.05, *P \u0026lt;\u0026nbsp;0.01, ***P \u0026lt;\u0026nbsp;0.0001 vs Control).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/6864f14917ac36ecf4662b91.png"},{"id":93528375,"identity":"52834f99-9e7a-474f-9763-bdb05042968e","added_by":"auto","created_at":"2025-10-14 20:24:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":216102,"visible":true,"origin":"","legend":"\u003cp\u003eMRM chromatograms of blank serum vs. SSG-containing serum. (A) Chromatogram for ginsenoside Rg1; (B) Rb1; (C) Rd; (D) notoginsenoside R1. These profiles confirm the presence of SSG’s major ginsenosides in the serum of treated rats (peaks indicated by arrows).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/0c7422185e6f31f7c0755fe6.png"},{"id":93527863,"identity":"fca0e80d-b325-40ef-a166-befacedab60e","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":502467,"visible":true,"origin":"","legend":"\u003cp\u003eSSG-containing serum reduces the immunosuppressive activity of MDSCs in vitro. (A) Flow cytometry dot plots showing the percentage of MDSCs (CD11b\u003csup\u003e+\u003c/sup\u003eGr-1\u003csup\u003e+\u003c/sup\u003e) before and after expansion, confirming \u0026gt;95% purity. (B) Quantified expression of Arg-1, IDO, and iNOS in MDSCs (by Western blot or flow cytometry) with and without SSG serum treatment; SSG significantly decreases all three (P \u0026lt;\u0026nbsp;0.05, *P \u0026lt;\u0026nbsp;0.01). (C) Surface levels of PD-L1 and Gal-9 on MDSCs after treatment (flow cytometry MFI), *P \u0026lt;\u0026nbsp;0.01, ***P \u0026lt;\u0026nbsp;0.0001 vs untreated MDSCs. (D) Secretion of IL-10 and TGF-β by MDSCs, measured by ELISA; ***P \u0026lt;\u0026nbsp;0.0001 vs untreated. (E) Western blot confirming reduced PD-L1 and Gal-9 protein in MDSCs treated with SSG serum (representative blots).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/4b062cf8f04b79d5a4a37def.png"},{"id":93528371,"identity":"7ce05da0-a4f5-4b5e-b33b-757ff309d29b","added_by":"auto","created_at":"2025-10-14 20:24:45","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":637826,"visible":true,"origin":"","legend":"\u003cp\u003eSSG-treated MDSCs alleviate T cell exhaustion in co-culture. (A) Flow cytometry sorting of CD8\u003csup\u003e+\u003c/sup\u003eT cells from mouse spleens (pre-sort and post-sort purity plots). (B) Expression of PD-1 and TIM-3 on CD8\u003csup\u003e+\u003c/sup\u003eT cells after co-culture with MDSCs ± SSG treatment (P \u0026lt;\u0026nbsp;0.05, *P \u0026lt;\u0026nbsp;0.01 vs co-culture with untreated MDSCs). (C) Western blot analysis of CTLA-4, BTLA, LAG-3 in CD8\u003csup\u003e+\u003c/sup\u003eT cells after co-culture, showing lower levels when co-cultured with SSG-treated MDSCs. (D) Levels of IL-2, IFN-γ, and TNF-α in co-culture supernatants, reflecting enhanced cytokine production in the presence of SSG-treated MDSCs. (E) Apoptosis rates of CD8\u003csup\u003e+\u003c/sup\u003eT cells after 24\u0026nbsp;h co-culture, with significantly fewer Annexin\u0026nbsp;V\u003csup\u003e+\u003c/sup\u003ecells when MDSCs were pretreated with SSG (*P \u0026lt;\u0026nbsp;0.01). (F) Proliferation of CD8\u003csup\u003e+\u003c/sup\u003eT cells (CFSE dilution after 72\u0026nbsp;h co-culture), showing increased proliferation index with SSG-treated MDSCs (**P \u0026lt;\u0026nbsp;0.001).\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/0c6c2f2bd07d881720106772.png"},{"id":93528377,"identity":"1286f84e-9af4-4990-a326-1866ab968933","added_by":"auto","created_at":"2025-10-14 20:24:45","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":6441887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell RNA sequencing analysis of T cells in tumor tissues\u003c/strong\u003e. (A) UMAP plot showing distinct T-cell clusters identified from pooled samples. (B) Annotation of major T-cell subsets, including CD4⁺ T cells, CD8⁺ T cells, memory T cells, and regulatory T cells. (C) Distribution of T-cell clusters stratified by experimental groups (Model vs. SSG). (D) Heatmap of immune checkpoint molecules (Pdcd1, Ctla4, Havcr2, Lag3, and Tigit) across T-cell clusters. (E) UMAP visualization of cells positive for checkpoint molecules (PD-1, CTLA-4, TIM-3, LAG-3, TIGIT). (F) Group-specific distribution of checkpoint-positive T cells. (G) Quantitative comparison of the proportion of checkpoint-positive CD8⁺ T cells between Model and SSG groups. Data demonstrate increased CTLA-4⁺, TIM-3⁺, LAG-3⁺, and TIGIT⁺ CD8⁺ T cells in the Model group, whereas SSG treatment reduced exhausted phenotypes.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/85adc4ec28ca8f30298a42f1.png"},{"id":93529205,"identity":"264d53c5-70fe-40db-9779-3495c500ccb0","added_by":"auto","created_at":"2025-10-14 20:48:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14303430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/b082a9cc-d985-4452-b120-5b96198d3302.pdf"},{"id":93527837,"identity":"7a937fb4-ac65-4d1d-87d4-5448290f4905","added_by":"auto","created_at":"2025-10-14 20:16:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":63553,"visible":true,"origin":"","legend":"","description":"","filename":"SupplentaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7686641/v1/2fcb2d8edcd1dea25e221c05.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shuangshen granules enhance anti-PD1 therapy efficacy in lung adenocarcinoma by modulating myeloid-derived suppressor cell-induced T cell exhaustion","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer remains the most commonly diagnosed malignancy and the leading cause of cancer-related death worldwide. In 2022, it accounted for nearly 2.5\u0026nbsp;million new cases and more than 1.8\u0026nbsp;million deaths, representing approximately one in eight of all cancer diagnoses (12.4%) and almost one in five cancer deaths (18.7%) globally. Despite advances in surgery, radiotherapy, chemotherapy, and targeted therapies, the overall prognosis of lung cancer patients remains poor, underscoring the urgent need for more effective treatment strategies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent years, immune checkpoint inhibitors (ICIs), particularly antibodies targeting the programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) axis, have revolutionized the treatment landscape of advanced non-small cell lung cancer (NSCLC) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These agents can restore antitumor T cell activity and prolong survival in a subset of patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the clinical benefit of PD-1/PD-L1 blockade is limited, as only a fraction of patients achieves durable responses. A major obstacle to immunotherapy efficacy is the immunosuppressive tumor microenvironment (TME), which promotes T cell dysfunction and exhaustion [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eT cell exhaustion is a state of T cell dysfunction that arises during chronic infection and tumor progression. It is characterized by the upregulation and co-expression of multiple inhibitory receptors and a concomitant reduction in the cytotoxic activity of effector T cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This exhausted state weakens the anti-tumor immune function of T cells and also limits the effectiveness of ICIs and other immunotherapies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Among the key regulators of this immunosuppressive milieu are myeloid-derived suppressor cells (MDSCs), a heterogeneous population of immature myeloid cells that expand in cancer and inhibit antitumor immunity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. MDSCs suppress CD8⁺ T cell function through the expression of arginase-1, inducible nitric oxide synthase, and immunoregulatory cytokines, while also driving the upregulation of multiple exhaustion markers, including PD-1, TIM-3, and LAG-3. This culminates in the impaired function of tissue-resident memory T cells (TRMs), which are critical for sustained local immune surveillance and antitumor responses [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, targeting MDSCs within the immunosuppressive tumor microenvironment is considered a promising approach to reverse T cell exhaustion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTraditional Chinese medicine (TCM) has long been used as an adjuvant approach in oncology, and accumulating evidence suggests that specific herbal formulations may modulate immune responses and improve therapeutic outcomes due to its complex composition and multi-target mechanisms. Feiyuping ointment, which contains Shuangshen granules (SSG) as the primary component, is commonly used in clinical practice for NSCLC patients and has shown promise as an effective TCM therapy. Studies have demonstrated that SSG can significantly improve quality of life and extend survival in patients with NSCLC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. SSG is a formulation developed at Guang\u0026rsquo;anmen Hospital (patent No. 201310091864.4) composed of Panax quinquefolium L., Panax notoginseng, and Cordyceps sinensis, and it has undergone extensive pharmacological and toxicological testing. For nearly a decade, SSG has shown notable therapeutic benefits in the clinical management of lung adenocarcinoma. Previous research suggests that SSG inhibits the differentiation of myeloid cells into MDSCs, thereby limiting lung cancer metastasis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It has shown potential in restoring immune balance and enhancing the efficacy of conventional therapies. Nevertheless, its precise effects on MDSC-mediated immunosuppression and TRM cell exhaustion in the context of PD-1 blockade remain unclear.\u003c/p\u003e\u003cp\u003eTherefore, in this study, we investigated the impact of SSG on tumor growth, immune cell composition, and functional cytokine expression in a murine model of lung adenocarcinoma. We further explored the potential of SSG to alleviate MDSC-induced T cell exhaustion and augment the efficacy of anti-PD-1 therapy, providing new mechanistic insights and translational implications for combining TCM with modern immunotherapy.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Materials, reagents, and cell lines\u003c/h2\u003e\u003cp\u003ePanax quinquefolium L. (Batch No. 23011602), Panax notoginseng (Batch No. 21030903), and Cordyceps sinensis (Batch No. 230360511) were obtained from Guang\u0026rsquo;anmen Hospital of the China Academy of Chinese Medical Sciences (Beijing, China) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details). Ginsenoside Rg1 (No. 110703\u0026ndash;202235), ginsenoside Rb1 (No. 110704\u0026ndash;202331), ginsenoside Rd (No. 111818\u0026ndash;202104), and notoginsenoside R1 (No. 110745\u0026ndash;202322) were obtained from the China Institute of Food and Drug Control (Beijing, China). RIPA lysis buffer was from Promega (Madison, WI, USA), RPMI-1640 medium was from Solarbio (Beijing, China), and DMEM and fetal bovine serum (FBS) were from HyClone (Logan, UT, USA).\u003c/p\u003e\u003cp\u003ePrimary antibodies against Arg-1 (arginase-1, #93668), IDO (#51851), PD-L1 (#64988), TIM-3 (#83882), CTLA-4 (#53560), LAG-3 (#80282), and β-actin (#3700) were purchased from Cell Signaling Technology (CST, Boston, MA, USA). Antibodies against iNOS (ab178945), Galectin-9 (Gal-9, ab69630), PD-1 (ab214421), and BTLA (ab216505) were from Abcam (Cambridge, UK). An anti-mouse PD-1 monoclonal antibody (clone RMP1-14, BE0273) for in vivo use was obtained from BioXcell (West Lebanon, NH, USA). Fluorophore-conjugated antibodies for flow cytometry were purchased from BioLegend (San Diego, CA, USA), including FITC\u0026ndash;anti-CD11b (Cat# 101205), PerCP/Cyanine5.5\u0026ndash;anti-Gr-1 (Cat#108425), PE\u0026ndash;anti-PD-L1 (Cat#155403), PE/Cyanine7\u0026ndash;anti-Gal-9 (Cat#137913), FITC\u0026ndash;anti-CD3 (Cat#100203), PerCP/Cyanine5.5\u0026ndash;anti-CD8a (Cat#100733), PE\u0026ndash;anti-PD-1 (Cat#114117), and Brilliant Violet 605\u0026ndash;anti-TIM-3 (Cat# 119721). ELISA kits for IL-10, TNF-α, IL-2, IFN-γ, and TGF-β were from NeoBioscience (Shenzhen, China). Recombinant GM-CSF (Cat#315-03) and IL-6 (Cat#216\u0026thinsp;\u0026minus;\u0026thinsp;16) were obtained from PeproTech (Rocky Hill, NJ, USA). The Lewis lung carcinoma (LLC) cell line was obtained from the National Infrastructure of Cell Line Resource (Beijing, China) and maintained in DMEM supplemented with 10% FBS at 37\u0026deg;C in a humidified 5% CO\u003csub\u003e2\u003c/sub\u003e incubator.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe information pertaining to the Chinese medicines found in the SSG-derived herb\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChinese Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLatin name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFamily\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePlace of origin (province)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUsed part\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMajor effective compound in modern pharmacology study\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXi Yang Shen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePanax quinquefolium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAraliaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJi Lin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRhizome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGinsenosides\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSan Qi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePanax notoginseng\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAraliaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYun Nan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRhizome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNotoginsenosides\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDong Chong Xia Cao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCordyceps sinensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClavicipitaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTibet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStroma\u0026ndash;larva complex\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNucleosides, polysaccharides, and cordycepin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e The dried complex of the stromata of the fungus and the dead bodies of the larvae of insects in the family Bat Moths\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 \u003cb\u003eNetwork pharmacological analysis of SSG\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eActive compounds in SSG were identified from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) based on criteria of oral bioavailability (OB)\u0026thinsp;\u0026ge;\u0026thinsp;30% and drug-likeness (DL)\u0026thinsp;\u0026ge;\u0026thinsp;0.18 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Predicted targets of these compounds were intersected with immune-related genes (from the ImmPort database) and lung adenocarcinoma\u0026ndash;related genes (from the GeneCards database) to identify common targets. (Lung adenocarcinoma targets from GeneCards were filtered by relevance score using two rounds of median cutoff to improve reliability.) The overlapping targets among SSG compounds, immune-related genes, and LUAD-related genes were determined using Venny 2.1.0 and were subsequently used to construct a drug\u0026ndash;target protein-protein interaction (PPI) network via the STRING database (v12.0). A compound\u0026ndash;target\u0026ndash;disease network was visualized using Cytoscape v3.9.1. Functional enrichment analysis of the common targets was performed using Metascape (for Homo sapiens) with the criteria of a minimum count of 3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and an enrichment factor\u0026thinsp;\u0026gt;\u0026thinsp;1.5. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to elucidate the biological functions and pathways associated with the targets [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 \u003cb\u003eMolecular docking study\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eMolecular docking was carried out using AutoDock Vina v1.5.6 to evaluate potential interactions between key active SSG compounds and high-priority protein targets identified from the network analysis. The crystal structures of target proteins were obtained from the Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www1.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www1.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and prepared by removing water molecules using PyMOL. Chemical structures of the major active components were sketched and optimized to low-energy three-dimensional conformations using ChemDraw. Each compound was then docked to its respective target protein using AutoDock Vina, and the binding pose with the lowest binding free energy was recorded for analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 \u003cb\u003eEstablishment of the tumor-bearing mouse model and SSG treatment\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eSSG was prepared from authenticated raw herbal materials (Panax quinquefolius, Panax notoginseng, and Cordyceps sinensis) by pulverizing the herbs and mixing them in a 1:6:1 (w/w/w) ratio. Quality control for the raw herbs and the final formulation followed protocols from our previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Male C57BL/6J mice (4\u0026ndash;5 weeks old) were acclimated for 7 days prior to tumor implantation. To establish the tumor model, LLC cells (5\u0026times;10\u003csup\u003e^\u003c/sup\u003e6 in 100 \u0026micro;L PBS) were injected subcutaneously into the left flank of each mouse [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To determine the optimal SSG dosage, tumor-bearing mice were randomly divided into four groups (n\u0026thinsp;=\u0026thinsp;5 per group): Control, low-dose SSG (SSG-L), medium-dose SSG (SSG-M), and high-dose SSG (SSG-H). The SSG-L dose was based on the human equivalent dose of the herbal formula (~\u0026thinsp;6 g per 70 kg adult), which corresponds to approximately 18.0 mg/day for a\u0026thinsp;~\u0026thinsp;20 g mouse using a body surface area conversion factor (mouse:human ratio of 9.01). SSG-M and SSG-H were set at two times (36.0 mg/day) and three times (54.0 mg/day) the SSG-L dose, respectively. SSG was administered orally once daily for 21 days, while the Control group received an equal volume of water. After 21 days, the mice were euthanized and tumor tissues were excised for analysis by H\u0026amp;E staining and IHC. Based on the extent of tumor inhibition observed, the high dose of SSG was selected as the optimal dose for subsequent experiments.\u003c/p\u003e\u003cp\u003eFor the combination therapy experiment, another set of LLC tumor-bearing mice were randomly assigned to four groups (n\u0026thinsp;=\u0026thinsp;8 per group): Control, SSG alone, anti-PD-1 alone, and SSG\u0026thinsp;+\u0026thinsp;anti-PD-1. Mice in the SSG group received the optimal dose of SSG orally each day for 21 days. Mice in the anti-PD-1 group received intraperitoneal injections of anti-mouse PD-1 antibody (200 \u0026micro;g per mouse, every other day) starting on day 7 post-tumor inoculation, for a total of 7 injections (14 days). Control group mice received oral water and i.p. saline on matched schedules. On day 21, all mice were euthanized, and tumor tissues, peripheral blood, and spleens were collected for further analysis. All animal procedures were approved by the Animal Ethics Committee of Guang\u0026rsquo;anmen Hospital, China Academy of Chinese Medical Sciences (Approval No. IACUC-GAMH-2022-011). The experimental timeline is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. HE and IHC\u003c/h2\u003e\u003cp\u003eExcised tumor tissues were fixed in 4% neutral buffered formalin, embedded in paraffin, and sectioned into 4\u0026ndash;6 \u0026micro;m slices. For H\u0026amp;E staining, sections were deparaffinized, rehydrated, and stained with hematoxylin followed by eosin. For IHC, tissue sections underwent antigen retrieval in sodium citrate buffer (pH 6.0) and quenching of endogenous peroxidase activity with 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in methanol. Sections were then blocked with 5% BSA and incubated with primary antibodies overnight at 4\u0026deg;C. After washing, HRP-conjugated secondary antibodies were applied, and immunoreactivity was visualized using a DAB chromogen. Sections were counterstained (with hematoxylin), mounted with neutral resin, and examined under a light microscope for imaging and scoring.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Immunofluorescence\u003c/h2\u003e\u003cp\u003eParaffin-embedded tumor sections were deparaffinized and subjected to antigen retrieval as above. After blocking with 5% BSA for 1 h at room temperature, sections were incubated with primary antibodies against CD3, PD-1, CD11b, or PD-L1. Subsequently, sections were incubated with species-appropriate Alexa Fluor\u0026ndash;conjugated secondary antibodies. Nuclei were counterstained with DAPI. Stained sections were observed and imaged using a laser scanning confocal microscope (Leica Microsystems, Wetzlar, Germany).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Flow cytometry\u003c/h2\u003e\u003cp\u003eSingle-cell suspensions from mouse spleens or cultured cells were prepared and washed with PBS. Cells were first incubated with an anti-mouse CD16/32 Fc-blocker for 15 min on ice to prevent nonspecific antibody binding. Two antibody staining panels were then applied: one panel (for myeloid-derived cells) included FITC-anti-CD11b, PerCP/Cyanine5.5-anti-Gr-1, PE-anti-PD-L1, and PE/Cyanine7-anti-Gal-9; the other panel (for T cells) included FITC-anti-CD3, PerCP/Cyanine5.5-anti-CD8a, PE-anti-PD-1, and BV605-anti-TIM-3. Staining was performed in the dark at 4\u0026deg;C for 25 min. After staining, cells were washed (1500 rpm, 5 min) and resuspended in FACS buffer (PBS with 2% FBS). Data acquisition was done on a BD LSR II flow cytometer (BD Biosciences), and the data were analyzed using FlowJo v10 software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Western blotting\u003c/h2\u003e\u003cp\u003eTumor tissue samples or cultured cells were lysed in ice-cold RIPA buffer containing protease and phosphatase inhibitors (Topscience, Shanghai, China) for 30 min. Lysates were clarified by centrifugation at 12,000 rpm for 15 min at 4\u0026deg;C, and supernatants were collected. Equal amounts of protein (determined by BCA assay) were separated by SDS-PAGE and transferred onto PVDF membranes. Membranes were blocked with 5% BSA in TBST for 1 h at room temperature and then incubated with primary antibodies at 4\u0026deg;C overnight. After washing, membranes were probed with HRP-conjugated secondary antibodies (ZSGB-Bio, Beijing, China) for 1 h at room temperature. Immunoreactive bands were detected using enhanced chemiluminescence (ECL) reagents and imaged. Band intensities were quantified using ImageJ software (v1.53a).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Enzyme-linked immunosorbent assay\u003c/h2\u003e\u003cp\u003ePeripheral blood samples and cell culture supernatants were centrifuged at 12,000 rpm for 20 min at 4\u0026deg;C to obtain cell-free supernatants. The concentrations of IL-10 and TGF-β in these supernatants were measured using specific ELISA kits according to the manufacturers\u0026rsquo; protocols. Absorbance at the appropriate wavelength was read using a GloMax Discover microplate reader (Promega, Madison, WI, USA), and cytokine concentrations were calculated from standard curves.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10. Reverse transcription-quantitative polymerase chain reaction\u003c/h2\u003e\u003cp\u003eTotal ribonucleic acid (RNA) was extracted from mouse tumor tissues using a Rapid RNA Purification Kit (ES Science, Shanghai, China). cDNA was synthesized with the RevertAid reverse transcription kit (Yeasen, Shanghai, China), followed by reverse transcription-quantitative polymerase chain reaction on an applied biosystems (Bio-Rad T100, USA) to quantify IL-2, IFN-γ, and TNF-α mRNA levels. Gene expression was normalized using GAPDH as a stably expressed reference gene. Triplicate experiments ensured reproducibility, and primers were designed via primer-basic local alignment search tool (National Center for Biotechnology Information, National Institutes of Health, USA) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer sequences for reverse transcription-quantitative polymerase chain reaction.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimer sequence (5\u0026rsquo;\u0026ndash;3\u0026rsquo;)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIL-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGCCAGCTGTGAGTGTTTCTTTGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTCGCTTCATCTTCCCTCTTGGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIFN-γ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTGAACGCTACACACTGCATCTTGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTCCTTTTCCGCTTCCTGAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTNF-α\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCCCTCACACTCAGATCATCTTCT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCTACGACGTGGGCTACAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGAPDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGTGGACCTGACCTGCCGTCT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGAGGAGTGGGTGTCGCTGT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11. Preparation of SSG drug-containing serum\u003c/h2\u003e\u003cp\u003eMale Wistar rats with initial body weights of (180\u0026ndash;200) g received SSG extract (62.5 mg/kg, intragastrically, BID) for 7 days. This dose was based on HED conversion from a 6 g/70 kg human dose (rat:human ratio 6.25). Two hours after the final administration, the blood was collected from the aorta and refrigerated overnight at 4\u0026deg;C. Following centrifugation at 3,000 rpm for 15 min, sera were collected and stored at -20\u0026deg;C. The experimental protocol was approved by the Animal Welfare and Ethics Committee of Guang\u0026rsquo;anmen Hospital, China Academy of Chinese Medical Sciences and conducted in compliance with the International Council for Laboratory Animal Science guidelines (approval No. IACUC-GAMH-2024-004-SQ).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12. Component analysis for SSG drug-containing serum\u003c/h2\u003e\u003cp\u003eThe major chemical constituents in SSG-containing serum were analyzed using high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). The analysis was performed on an ExionLC-20AD HPLC system coupled to an AB Sciex mass spectrometer, following a previously described protocol [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Optimized multiple reaction monitoring (MRM) parameters for each ginsenoside are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Prior to analysis, 100 \u0026micro;L of serum was mixed with 300 \u0026micro;L of methanol\u0026ndash;acetonitrile (4:1, v/v), vortexed at 6000 rpm for 5 min, and then centrifuged at 20,000 \u0026times; g for 10 min at 4\u0026deg;C. The supernatant was collected and evaporated to dryness under a stream of nitrogen. The residue was re-dissolved in methanol to a concentration of 10 mg/mL. A stock solution of mixed standard compounds (1 mg/mL each in methanol) was prepared and serially diluted to generate calibration curves. LC-MS/MS data were acquired in both positive and negative ion modes over an m/z range of 30\u0026ndash;1300, with a scan time of 0.4 s. Data acquisition and analysis were performed using MassLynx v4.1 (Waters Corp. Milford, MA, USA) and its Elemental Composition tool (Waters).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe optimized HPLC-MS parameters of each component\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantitative ion pair\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDwell time /ms\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCollision energy /eV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDeclustering Potential /V\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRetention /min\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rg1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e823.2/643.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rb1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1131.7/789.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e969.7/789.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNotoginsenoside R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e955.3/775.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13. Isolation of MDSCs and CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003c/h2\u003e\u003cp\u003eBone marrow cells were harvested from the femurs and tibias of 4\u0026ndash;5-week-old C57BL/6J mice. After red blood cell lysis and washing with PBS, the bone marrow cells were cultured in RPMI-1640 medium containing 10% FBS and supplemented with 100 ng/mL GM-CSF and 100 ng/mL IL-6 to induce the differentiation and expansion of MDSCs. After an incubation period (allowing sufficient MDSC generation), cells were collected and adjusted to 5 \u0026times; 10^6 cells/mL. The cells were labeled with anti-CD11b and anti-Gr-1 antibodies and analyzed by flow cytometry to confirm the successful enrichment of MDSCs. The Gr-1 antibody recognizes epitopes on both Ly6G and Ly6C, thereby labeling the entire MDSC population [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The MDSC-enriched cells were then treated with culture medium containing SSG-containing rat serum for 24 h. After treatment, the MDSCs and their culture supernatants were collected for analysis (flow cytometry, Western blotting, and ELISAs) to evaluate changes in their immunosuppressive features.\u003c/p\u003e\u003cp\u003eFor CD8\u003csup\u003e+\u003c/sup\u003e T cell isolation, spleens were collected from 4\u0026ndash;5-week-old C57BL/6J mice. Single-cell suspensions were prepared by grinding spleen tissues through 70 \u0026micro;m cell strainers. After red cell lysis, CD8\u003csup\u003e+\u003c/sup\u003e T cells were isolated using a magnetic CD8a\u003csup\u003e+\u003c/sup\u003e T Cell Isolation Kit (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer\u0026rsquo;s instructions. Briefly, splenocytes were incubated with anti-CD8a magnetic microbeads and passed through MS columns in a magnetic field; the enriched CD8\u003csup\u003e+\u003c/sup\u003eT cells were then eluted and verified by flow cytometry for purity. MDSCs and CD8\u003csup\u003e+\u003c/sup\u003eT cells (both pretreated with SSG-containing serum as described above) were co-cultured using a Transwell system to assess functional interactions. CD8\u003csup\u003e+\u003c/sup\u003eT cells were placed in the lower chamber of a Transwell plate, and MDSCs were added to the upper insert (pore size\u0026thinsp;~\u0026thinsp;0.4 \u0026micro;m, which permits exchange of factors but prevents cell migration). The two cell populations were co-cultured for the durations specified in subsequent assays. After co-culture, cells (and conditioned media) were collected for flow cytometry, Western blotting, and ELISA analyses, as described earlier.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14. CFSE proliferation assay\u003c/h2\u003e\u003cp\u003eProliferation of CD8\u003csup\u003e+\u003c/sup\u003eT cells was assessed using a CFSE dilution method. Isolated CD8\u003csup\u003e+\u003c/sup\u003eT cells were labeled with 5 \u0026micro;M CFSE (Carboxyfluorescein diacetate succinimidyl ester; BD Biosciences) in the dark at 37\u0026deg;C for 15 min. Staining was quenched by adding five volumes of cold RPMI-1640 medium. The cells were washed and then resuspended in RPMI-1640 complete medium containing 200 ng/mL IL-2, 10 \u0026micro;g/mL anti-CD3ε, and 10 \u0026micro;g/mL anti-CD28 to provide costimulatory signals. CFSE-labeled CD8\u003csup\u003e+\u003c/sup\u003eT cells (5 \u0026times; 10^5) were co-cultured with MDSCs (also pretreated with SSG serum) at a 1:1 ratio in 96-well plates for 72 h. After 72 h, cells were harvested and the dilution of CFSE intensity in CD8\u003csup\u003e+\u003c/sup\u003eT cells was measured by flow cytometry to determine T cell proliferation (a decrease in CFSE fluorescence indicates cell division).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15. Apoptosis assay\u003c/h2\u003e\u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cell apoptosis was evaluated after co-culture with MDSCs using an Annexin V-FITC/PI apoptosis detection kit (BD Biosciences, Cat# 556547). CD8\u003csup\u003e+\u003c/sup\u003eT cells and MDSCs were co-cultured for 24 h in a Transwell setup (CD8\u003csup\u003e+\u003c/sup\u003eT cells in the lower well, MDSCs in the upper insert). After 24 h, CD8\u003csup\u003e+\u003c/sup\u003eT cells were collected, washed with cold PBS, and resuspended in 500 \u0026micro;L of binding buffer. The cells were then stained with 5 \u0026micro;L of Annexin V\u0026ndash;FITC and 5 \u0026micro;L of propidium iodide (PI) and incubated in the dark for 30 min at room temperature. Early and late apoptosis of CD8\u003csup\u003e+\u003c/sup\u003eT cells were analyzed by flow cytometry, with Annexin V\u003csup\u003e+\u003c/sup\u003ePI\u003csup\u003e\u0026minus;\u003c/sup\u003ecells considered early apoptotic and Annexin V\u003csup\u003e+\u003c/sup\u003ePI\u003csup\u003e+\u003c/sup\u003ecells considered late apoptotic.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.16. Single-cell RNA sequencing and data preprocessing\u003c/h2\u003e\u003cp\u003eFresh tumor tissues from each group were dissociated into single-cell suspensions using enzymatic digestion according to the manufacturer\u0026rsquo;s protocol (Miltenyi Biotec, Germany). Viable single cells were counted and loaded onto the 10x Genomics Chromium platform (10x Genomics, Pleasanton, CA, USA) to generate single-cell gel beads-in-emulsion (GEMs). Libraries were constructed using the Chromium Single Cell 3\u0026prime; Reagent Kit (v3.1) and sequenced on the Illumina NovaSeq 6000 platform with paired-end reads. Raw sequencing data were processed with the Cell Ranger pipeline (v6.0, 10x Genomics) for demultiplexing, alignment to the mouse reference genome (mm10), and gene expression quantification. Cells with fewer than 200 detected genes or with \u0026gt;\u0026thinsp;10% mitochondrial transcripts were excluded. Genes expressed in fewer than three cells were filtered out. Quality-controlled expression matrices were analyzed using the Seurat R package (v4.3.0). Normalization was performed by the \u0026ldquo;LogNormalize\u0026rdquo; method, and highly variable genes were identified. Principal component analysis (PCA) was conducted, followed by Uniform Manifold Approximation and Projection (UMAP) for visualization. Cell clusters were identified using the Louvain algorithm with a resolution parameter of 0.6.\u003c/p\u003e\u003cp\u003eCluster annotation was based on canonical marker genes for T-cell subtypes, including Cd4, Cd8a, and Treg. Differentially expressed genes (DEGs) between clusters and groups were identified using the Wilcoxon rank-sum test, with adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. Expression of immune checkpoint molecules Pdcd1 PD-1, CTLA-4, TIM-3, LAG-3, and TIGIT was evaluated across T-cell subsets. Proportions of checkpoint-positive CD8\u003csup\u003e+\u003c/sup\u003eT cells were quantified and compared between groups. Visualization was performed with heatmaps, feature plots, and UMAP distribution plots using Seurat and ggplot2 packages.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.17. Statistical analysis\u003c/h2\u003e\u003cp\u003eNormality and variance homogeneity were assessed prior to analysis. For normally distributed parameters with equal variances, data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and analyzed by parametric one-way analysis of variance (ANOVA), with post-hoc comparisons adjusted via False Discovery Rate test. When normality or variance assumptions were violated, non-normally distributed parameters are presented as median (interquartile range) and analyzed using the Kruskal-Wallis test (non-parametric equivalent of ANOVA), followed by Dunn's post-hoc comparisons. All statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software, USA), with statistical significance defined as a two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cb\u003e3.1. Potential therapeutic targets of SSG for LUAD, PPI, enrichment analysis, network diagram construction and Molecular docking\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 26 active ingredients of SSG were identified for network pharmacology analysis, including 7 compounds from Cordyceps sinensis and 8 from Panax notoginseng. According to TCMSP data, 19 of these 26 ingredients have reported potential anticancer properties. Using Venny 2.1.0, we identified 37 common gene targets that overlapped among SSG\u0026rsquo;s active compounds, immune-related genes, and lung adenocarcinoma\u0026ndash;related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). On the basis of the discussion on the ref erences information and network analysis conclusions, beta-sitosterol, papaverine, Daidzein-7-O-β-D-glucoside, Stigmasterol and quercetin were chosen as the core ingredients in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. A herb\u0026ndash;compound\u0026ndash;target\u0026ndash;disease network was constructed to visualize these interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), where nodes represent herbs, chemical components, targets, or diseases, and edges indicate their relationships. Network topology analysis (node degree) highlighted the most influential compounds and targets in this network.\u003c/p\u003e\u003cp\u003eIn the protein\u0026ndash;protein interaction (PPI) network of SSG targets, we applied a minimum confidence score of 0.7 to filter meaningful interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This analysis identified several hub targets, including IL-6, IL-1β, TNF, EGFR, and JUN, which may play central roles in SSG\u0026rsquo;s pharmacological effects. Pathway enrichment results further underscored the immune relevance of these targets. The top 20 KEGG pathways enriched by the common targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) were predominantly immune-related, suggesting that SSG\u0026rsquo;s effects are strongly tied to immune modulation and checkpoint pathways. GO enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) indicated that the targets are involved in numerous biological processes and molecular functions, such as regulation of inflammatory responses, nuclear receptor activity, signaling receptor activity, and cytokine activity. Notably, many of these pathways are critically involved in controlling MDSC differentiation and function, as well as T cell exhaustion in the tumor microenvironment.\u003c/p\u003e\u003cp\u003eMolecular docking simulations provided additional support for these network findings. Key active compounds of SSG (e.g., quercetin, luteolin, kaempferol, isorhamnetin) were docked with representative hub targets (TNF, IL-6, IL-1β, EGFR). The docking results showed favorable binding interactions; for instance, quercetin was predicted to bind strongly to IL-1β (estimated binding free energy \u0026asymp; \u0026minus;\u0026thinsp;8.0 kcal/mol) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). A heatmap of docking affinities (see Supplementary Figure) indicated generally strong binding between the major SSG compounds and the core targets identified, lending credence to the importance of these compound\u0026ndash;target interactions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe core compounds of SSG against LUAD ranked top 5\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecompounds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHerbs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOB(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebeta-sitosterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eXi Yang Shen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL006980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epapaverine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eXi Yang Shen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL001792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaidzein-7-O-β-D-glucoside\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSan Qi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStigmasterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSan Qi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOL000098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003equercetin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSan Qi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.2. SSG inhibits tumor growth in a xenograft mouse model\u003c/h2\u003e\u003cp\u003eWe first evaluated the effect of different SSG doses on tumor growth using the LLC xenograft mouse model. SSG treatment significantly suppressed tumor growth in a dose-dependent fashion, with higher doses of SSG producing more pronounced tumor inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;C). Throughout the 21-day treatment, SSG was well tolerated: all groups of mice showed stable body weights with no significant differences among the control and SSG-treated groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Histological examination of tumor tissues revealed that higher doses of SSG led to a noticeable reduction in the proportion of abnormally shaped (irregular) tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Consistently, IHC staining showed that tumors from the high-dose SSG group had markedly lower expression of proliferation markers (CD34 and Ki-67) compared to tumors from control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These findings demonstrate that SSG exerts antitumor effects in vivo, and that a high dose of SSG is most effective at suppressing tumor growth and tumor cell proliferation. Accordingly, we selected the high-dose SSG regimen for subsequent experiments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.3. SSG enhances the efficacy of anti-PD-1 therapy in tumor-bearing mice\u003c/h2\u003e\u003cp\u003eWe next examined whether SSG could augment the therapeutic efficacy of PD-1 blockade. In LLC tumor-bearing mice, SSG alone significantly inhibited tumor growth, and the combination of SSG with an anti-PD-1 antibody resulted in the greatest tumor suppression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;C). By the end of the experiment, the combination treatment (SSG\u0026thinsp;+\u0026thinsp;anti-PD-1) achieved the smallest tumor volumes among all groups. During the treatment period, we monitored the body weights of the mice as an indicator of systemic effects. Mice receiving SSG or SSG\u0026thinsp;+\u0026thinsp;anti-PD-1 exhibited a slight divergence in weight gain starting around day 14, resulting in marginally lower body weights than control mice by day 21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). However, the weight differences were not accompanied by any overt signs of toxicity or distress.\u003c/p\u003e\u003cp\u003eHistopathology provided further evidence of enhanced efficacy in the combination group. H\u0026amp;E-stained tumor sections from the SSG\u0026thinsp;+\u0026thinsp;anti-PD-1 group showed more uniform, regular tumor cell morphology (cells were predominantly round or ovoid) compared to the more pleomorphic cells observed in controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Moreover, IHC analysis demonstrated that the combination therapy greatly reduced the expression of proliferative markers in tumor tissues. CD34 and Ki-67 levels were dramatically lower in the SSG\u0026thinsp;+\u0026thinsp;anti-PD-1 group relative to both the control group and either monotherapy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Importantly, the expression of T cell exhaustion markers in tumors was also lowest in the combination group: PD-1, TIM-3, CTLA-4, and LAG-3 staining in tumor-infiltrating lymphocytes was substantially reduced with SSG\u0026thinsp;+\u0026thinsp;anti-PD-1 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). These data indicate that SSG can potentiate anti-PD-1 immunotherapy, leading to stronger tumor growth inhibition and a more favorable tumor immune milieu than anti-PD-1 therapy alone.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.4. SSG enhances T cell infiltration and reduces T cell exhaustion in vivo\u003c/h2\u003e\u003cp\u003eTo assess SSG\u0026rsquo;s impact on T cells in the context of PD-1 therapy, we analyzed immune cell populations and cytokines in treated mice. Flow cytometry of spleen samples showed that anti-PD-1 monotherapy slightly increased certain immune responses but was paradoxically associated with a reduction in the proportion of splenic CD8\u003csup\u003e+\u003c/sup\u003eT cells (possibly reflecting T cell redistribution or negative feedback). In contrast, mice treated with SSG had a significantly higher percentage of CD8\u003csup\u003e+\u003c/sup\u003e T cells in the spleen, and the combination of SSG\u0026thinsp;+\u0026thinsp;anti-PD-1 restored CD8\u003csup\u003e+\u003c/sup\u003eT cell frequencies to levels comparable to or higher than control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We next examined T cell \u0026ldquo;exhaustion\u0026rdquo; status by measuring key inhibitory receptors on CD8\u003csup\u003e+\u003c/sup\u003e T cells. SSG treatment led to lower expression of exhaustion markers, and the addition of SSG to anti-PD-1 therapy had an additive effect. In the combination group, surface levels of PD-1 and TIM-3 on CD8\u003csup\u003e+\u003c/sup\u003eT cells were markedly reduced compared to either SSG or anti-PD-1 alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Similarly, the combination group showed the lowest expression of other inhibitory receptors (e.g., CTLA-4, BTLA, LAG-3) on T cells (data not shown in figure, see Western blot results below). Consistent with the improved T cell profiles, SSG-treated mice (especially when combined with PD-1 blockade) had significantly elevated levels of Th1-type cytokines. ELISA results from serum (or spleen homogenates) demonstrated that IL-2, TNF-α, and IFN-γ concentrations were higher in the SSG and SSG\u0026thinsp;+\u0026thinsp;anti-PD-1 groups than in controls, with the combination therapy yielding the highest cytokine levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These findings suggest that SSG enhances anti-tumor immunity in vivo by expanding the CD8\u003csup\u003e+\u003c/sup\u003eT cell pool, relieving T cell exhaustion, and promoting a pro-inflammatory cytokine environment.\u003c/p\u003e\u003cp\u003eDouble immunofluorescence staining of tumor sections provided visual confirmation of these immune changes. In control tumors, there was strong co-localization of PD-1 with CD3\u003csup\u003e+\u003c/sup\u003eT cells, indicating that many tumor-infiltrating T cells were PD-1\u003csup\u003ehigh\u003c/sup\u003e (exhausted). In contrast, SSG-treated tumors (especially those also treated with PD-1 antibody) showed markedly fewer PD-1\u003csup\u003e+\u003c/sup\u003eT cells (reduced PD-1/CD3 co-localization). We did not observe significant co-localization of PD-L1 with CD11b in control tumors, suggesting that in this model, PD-L1 may be expressed more on tumor or other cells than on MDSCs. Notably, SSG treatment (\u0026plusmn;\u0026thinsp;anti-PD-1) appeared to reduce overall PD-L1 expression in the tumor tissue, with the most pronounced reduction seen in the combination group (as qualitatively shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These microscopy results align with the flow cytometry and IHC data, indicating that SSG contributes to an immune-permissive tumor environment by modulating both T cells and suppressive myeloid cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.5. SSG reduces MDSC accumulation and suppresses MDSC immunosuppressive functions in vivo\u003c/h2\u003e\u003cp\u003eWe then investigated how SSG affects MDSCs in tumor-bearing mice. Flow cytometric analysis of splenocytes revealed that mice treated with SSG had significantly fewer MDSCs (identified as CD11b\u003csup\u003e+\u003c/sup\u003eGr-1\u003csup\u003e+\u003c/sup\u003ecells) compared to control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In control mice, splenic MDSCs expressed high levels of PD-L1 and Galectin-9 on their surface. Treatment with SSG led to a notable downregulation of these exhaustion-inducing ligands on MDSCs, and the combination of SSG with anti-PD-1 resulted in the most significant reductions in PD-L1 and Gal-9 levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). These data suggest that SSG not only reduces the number of MDSCs but also impairs the ability of any remaining MDSCs to inhibit T cells. Key MDSC-associated enzymes that mediate immune suppression\u0026mdash;Arg-1, IDO, and iNOS\u0026mdash;were all markedly downregulated in the SSG-treated groups, with the greatest decrease observed in the combination therapy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). In addition, the combination group showed substantially lower levels of PD-L1 and Gal-9 proteins in tumor tissue (reflecting contributions from both MDSCs and tumor cells) compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). SSG treatment alone also reduced PD-L1 and Gal-9 levels relative to control, consistent with an overall less suppressive microenvironment. Functionally, we assessed whether SSG\u0026rsquo;s effects on MDSCs translated into changes in cytokine production. ELISA measurements indicated that MDSCs from SSG-treated mice secreted significantly lower amounts of IL-10 and TGF-β\u0026mdash;two potent immunosuppressive cytokines\u0026mdash;compared to MDSCs from control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Again, the combination of SSG with anti-PD-1 had the strongest effect, virtually normalizing the levels of these cytokines to baseline. Collectively, these results demonstrate that SSG can attenuate both the abundance and the suppressive activity of MDSCs in vivo, which likely contributes to the improved T cell responses and anti-tumor effects observed with SSG treatment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Chemical components identified in SSG-containing serum\u003c/h2\u003e\u003cp\u003eTo facilitate the in vitro assays, we prepared SSG-containing serum from rats and confirmed the presence of key active compounds in the serum. Using UPLC-MS/MS in line with Chinese Pharmacopoeia guidelines (2015 edition), we verified that the prepared serum contained the expected ginsenosides within pharmacopoeial standards. HPLC-MS/MS analysis identified the primary constituents of the SSG-containing serum, and representative MRM chromatograms for each compound are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Standard curves (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) were generated for quantification, and the concentrations of major ginsenosides in the serum were determined by correlating chromatographic peak areas with these standard curves. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the serum from SSG-treated rats contained approximately 12.92 ng/mL of ginsenoside Rg1, 1723.06 ng/mL of ginsenoside Rb1, 412.56 ng/mL of ginsenoside Rd, and 13.37 ng/mL of notoginsenoside R1. These data confirm that oral administration of SSG leads to systemic exposure of its active ingredients (at least in metabolite form), which provides a rationale for our in vitro experiments using drug-containing serum. While using serum from SSG-treated animals makes the in vitro findings more physiologically relevant, it should be noted that detailed pharmacokinetic studies were not conducted here. In future studies, a more comprehensive pharmacokinetic profiling of SSG\u0026rsquo;s constituents would be valuable to correlate specific component levels with biological effects, thereby guiding dose optimization and translational prospects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of standard curve determination of main components in SSG-containing serum\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLinear regression equation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCorrelation coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLinear range ng/mL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rg1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;4.65*104X-2.85*105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.88\u0026thinsp;~\u0026thinsp;156.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rb1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.21*103X-1.31*105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.76\u0026thinsp;~\u0026thinsp;5000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;7.79*103X-8.24*105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.76\u0026thinsp;~\u0026thinsp;5000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNotoginsenoside R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.155*104X-8.42*104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.88\u0026thinsp;~\u0026thinsp;156.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConcentrations of main components in SSG-containing serum\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConcentration (ng/mL)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rg1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.9237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rb1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1723.0593\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e412.5584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNotoginsenoside R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.3738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.7. SSG-containing serum suppresses MDSC immunosuppressive activity and reverses T cell exhaustion in vitro\u003c/h2\u003e\u003cp\u003eWe modeled the interaction between MDSCs and T cells in vitro to further dissect the mechanism of SSG. MDSCs were derived from mouse bone marrow in culture (yielding a\u0026thinsp;\u0026gt;\u0026thinsp;95% pure CD11b\u003csup\u003e+\u003c/sup\u003eGr-1\u003csup\u003e+\u003c/sup\u003e population, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA) and then treated with SSG-containing serum. This treatment significantly diminished the immunosuppressive features of the MDSCs. Specifically, after exposure to SSG-conditioned serum, MDSCs showed markedly lower expression of Arg-1, IDO, and iNOS compared to untreated MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Flow cytometry also revealed that the surface expression of PD-L1 and Gal-9 on MDSCs was greatly reduced following SSG serum treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Correspondingly, the amounts of IL-10 and TGF-β released by MDSCs into the culture medium were significantly decreased (as measured by ELISA, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). These results are in line with our in vivo observations, indicating that SSG can directly impair MDSC-derived suppressive factors. Western blotting of MDSC lysates provided further confirmation: protein levels of PD-L1 and Gal-9 were lower in MDSCs treated with SSG-containing serum than in control MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eNext, we investigated whether these changes in MDSCs could alleviate T cell exhaustion. Purified CD8\u003csup\u003e+\u003c/sup\u003eT cells were co-cultured with MDSCs in a Transwell system. In co-cultures where MDSCs had been pretreated with SSG-containing serum, we observed a clear improvement in T cell indicators compared to co-cultures with untreated MDSCs. Flow cytometry showed that CD8\u003csup\u003e+\u003c/sup\u003eT cells co-cultured with SSG-treated MDSCs had significantly lower levels of PD-1 and TIM-3 on their surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB), suggesting reduced exhaustion. Consistently, Western blot analysis of these CD8\u003csup\u003e+\u003c/sup\u003eT cells indicated reduced expression of other exhaustion markers (CTLA-4, BTLA, LAG-3) when the T cells were influenced by SSG-treated MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). Functionally, T cells in the presence of SSG-treated MDSCs were more active: the production of key anti-tumor cytokines (IL-2, IFN-γ, TNF-α) was higher in these co-cultures \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). Moreover, the CD8\u003csup\u003e+\u003c/sup\u003eT cells co-cultured with treated MDSCs showed lower apoptosis rates and higher proliferation (as evidenced by Annexin V/PI staining and CFSE dilution, respectively) compared to T cells co-cultured with control MDSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, F). Taken together, these in vitro findings demonstrate that SSG (via factors present in drug-containing serum) can relieve MDSC-induced T cell suppression\u0026mdash;by both disabling MDSC suppressive mechanisms and protecting T cells from exhaustion\u0026mdash;thereby restoring T cell proliferative capacity and survival.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Single-cell RNA sequencing reveals attenuation of T-cell exhaustion by SSG treatment\u003c/h2\u003e\u003cp\u003eUsing single-cell RNA sequencing, we comprehensively characterized the immune landscape of T lymphocytes. Uniform Manifold Approximation and Projection (UMAP) revealed distinct cellular clusters, highlighting transcriptional heterogeneity among T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). Clustering analysis demonstrated clear separation of subsets, suggesting potential functional specialization within the T-cell compartment. Annotation of clusters identified major T-cell populations, including CD4\u003csup\u003e+\u003c/sup\u003eT cells, CD8\u003csup\u003e+\u003c/sup\u003eT cells, memory T cells, and regulatory T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). When stratified by experimental groups, differential distribution patterns of T cells were observed between the Model and SSG groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC), indicating a group-specific immune response. The expression profiles of canonical immune checkpoint molecules, including PD-1, CTLA-4, TIM-3, LAG-3, and TIGIT, were further examined. Heatmap analysis illustrated marked differences in expression across T-cell subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD). These findings support the existence of functional heterogeneity among T-cell subsets. Cells positive for PD-1, CTLA-4, TIM-3, LAG-3, and TIGIT were visualized on the UMAP plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eE), which demonstrated their spatial distribution within the T-cell landscape. When categorized by experimental groups, distinct patterns of checkpoint-positive T cells emerged (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eF), further emphasizing the divergent immunological states between groups. We next quantified the proportion of CD8\u003csup\u003e+\u003c/sup\u003eT cells expressing checkpoint molecules. Compared with the SSG group, the Model group exhibited a higher proportion of CTLA-4\u003csup\u003e+\u003c/sup\u003e, TIM-3\u003csup\u003e+\u003c/sup\u003e, LAG-3\u003csup\u003e+\u003c/sup\u003e, and TIGIT\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eG). This suggests that T-cell exhaustion was more pronounced in the Model group, whereas the SSG group displayed attenuated exhaustion signatures. Collectively, these findings indicate thatCD8\u0026thinsp;+\u0026thinsp;T cells exhibit distinct transcriptional states and checkpoint expression patterns under different experimental conditions. The enrichment of exhausted CD8\u003csup\u003e+\u003c/sup\u003eT cells in the Model group highlights potential mechanisms of immune dysfunction, while the reduction of such populations in the SSG group suggests an immunomodulatory effect.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThere is increasing recognition of the value of integrating traditional Chinese medicine (TCM) with contemporary cancer therapies, largely due to its immunomodulatory properties [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Within TCM theory, tumor-induced immunosuppression is often conceptualized as a deficiency of \u0026ldquo;vital Qi\u0026rdquo; [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therapies that \u0026ldquo;strengthen vital Qi to combat malignancy\u0026rdquo; are considered to restore immune vigor. Translating this into modern immunology, such interventions are expected to enhance the activity and infiltration of effector immune cells (e.g., cytotoxic T lymphocytes, dendritic cells, natural killer cells) while restraining or reprogramming immunosuppressive populations (e.g., regulatory T cells, tumor-associated macrophages, myeloid-derived suppressor cells, MDSCs) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our findings are consistent with this principle, showing that Shuangshen granules (SSG) enhanced antitumor immune responses while attenuating tumor-promoting immunosuppressive mechanisms.\u003c/p\u003e\u003cp\u003eCombining TCM formulations with immune checkpoint inhibitors (ICIs) has shown synergistic potential in multiple cancer models. SSG, which contains Panax quinquefolius (American ginseng), Panax notoginseng, and Cordyceps sinensis, has been clinically used as an adjuvant to improve treatment tolerance. Ginseng saponins and cordycepin are known to enhance immunity and exhibit anti-tumor effects [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We previously reported that SSG inhibited lung cancer metastasis by interfering with MDSC differentiation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The present study extends these observations by demonstrating that SSG not only reduces markers of T cell exhaustion but also improves the efficacy of PD-1 blockade, effectively reshaping the tumor microenvironment toward immune-mediated control.\u003c/p\u003e\u003cp\u003eNotably, mice receiving high-dose SSG showed a trend of body weight loss toward the end of treatment. Although modest and not accompanied by overt toxicity, this observation suggests that prolonged or high-dose exposure may induce mild metabolic alterations. This finding underscores the importance of dose optimization. Establishing an optimal therapeutic window\u0026mdash;where efficacy is maintained while minimizing adverse effects\u0026mdash;will be critical for the future clinical translation of SSG. Future studies should assess whether reduced dosing or shortened treatment schedules maintain efficacy while minimizing side effects. For clinical translation, dose-escalation studies and close monitoring of body weight and general health will be essential when integrating SSG with ICIs.\u003c/p\u003e\u003cp\u003eOur results further underscore the pivotal role of MDSCs in immunotherapy resistance. Elevated MDSC frequencies have been associated with poor responses to PD-1/PD-L1 inhibitors in NSCLC patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These cells foster a suppressive milieu by limiting T cell activation and promoting additional immunosuppressive subsets [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We focused on MDSC-derived mediators\u0026mdash;Arg-1, IDO, iNOS, PD-L1, and Galectin-9\u0026mdash;because they represent major effector arms of suppression. Arg-1 and IDO deplete amino acids required for T cell proliferation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], while iNOS produces nitric oxide that impairs T cell function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In parallel, PD-L1 and Gal-9 interact with inhibitory receptors such as PD-1 and TIM-3, reinforcing T cell exhaustion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. MDSCs secrete factors such as TGF-β, IL-10, and IDO, which boost the immunosuppressive activity of effector T cells, facilitate immune evasion, and contribute to T cell exhaustion [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our findings that SSG reduced these mediators indicate that it can disrupt multiple layers of MDSC-driven suppression.\u003c/p\u003e\u003cp\u003eImportantly, SSG treatment not only diminished immunosuppressive factors but also enhanced cytokine production by CD8⁺T cells (IL-2, IFN-γ, TNF-α). The elevation of TNF-α requires careful interpretation. While chronic TNF-α signaling within the tumor microenvironment has been implicated in promoting immunosuppression and tumor progression via NF-κB activation in myeloid cells [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], transient increases in T cell\u0026ndash;derived TNF-α reflect restored effector function [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In our study, the concurrent reduction of exhaustion markers alongside increased pro-inflammatory cytokines strongly suggests that SSG reinvigorates antitumor T cell activity rather than aggravating immunosuppression. This dual role of TNF-α highlights the complexity of cytokine biology in cancer immunity and warrants further mechanistic dissection of its temporal and cellular contexts.\u003c/p\u003e\u003cp\u003eIn addition to bulk immunological assays, our single-cell transcriptomic profiling provided mechanistic depth by resolving T cell heterogeneity at high resolution. The analysis was performed on lung tissues from the Model group and the high-dose SSG (SSG-H) group, enabling direct comparison of untreated versus SSG-treated immune landscapes. Consistent with previous reports that exhausted CD8\u003csup\u003e+\u003c/sup\u003eT cells are characterized by co-expression of multiple checkpoint receptors [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], we observed that the untreated tumor microenvironment was enriched in such dysfunctional subsets. Importantly, SSG treatment attenuated exhaustion signatures and restored functional CD8\u003csup\u003e+\u003c/sup\u003eT cell populations. These findings complement our MDSC analyses, suggesting that SSG may alleviate immunosuppression not only by limiting MDSC abundance and function, but also by reversing downstream T cell dysfunction. Compared with prior studies focusing mainly on bulk T-cell markers [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], our single-cell data highlight the value of integrating high-dimensional approaches to capture the dynamic and heterogeneous immune responses modulated by TCM formulations. Nevertheless, an important limitation of our single-cell analysis is that it was restricted to Model and SSG-H groups. While this design provided clear mechanistic insight into the effect of SSG, future studies including PD-1 blockade and lower-dose SSG groups will be necessary to fully elucidate dose-response relationships and potential interactions between SSG and ICIs at the single-cell level.\u003c/p\u003e\u003cp\u003eIn terms of mechanistic insights, our results suggest that SSG acts on multiple signaling pathways associated with MDSC activity and T cell exhaustion. Future studies should focus on identifying the precise bioactive constituents\u0026mdash;such as ginsenosides (e.g., Rg3, Rh2) and cordycepin\u0026mdash;that are known to regulate immune function [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Mechanistic validation of how these compounds influence canonical signaling pathways including NF-κB, STAT3, and TGF-β/SMAD will be critical for elucidating their contributions to the observed immunomodulatory effects. Such studies will provide direct mechanistic evidence linking SSG\u0026rsquo;s phytochemical composition with its immunological activity.\u003c/p\u003e\u003cp\u003eFinally, the translational implications of our findings are significant. MDSCs are increasingly recognized as predictive biomarkers for immunotherapy outcomes in NSCLC, with elevated frequencies correlating with poor responses to PD-1/PD-L1 blockade [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. By demonstrating that SSG reduces both MDSC abundance and their suppressive mediators, our study suggests that SSG may not only enhance ICI efficacy but also support the use of MDSC levels as a predictive biomarker to guide patient stratification. This dual therapeutic and diagnostic potential reinforces the rationale for integrating herbal adjuvants such as SSG with immune checkpoint therapy in lung cancer.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study provides experimental evidence that Shuangshen granules (SSG) enhance antitumor immunity in a lung adenocarcinoma model by modulating the tumor microenvironment. SSG reduced the frequency and suppressive mediators of MDSCs, alleviated T cell exhaustion, and improved the efficacy of anti-PD-1 checkpoint therapy. These findings highlight the therapeutic potential of concurrently targeting myeloid immunosuppression and T cell dysfunction to overcome immunotherapy resistance. SSG represents a promising adjunctive strategy, though future studies are required to determine the active components, define safe and effective dosing regimens, and validate these immunological effects in clinical settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e All animal experiments were approved by the Animal Ethics Committee of Guang\u0026rsquo;anmen Hospital, China Academy of Chinese Medical Sciences (Approval No. IACUC-GAMH-2022-011). and carried out in accordance with the institutional guidelines.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the National Natural Science Foundation of China\u003c/p\u003e\u003cp\u003e(No. 82205226,82174465 ), the Fundamental Research Funds for the Central Public Welfare Research Institutes (No. ZZ18-XRZ-028, ZZ17-XRZ-023), The Special Training of Scientific and Technological Talents, China Academy of Chinese Medical Sciences (No. ZZ13-YQ-028, ZZ13-YQ-023), Natural Science Foundation of Beijing Municipality (No. 7232310), Central High-level Hospital of Traditional Chinese Medicine Clinical Research and Achievement Transformation Capability Improvement Project (No. HLCMHPP2023101), Capital\u0026rsquo;s Funds for Health Improvement and Research (No. 2024-2-4153).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll the listed authors contributed to the conception of the study and its design. Zhongning He、Qi Huang: Conceptualization, Methodology, Writing \u0026ndash; original draft. Jiaqi Hu、Yi Li: Visualization, Investigation. Tongtong Liu: Conceptualization, Methodology. Yuwei Zhao: Visualization, Investigation. Xiaoling Ren: Data analysis, Resources. Shulin He: Experiment. Yue Li: Experiment. Rui Liu: Conceptualization, Methodology. Qiujun Guo: Data analysis. Xing Zhang: Resources. Bolun Shi: Experiment. Jie He: Experiment. Runzhi Qi: Methodology, Validation, Writing \u0026ndash; review, and editing. Zhan Shi、Baojin Hua: Resources, Writing-Review and Editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or investigated during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. 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Nat Rev Immunol. 2009;9(3):162\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nri2506\u003c/span\u003e\u003cspan address=\"10.1038/nri2506\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chinese-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmed","sideBox":"Learn more about [Chinese Medicine](http://cmjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cmed/default.aspx","title":"Chinese Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Shuangshen granules, lung adenocarcinoma, myeloid-derived suppressor cells, immunotherapy, T cell exhaustion","lastPublishedDoi":"10.21203/rs.3.rs-7686641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7686641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWorldwide, lung cancer is the most common cause of cancer-related deaths. Molecular targeted therapies and immunotherapies for non-small-cell lung cancer (NSCLC) have improved outcomes markedly over the past two decades. However, the vast majority of advanced NSCLCs become resistant to current treatments and eventually progress. A traditional Chinese medicine (TCM) formula of Shuangshen granules (SSG) has demonstrated potential in alleviating cancer side effects and imporving survival rate. Despite clinical evidence supporting its benefit, there is still insufficient understanding of the active compounds in SSG and their underlying mechanisms, which limits its broader clinical application.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aims to identify the key active ingredients in SSG and explore their mechanisms, particularly through modulating myeloid-derived suppressor cell (MDSC)- induced T cell exhaustion, to provide a scientific basis for its application in cancer treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe primary active compounds, potential therapeutic targets and intervening signaling pathways, which SSG might inhibit lung adenocarcinoma (LUAD) were predicted by network pharmacology and molecular docking. Subsequently, Lewis lung carcinoma (LLC) tumor-bearing mouse model was established to assess the efficacy of combined SSG and anti-PD-1 therapy in vivo, and MDSCs and CD8\u003csup\u003e+\u003c/sup\u003eT cells were isolated for in vitro co-culture experiments, while pathological examination was conducted using hematoxylin and eosin (HE). The expression of PD-1, TIM-3, CTLA-4, LAG-3 Arg-1, IDO, iNOS, PD-L1 and Gal-9 was detected using immunohistochemistry (IHC), immunofluorescence, and flow cytometry and Western blotting. The expression of IL-2, TNF-α and IFN-γ were detected by reverse transcription-quantitative polymerase chain reaction (qPCR). Concentrations of IL-10 and TGF-β were measured by enzyme-linked immunosorbent assay (ELISA)\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe obtained 19 active ingredients of SSG and predicted 37 potential targets through network pharmacology analysis, among which MOL001792, MOL000449, MOL000358 and MOL000098 were selected as core drug ingredients, and EGFR, IL1B, IL6 and TNF were identified and included into the range of core targets. GO and KEGG analyses suggested that the TNF signaling pathway might hold a crucial role in lung cancer by reducing MDSC and T-cell exhaustion. In the animal experiment, SSG increased TNF-α levels and reduction of T cell exhaustion markers and the down-modulation of MDSC suppressive mediators.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis investigation identifies MOL001792, MOL000449, MOL000358 and MOL000098 as critical active ingredients in SSG, impacting key biomarkers such as EGFR, IL1B, IL6 and TNF. These substances effectively modulate the TNF signaling pathway, alleviating MDSC-induced T cell exhaustion and restoring anti-tumor immune function.\u003c/p\u003e","manuscriptTitle":"Shuangshen granules enhance anti-PD1 therapy efficacy in lung adenocarcinoma by modulating myeloid-derived suppressor cell-induced T cell exhaustion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 20:16:40","doi":"10.21203/rs.3.rs-7686641/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T09:22:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T11:34:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T10:02:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T09:15:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T08:12:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T08:53:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150993155153055134212253196490786763192","date":"2025-10-27T02:39:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136740409943785101957556124423987056779","date":"2025-10-27T02:06:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179505791708796870778336053161484771579","date":"2025-10-26T10:29:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282613199654899789042436173061031411339","date":"2025-10-25T07:22:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114213590319435682642147589779395026163","date":"2025-10-24T08:09:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110402733998689204669319667916466592172","date":"2025-10-24T07:12:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221385384698244969325596534848557036878","date":"2025-10-24T04:26:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223163209956311761288761795886333892850","date":"2025-10-24T04:21:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58328204725991821086459906145717123930","date":"2025-10-24T03:57:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T02:04:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T08:32:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-27T05:20:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chinese Medicine","date":"2025-09-22T15:40:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chinese-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmed","sideBox":"Learn more about [Chinese Medicine](http://cmjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cmed/default.aspx","title":"Chinese Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"162375a9-4f2f-486d-b709-58d32620bde4","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-28T09:39:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 20:16:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7686641","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7686641","identity":"rs-7686641","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}