Single-patient single-cell RNA sequencing reveals neuroendocrine predominance and immunosuppression in small-cell lung cancer

Introduction

As the cancer with the highest incidence and mortality in the world, lung cancer poses a serious threat to public health (1,2). Lung cancer has surpassed liver cancer as the leading cause of cancer-related death in China (3). Lung cancer is divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). The incidence of SCLC makes up 13–15% of all lung cancers (2). The origin of SCLC is primarily attributed to pulmonary neuroendocrine cells (PNECs), a type of amine precursor uptake decarboxylation (APUD) cell also known as Kulchitsky cells, with their progenitors as another possible candidate. SCLC is characterized by the inactivation of key tumor suppressor genes, most commonly through mutations in TP53 and Rb1 (4). Depending on whether the lesion scope exceeds one radiation field, SCLC is divided into limited stage (LS) or extensive stage (ES). Most patients are already in ES-SCLC at first diagnosis. NSCLC now has a 2-year overall survival (OS) rate of over 42%, but SCLC has a 2-year OS of around 14–15% (5) and a 5-year OS of less than 7% (6,7).

SCLC is characterized by its rapid progression and early metastasis. Platinum-combined with etoposide and platinum-combined with irinotecan have been used as first- and second-line treatments for SCLC since 1985, respectively. There has been no appreciable advancement in treatment for more than two decades. Decreased sensitivity to chemotherapy and rapid development of chemotherapy resistance in SCLC treatment lead to poor prognosis, with the median overall survival (mOS) following first-line chemotherapy being 10 months (8). Even when used in conjunction with standard chemotherapy, such as atezolizumab and durvalumab, which the Food and Drug Administration (FDA) approved in 2019 and 2020, respectively, mOS for patients with ES-SCLC was only increased by about 2 months (9,10). As immunotherapy and targeted therapy failed to significantly support SCLC patients, the FDA revoked the third-line SCLC therapeutic medicines nivolumab and pembrolizumab, approved in 2018 and 2019, respectively (11,12). Nevertheless, immune checkpoint inhibitors have continued to make incremental progress in the treatment of SCLC. For LS-SCLC, the Phase III ADRIATIC trial demonstrated that patients who received durvalumab as consolidation therapy for two years following concurrent chemoradiotherapy showed significantly longer median progression-free survival (mPFS) and mOS compared to those receiving placebo, leading to FDA approval of durvalumab for LS-SCLC consolidation therapy in December 2024 (13). In the case of ES-SCLC, based on the IMPower133 and CASPIAN trials, the PD-L1 inhibitors atezolizumab or durvalumab in combination with platinum-etoposide chemotherapy have been recommended as first-line treatment options (14,15). However, the absolute survival benefit derived from immune checkpoint inhibitors remains limited. In the IMPower133 and CASPIAN trials, only 13.0% and 11.8% of patients, respectively, experienced an OS benefit, underscoring the urgent need for predictive biomarkers to identify patients most likely to respond to immunotherapy. Additionally, the PD-1 inhibitor serplulimab became the first PD-1 inhibitor approved in combination with chemotherapy for ES-SCLC, based on the Phase III ASTRUM-005 trial, which demonstrated a survival advantage with serplulimab plus chemotherapy followed by maintenance therapy compared to chemotherapy alone (16). Furthermore, three Phase III trials conducted in China have corroborated that PD-1/PD-L1 inhibitors can improve outcomes in ES-SCLC patients (17-19). In summary, the efficacy of PD-1/PD-L1 inhibitors in SCLC remains constrained by the “immune-cold” nature of the disease, characterized by impaired antigen presentation and a highly immunosuppressive tumor microenvironment (TME). To overcome this therapeutic challenge, future research should focus on developing rational combination strategies and establishing biomarker-guided precision treatment approaches.

Currently, next-generation sequencing (NGS), RNA microarray analysis, mass spectrometry, and other methods are used for molecular biology studies of SCLC cell lines, xenotransplantation models, and mutant mice models. New avenues for tumor and TME analysis from the viewpoints of genomes, transcriptomics, epigenetics, and multi-omics are made possible by modern advances in single-cell RNA sequencing (scRNA-seq) technology. The biological characteristics of SCLC are still unclear. In this study, we used scRNA-seq to analyze tumor tissues and adjacent noncancerous tissues of a patient with LS-SCLC, and validated some of the findings with cell experiments to gain a deeper understanding of SCLC. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1674/rc).

Methods

Sample source

Tissue samples were obtained from a patient with LS-SCLC from the Department of Thoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, between September 2022 and October 2022. The two tissues were subjected to scRNA-seq. The patient was diagnosed with SCLC in the anterior basal segment of the lower lobe of the right lung by preoperative puncture biopsy. This patient was previously diagnosed with lymphoepithelial carcinoma of the left upper lung in May 2015. In June 2015, they underwent left upper lobectomy and were diagnosed with pT2N0M0, stage Ib. Subsequently, from July to September 2015, they received 4 cycles of GP (gemcitabine + platinum) chemotherapy. There was no history of other chronic diseases.

The inclusion criteria were as follows: (I) age between 18 and 70 years; (II) histopathologically confirmed SCLC; (III) no distant metastasis at initial diagnosis; (IV) surgical resection at initial diagnosis without prior chemotherapy or immunotherapy; (V) availability of well-preserved tumor specimens; and (VI) complete clinical records.

The exclusion criteria were as follows: (I) history of any other malignancy within the past five years; (II) severe systemic diseases involving the cardiovascular, endocrine, hematologic, or immune systems; and (III) Karnofsky performance status (KPS) below 80.

Our research complies with the Declaration of Helsinki and its subsequent amendments. This study was approved by The First Affiliated Hospital of Guangxi Medical University’s ethics committee (No. 2023-E428-01) and informed consent was obtained from the individual participant.

Tissue processing

Tissue preparation

After an intraoperative frozen biopsy confirmed the tumor growth range, the surgeon excised the tumor tissue and adjacent tissues about 3 cm away from the tumor. The cell suspension of the above specimens was prepared in vitro within 20 minutes. The specific preparation method is as follows: fresh tumor tissue, as well as surrounding noncancerous tissue, were minced in DMEM before being treated for 30 minutes at 37 ℃ with Collagenase IV (Sigma C5138-500MG, Sigma-Aldrich, St. Louis, MO, USA) and DNase I (Sigma DN25-100MG, Sigma-Aldrich, St. Louis, MO, USA). The cells were filtered through 40 µm filters, re-suspended in 5 mL of RBC Lysis buffer, and centrifuged. After the supernatant was discarded, the cells were well mixed. For centrifugation, cells were then re-suspended in 5 mL of 0.04% BSA PBST solution.

Cell viability and counting

Cell viability was assessed by the AOPI staining technique, which calls for a minimum of 80% cell viability. The LUNA-FL fluorescent cell counter was used to accomplish cell sorting and counting.

scRNA-seq and library construction

Sequencing was carried out on the 10x Genomics platform in line with the manufacturer’s instructions (Pleasanton, CA, USA). We used the Chromium Next GEM Single Cell 5' Library &Gel Bead Kit v1.1 (10x Genomics, PN-1000165, PN-1000167) to create single-cell transcriptome libraries that were sequenced at the Illumina NovaSeq 6000 (Illumina, Inc, San Diego, CA, USA) to obtain the results we want.

Preprocessing and analysis of scRNA-seq data

The Cell Ranger program (v6.1.2) was used to demultiplex the raw information into FASTQ files, after which the gene expression matrix for each cell was produced and compared to the reference transcriptome GRCh38. Ambient RNA contamination and doublets were removed from each sample with SoupX (v1.6.1) and DoubletFinder respectively. Subsequent analyses were performed on Seurat (v4.1.1). Two samples’ single-cell gene expression data were combined. At the quality control (QC) stage, cells having fewer than 500 or more than 7000 expressed genes and more than 10% of unique molecular identifiers (UMIs) produced from the mitochondrial genome were disqualified. QC results are summarized in Table S1.

After filtering the low-quality cells, we used Seurat software for data consolidation analysis. We used the RunHarmony to remove batch effects, and the SCTransform to naturalize the original UMI counts, setting the number of cells to 3,000. Ultimately, a total of 15,500 cells passed QC and were included in the downstream analysis. Principal component analysis with RunPCA was used to achieve dimensionality reduction. The ElbowPlot function was used to determine the optimal number of principal components. Clustering with Louvain-Jaccard to obtain clustering information and visualization through the uniform manifold approximation projection (UMAP) dimensionality approach.

Clustering and annotation

The FindAllMarkers function in Seurat was used to find the marker genes of each cluster. We filtered the findings using an average log2 transformed fold-change (log₂FC) >1 and an adjusted P<0.01. BlueprintEncode and HPCA annotation library were used in SingleR to annotate the results. The results were then compared to those from conventional marker genes to identify the various cell types. Epithelial cells: KRT8, KRT18, EPCAM; neuroendocrine epithelial cells with ASCL1, NCAM1, MLLT11 and TUBB2B; Macrophages: CD163, CCL18, C1QA; monocytes: CD68, CD36, CD14, FCN1; myeloid cells: TPSAB1, KIT; fibroblasts: LUM, SFRP4, PI16, MMP11, FAP; T-natural killer (NK) cells: CD3G, CXCR3, FOXP3, NKG7, GZMA; B cells: IGKC, CD79A, MS4A1, JCHAIN; endothelial cells: VMF, ENG, CDH5, FLT1; smooth muscle cells: CNN1, DES (Table S2).

Gene set variation analysis (GSVA)

We assessed the level of pathway activation in each cell cluster with GSVA (v1.32.0). We obtained 50 signature gene sets (v6.2) from the Molecular Signature Database (MsigDB) that each represented a distinct biological process (20). The differentially enriched pathways between tumor tissue and adjacent noncancerous tissue for the cell types of interest (neuroendocrine epithelial cells, T-NK cells, B cells, macrophages, monocytes, and myeloid cells) were found by the limma package (v3.40.6).

Immunohistochemistry

Tumor and adjacent noncancerous tissues were fixed in paraffin. HistoCore AutoCut (Leica, Deerfield, IL, USA) was used to prepare tissue sections. Slices were roasted and heated in citrate repair buffer at 95 ℃ for 5 minutes to complete antigen repair. Slides were blocked with 2% FBS at room temperature for 15 minutes after being treated with endogenous peroxidase blocker for 10 minutes. Slides were subsequently treated at 4 ℃ overnight with an antibody against BEX1 (Proteintech, Rosemont, IL, USA; Cat#12330-1-AP, 1:100) and an antibody against MAP1b (Proteintech Cat#21633-1-AP, RRID: AB_10793666, 1:400). Horseradish peroxidase-labeled goat anti-rabbit IgG (ZSGB-Bio, Beijing, China; ZB-2301) was applied to cells for 30 minutes at 37 ℃. Cells were dyed with DAB, dehydrated after hematoxylin redyeing. Images were taken by digital slide scanner (HAMAMATSU NANO ZOOMER s60, C13210-01, Hamamatsu Photonics, Hamamatsu, Japan).

Construction of intercellular communication networks

We used CellPhoneDB (21) with default settings to analyze ligand-receptor pairs interactions between T-NK cells and other cells. The Mann-Whitney U test was used to screen for ligands and receptors that were significantly overexpressed in each cell cluster. The significance cutoff was established as P<0.05. When the associated ligand and receptor were significantly expressed in two cell clusters, ligand-receptor (L-R) pairs were referred to as “significant interacting pairs”. The total of the upregulation rates of the ligand and receptor in their respective clusters was used to establish the interaction strength. The number of meaningful L-R interaction pairings between two cell clusters was also used to define the total cellular communication intensity between them. Within each cell subpopulation, these concepts were employed to build intercellular communication networks.

Cell line and transfection

Human SCLC lines NCI-H196/H446/H69/H209/H82/H524 were obtained from the Chinese Academy of Sciences Cell Bank (Shanghai, China). They were grown in RPMI-1640 medium (Gibco, Waltham, MA, USA) plus 10% fetal bovine serum (Gibco), 1% penicillin and streptomycin were added to the medium. The cell line was cultured at 37 ℃ with 5% CO₂ in a humidified incubator. BEX1/MAP1b-small interfering RNAs (siRNAs) and their corresponding negative control were synthesized by Gene-Pharma (Shanghai, China). When the cell density reached the range 70–80%, the transfection was performed using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA). The efficiency of the transfection was verified through reverse transcription quantitative polymerase chain reaction (RT-qPCR) analysis and Western blotting.

RT-qPCR

Total RNA was extracted using TRIzol reagent (Takara, Shiga, Japan). Reverse transcription was performed with random primers using the MonScriptTM miRNA First Strand cDNA Synthesis Kit (MR05301, Monad, Suzhou, China). Target RNA expression levels were quantified using SYBR Green Master Mix on a CFX96TM Real-Time PCR System. β-actin served as the endogenous control, and fold changes were calculated using the 2^−∆∆CT method. All experiments were conducted in triplicate. Primer sequences are shown in Table S3.

Western blot

Cells were lysed in RIPA buffer with phosphatase inhibitors (Solarbio, Beijing, China). Total protein was separated by SDS-PAGE (P1200, Solarbio) and transferred to PVDF membranes (Millipore, Burlington, MA, USA). The membranes were blocked in QuickBlock™ Blocking Buffer (P0252, Beyotime, Shanghai, China). Membranes were incubated on a plate shaker overnight at 4 ℃ with corresponding primary antibodies against MAP1b (Proteintech Cat#21633-1-AP, RRID: AB_10793666), BEX1 (ThermoFisher Cat#PA5-100404, Thermo Fisher Scientific, Waltham, MA, USA), and Actin (Abcam Cat#ab8226, RRID: AB_306371, Abcam, Cambridge, UK). Membranes were extensively washed with TBS-T (3× for 10 min), followed by incubation with appropriate horseradish peroxidase-conjugated secondary antibodies diluted in PBS for 2 h at room temperature. Membranes were detected by Western blot imaging system (G:Box chemi XX9, Syngene, Cambridge, UK) following the manufacturer’s recommendations.

Cell Counting Kit-8 (CCK-8)

Experiment according to the CCK-8 (BS350B, Biosharp, Hefei, China) instructions. After transfection, cells were inoculated on 96-well plates with a density of 5000 cells per well, 100 µL per well, and cultured in a 37 ℃ incubator for 12, 24, 48, 72, and 96 h in sequence. Each well was added 10 µL CCK-8 solution and incubated for 4 h. Optical density (OD) values of the samples were measured at 450 nm by enzyme-labeler. The cell survival rate was calculated. Cell viability (%) = OD (treatment)/OD (control) × 100%.

Flow cytometry analysis of apoptosis

Annexin V-APC/7-AAD Apoptosis Kit (AP105-01, MultiSciences, Hangzhou, China) was applied to detect apoptosis rate of each sample. Cells were collected 24 h after transfection and washed twice with PBS. Each sample was resuspended in 500 µL binding buffer, screened and incubated with 5 µL Annexin V-APC and 10 µL 7-AAD. The apoptosis rate was detected using flow cytometer (BD FACS Aria, BD Biosciences, San Jose, CA, USA). Within the single-cell gate, a four-quadrant analysis was performed based on Annexin V and PI fluorescence signals. PI⁻/Annexin V⁻ cells were considered viable cells, PI⁻/Annexin V⁺ cells were considered early apoptotic cells and PI⁺/Annexin V⁺ cells were considered late apoptotic or dead cells (Table S4).

Transwell

The migration ability of NCI-H82 and NCI-H209 was investigated using the transwell chamber system. Serum-free medium was inoculated in the upper chamber with 1×10⁵ cells per well, and culture medium containing 20% FBS was added to the lower chamber. After incubation for 24 h, cells entering the lower chamber were counted by cell counter (JIMBIO FIL Mono, JIMBIO, Changzhou, China).

Statistical analysis

All our quantitative experiments were independently repeated three times to ensure data reliability and statistical significance. The researcher performing cell treatments, drug administration, or transfection did not participate in subsequent data analysis or result evaluation. All statistical calculations were done in R (v3.6.0). The distribution of various cells in tumor tissues and surrounding non-cancerous tissues was examined using the chi-square test. We used Bonferroni correction method to correct P values. Statistical significance was established as an adjusted P value of 0.05.

Results

Single-cell sequencing in tumor and adjacent noncancerous tissue

We used a droplet based single-cell sequencing approach to evaluate tumor tissue (n=1) and adjacent noncancerous lung tissue (Figure 1A). Adjacent noncancerous tissue had a median of 4,024 genes and 4,024 unique UMIs per cell, while tumor tissue had a median of 10,394 genes and 10,943 unique UMIs per cell. Single cell transcriptomes of two tissues were combined to create cell maps of SCLC. We discovered 19 cell clusters using the Louvain-Jaccard graph-based clustering of single cells (Figure 1B).

Figure 1 Single-cell RNA sequencing of SCLC. (A) Workflow of experimental design and scRNA-seq analysis. (B) UMAP plot of 15,500 cells, color-coded by cell type (left) and Seurat cluster identities (right). (C) Heatmaps showing traditional and cell type-specific marker genes across clusters. (D) Pie charts showing cell type fractions in adjacent noncancerous tissue and tumor tissue, colored by cell type. NK, natural killer; SCLC, small cell lung cancer; scRNA-seq, single-cell RNA sequencing; UMAP, uniform manifold approximation projection.

Our dataset’s cell clusters were classified into ten different cell types using traditional marker genes: epithelial cells (C10, C15, n=553), neuroendocrine epithelial cells (C0, C3, C6, C7, C11, n=6,887), endothelial cells (C12, n=328), fibroblasts (C8, n=742), and smooth muscle cells (C18, n=109), T-NK cells (C1, C5, C14, n=2,991), B cells (C16, n=181), macrophages (C2, C4, C9, n=3,284), monocytes (C13, n=280), and myeloid cells (C17, n=145) (Table S2). Neuroendocrine epithelial cells, epithelial cells, T-NK cells, and macrophages were made up of multiple clusters (Figure 1C). In tumor tissues, there were significantly fewer immune cells (T-NK cells, B cells, macrophages) and stromal cells (epithelial cells, endothelial cells, fibroblasts, smooth muscle cells). However, the percentage of neuroendocrine epithelial cells considerably increased in tumor tissue, reaching 95.12% of all cells (Figure 1D). This finding demonstrated the neuroendocrine cell phenotype of SCLC and the reduction in immune cell infiltration.

Heterogeneity of multicellular ecosystems in SCLC

Heterogeneity of SCLC

The cell composition of tumor tissue differs greatly from that of adjacent noncancerous tissue (Figure 2A). We evaluated the percentage of each cell population in adjacent noncancerous tissue and tumor tissue to understand how each cell type affected the multicellular ecological heterogeneity of SCLC (Figure 2B).

Figure 2 Diagram of different cell subtypes in adjacent noncancerous tissue and tumor tissue. (A) UMAP diagram of 15,500 cells, colored by the distribution of neighboring noncancerous tissue and tumor tissue (left) and different cell subtypes (right). (B) Bar plot showing the proportion of each cluster in adjacent noncancerous tissue and tumor tissue. (C) Violin plots depicting the expression of ASCL1, NCAM1, MLLT11, and TUBB2B in different clusters of neuroendocrine epithelial cells. N, noncancerous tissue; NK, natural killer; T, tumor tissue; UMAP, uniform manifold approximation projection.

Clusters C0, C3, C6, C7, and C11 exhibited high expression of neuroendocrine marker genes including ASCL1, NCAM1, TUBB2B and the oncogene MLLT11 (Figure 2C). ASCL1 is a molecular type indicator of SCLC. ASCL1-high SCLC exhibits neuroendocrine features. Neural cell adhesion molecules (NCAM), known as CD56 is only expressed in neuroendocrine organs and malignancies, such as SCLC (22). TUBB2B encodes the main components of microtubules. The expression level of the ectopic fusion gene MLLT11, also known as AF1q, is linked to tumor development, migration, and treatment resistance. In hematologic conditions and various solid malignancies (including breast cancer, thyroid cancer, testicular cancer, and neuroblastoma), increased MLLT11 expression relates to poor clinical outcome (23-25). These findings indicated that SCLC displayed marked intratumoral heterogeneity characterized by distinct neuroendocrine-related subpopulations, suggesting that cellular composition diversity contributed to its aggressive behavior and therapeutic resistance.

GSVA of various cell clusters

The functional pathway activity of immune-related cells and neuroendocrine epithelial cells was then compared using GSVA. Compared to adjacent noncancerous tissue, some pathways of neuroendocrine epithelial cells were considerably upregulated in tumor tissue, including MYC targets, Hedgehog signaling pathway, transforming growth factor (TGF)-β signaling pathway, Wnt/β catenin signaling, and Notch signaling. B cells expressed Kras signaling pathway highly. T-NK cells, Macrophages and Monocytes expressed Hedgehog signaling pathway highly. Myeloid cells expressed TNFA signaling via NF-κB and Wnt/β catenin signaling pathway highly (Figure 3A-3F). Findings suggested that the occurrence and growth of SCLC, as well as the preservation of tumor invasion, may be correlated with the activation of specific pathways. Neuroendocrine epithelial cells and most immune-related cells were downregulated in the interferon (IFN)-α/γ response (Figure 3A-3F).

Figure 3 Functional characteristics of relevant cell types. (A) Differences in hallmark pathway activities between neuroendocrine epithelial cells (C0, C3, C6, C7, C11), tumor versus adjacent noncancerous tissue. The t-value is calculated in a linear pattern, comparing the estimated path score between two groups by GSVA. (B-F) The same as (A) for B cell cluster (C16) (B), T-NK cluster (C1, C5, C14) (C), macrophage cluster (C2, C4, C9) (D), monocyte cluster (C13) (E), and myeloid cell cluster (C17) (F). (G) Venn diagram showing the intersection of significantly upregulated genes of immune-related cells and neuroendocrine epithelial cells from tumor compared to those from the adjacent noncancerous tissue. GSVA, genome set variation analysis; N, noncancerous tissue; NK, natural killer; T, tumor tissue.

We next examined the differentially expressed genes in cell types of concern and identified 2 significantly upregulated genes, which were shared by immune-related cells and neuroendocrine epithelial cells. These two genes were MAP1b and BEX1, respectively (Figure 3G). MAP1b, a member of the MAPs family, is an important component of microtubule assembly and stability, and is involved in cell proliferation, differentiation, autophagy, migration, and the growth of malignant tumors (26). After retinoic acid therapy, BEX1, an intracellular signaling transducer or regulator, initially showed decreased expression in F9 teratoma cells. BEX1 contributed to the growth of malignancies specifically related to the neuroendocrine system (27). MAP1b and BEX1 might link to the onset and progression of SCLC. MAP1b and BEX1 may be a possible therapy target and predictor of SCLC.

Immunohistochemistry of BEX1 and MAP1b

Sequencing results indicated that BEX1 and MAP1b were highly expressed in tumor, especially in neuroendocrine epithelial cells and epithelial cells (Figure 4A,4B). Immunohistochemistry (IHC) experiments were used to verify this. Both BEX1 and MAP1b are cytoplasmic proteins; BEX1 can also be found in nucleus on occasion. IHC results showed that tumor tissues had higher levels of BEX1 and MAP1b expression than nearby noncancerous lung tissues (Figure 4C,4D).

Figure 4 Immunochemistry data confirmed the presence of BEX1 and MAP1b in various cell types. (A) Violin plot showing the expression of BEX1 and MAP1b in each cell type. (B) Violin plot showing the expression of BEX1 and MAP1b in tumor tissue compared with adjacent noncancerous tissue. (C,D) Representative immunohistochemical staining of BEX1 (C) and MAP1b (D) in tumor tissue and adjacent noncancerous tissue. *, P<0.05; ***, P<0.001. N, noncancerous tissue; NK, natural killer; T, tumor tissue.

BEX1 and MAP1b regulated cell proliferation, apoptosis and migration in vitro

The qRT-PCR results showed that in human SCLC cell lines (NCI-H196/H446/H82/H69/H209/H524), the expressions of MAP1b and BEX1 were significantly increased in NCI-H82 cell lines and NCI-H209 cell lines, respectively (Figure 5A). Therefore, we selected NCI-H82 and NCI-H209 to knock out MAP1b and BEX1, respectively to further explore their biological functions in SCLC. After transfection of siRNA targeting MAP1b and BEX1 into NCI-H82 and NCI-H209 cells, the expression levels of MAP1b and BEX1 in siRNA#001, siRNA#002, and siRNA#003 groups were significantly reduced compared with negative controls (Figure 5B,5C). Considering the transfection efficiency, siMAP1b#002 and siBEX1#002 sites were selected for follow-up experiments. With CCK-8 detection, inhibition of BEX1 and MAP1b expression significantly reduced the proliferation ability of NCI-H209 and NCI-H82 cells, respectively (Figure 5D). Transwell assay suggested that inhibiting the expression of MAP1b and BEX1 could significantly reduce the migration ability of NCI-H82 and NCI-H209 cells, respectively (Figure 5E). Flow cytometry showed that downregulation of BEX1 and MAP1b significantly promoted the apoptosis of NCI-H209 and NCI-H82 cells, respectively (Figure 5F-5I). These findings suggested that BEX1 and MAP1b acted as potential oncogenic drivers in SCLC, promoting tumor cell proliferation and migration while inhibiting apoptosis, thus indicating their pivotal roles in maintaining the malignant phenotype of SCLC cells.

Figure 5 MAP1b and BEX1 modulated proliferation, migration and apoptosis in vitro. (A) Levels of MAP1b and BEX1 mRNA in different SCLC cell lines. (B,C) RT-qPCR and Western blotting were applied to examine the knockdown efficiency of MAP1b in NCI-H82 and BEX1 in NCI-H209. (D) A CCK-8 assay was conducted to detect cell proliferation following MAP1b knockdown (left) and BEX1 knockdown (right) in NCI-H82 and NCI-H209. (E) The effect of MAP1b (left) and BEX1 (right) knockdown on the migration capacity of NCI-H82 and NCI-H209. (F-I) The effect of MAP1b (F,G) and BEX1 (H,I) knockdown on cell apoptosis in NCI-H82 and NCI-H209, respectively. Unpaired two-tailed Student’s t-tests and one-way or two-way analysis of variance were used to determine significance. Data are presented as mean ± SD (n=3). **, P<0.01; ****, P<0.0001. CCK-8, Cell Counting Kit-8; OD, optical density; RT-qPCR, reverse transcription quantitative polymerase chain reaction; SCLC, small cell lung cancer; SD, standard deviation.

T cell subtype changes in tumor and adjacent noncancerous tissue

Adaptive immunity is essential for cancer therapy. T cell infiltration, particularly cytotoxic T cells and memory T cells, was positively associated with high therapeutic effectiveness (28). Three types of T-NK cells (cluster 1, cluster 5, cluster 14) were found in tumor tissues. C1 and C5 each contained two subtypes (Figure 6A). Compared to adjacent noncancerous tissue, tumor tissue had a higher percentage of C1 cluster, particularly C1b, while C4 and C15 clusters were the opposite (Figure 6B,6C).

Figure 6 Detailed description of T-NK cell characteristics. (A) UMAP plot of 2,991 T-NK cells, color-coded by re-clustered cell subtype. (B) Bar plot showing the proportion of T-NK cell clusters (C1, C5, C14) in adjacent noncancerous tissue and tumor tissue. (C) Bar plot showing the proportion of re-clustered T-NK cell subtypes in tumor tissue and adjacent noncancerous tissue. (D) Heatmap showing the expression of selected function-related marker genes in five T-NK cell clusters. (E) Heatmap showing differences in pathway activities of five T-NK cell clusters. (F) Bar plots showing differences in the hallmark pathway activities of T-NK cells of two groups: C1b versus C1a. The red columns indicate the significantly upregulated functional pathways in C1b cells (P≤0.05, t>0); the blue columns indicate the significantly downregulated functional pathways in C1b cells (P≥0.05, t<0); and the gray columns indicate functional pathways that were not significantly expressed in C1b cells (P≥0.05). (G) Volcano diagram showing differentially expressed genes (C1b versus C1a). Genes with adjusted P<0.05 and log₂FC ≥0.25 were considered significantly upregulated (red); genes with adjusted P<0.05 and log₂FC ≤−0.25 were considered significantly downregulated (green); genes not meeting these criteria were considered non-significant (blue). FC, fold change; N, noncancerous tissue; NK, natural killer; T, tumor tissue; UMAP, uniform manifold approximation projection.

We used T cell and NK cell classic markers as marker genes for specific cell subtypes (29-31). C5a highly expressed naive T cells (Tn) markers (Tcf7, Lef1, and Ccr7), C5b highly expressed CD4⁺ helper T cells (Th) markers (CD3E, CD4, and CD40LG). C14 expressed highly regulatory T cell (Treg) markers (IL2RA, FOXP3 and CTLA4) and CD28. As a cell surface glycoprotein receptor, CD28 is not only involved in the immune escape of many tumors, but has also been shown to negatively affect the anti-tumor response of T cells (32). Effector T cell features (CD3E, CD8A, and CD8B) were strongly expressed on C1a. C1b showed large quantities of effector markers (GZMA, GZMB), traditional NK or NKT cell markers (KLRF1, NKG7, and KLRB1) and cytokines (CXCR2, CCL3, and CX3CR1). Finally, T-NK cells were regrouped into five subtypes: CD8⁺ Tem cells (C1a), CXCR2⁺CCL3⁺CX3CR1⁺NK/NK T cells (C1b), Tn cells (C5a) and CD4⁺Th cells (C5b). Treg cells (C14) (Figure 6D).

The activity of each T-NK cell subtype pathway was then compared (Figure 6E). Many immune related pathways were downregulated in C1a Tem, including TNFA signaling via NF-κB, Wnt/β catenin signaling, TGF-β signaling, IL6 JAK STAT3 signaling, IFN-α/γ response signaling, and Hedgehog signaling. C1b CXCR2⁺CCL3⁺CX3CR1⁺NK/NK T, C5a Tn and C5b CD4⁺Th upregulated in TGF-β signaling pathway. In C14 Treg cells, many pathways were upregulated, including TNFA signaling via NF-κB, Wnt/β signaling, IL6 JAK STAT3 signaling, DNA repair, and G2M checkpoint, Notch signaling, PI3K AKT MTOR signaling, MYC, and oxidative phosphorylation.

Considering C1b was enriched in tumor tissue, we focused on the comparative analysis of C1a and C1b (Figure 6F). GSVA showed that IL6 JAK STAT pathway was upregulated in C1b. Additionally, we examined the C1b’s highly elevated genes (Figure 6F). C1b markedly increased the expression of a few integrins and chemokines, including CCL3, CX3CR1, and CCL4L2 (Figure 6G).

Intercellular communication in SCLC cell microenvironment

We created intercellular communication networks of SCLC by comparing the variations in ligand-receptor (L-R) pairings and differentially expressed genes between nearby noncancerous tissue and malignant tissue to clarify the regulatory relationship between relevant cell types. By visualizing the Circos diagram, we found 501 L-R interaction upgrades in all cell types (Figure 7A). We examined T-NK cells and screened for significantly upgraded receptor or adjunctive genes in tumor tissue T-NK cells (Figure 7B). Five significantly expressed ligand genes were upregulated, including CLEC2B, CCL3, CCL4, CD6, and CCL4L2. Three receptor genes were significantly upregulated, including KLRF1, KLRG1 and TGFGR3. We focused on eight L-R pairs of upregulated differential genes of T-NK cell subsets in tumor tissues as receptors or ligands to elucidate the close relationship between T-NK cells and neuroendocrine epithelial cells, epithelial cells, endothelial cells, monocytes, and macrophages (Figure 7C).

Figure 7 Interactions between immune cells and stromal cells based on ligand-receptor. (A) Circos diagram showing the ligand-receptor interaction pairs formed by up-regulated genes in different cell types in tumor tissue. (B) Circos diagram showing 8 upregulated ligand-receptor interactions between the T-NK cells and other cells in tumor tissue. (C) Bubble maps showing 8 upregulated ligand-receptor interactions between the T-NK cells and other cells in tumor tissue. (D) Violin plots showing the significantly upregulated ligand/receptor genes in tumor tissue. (E) Violin plots showing the corresponding and significantly upregulated ligand/receptor genes in T-NK cell clusters. N, noncancerous tissue; NK, natural killer; T, tumor tissue.

Macrophages (C2) expressed significantly upregulated receptor CCR1, paired with ligand CCL3 in T-NK cells (C1b) (P<0.05). Neuroendocrine epithelial cells (C6, C7) and epithelial cells (C15) expressed significantly upregulated receptor SLC7A1, paired with ligand CCL4 in T-NK cells (C1a and C1b) (P<0.05). Neuroendocrine epithelial cells (C0, C3, C6, C7) expressed significantly upregulated receptor ALCAM, paired with ligand CD6 in the whole T-NK cells (P<0.05). Macrophages (C2) expressed significantly upregulated receptor VSIR, paired with ligand CCL4L2 in T-NK cells (P<0.05) (Figure 7D,7E). These findings indicated that extensive intercellular communication remodeling occurred within the SCLC microenvironment, particularly involving T-NK cells and other immune or epithelial cell subsets. Such enhanced ligand-receptor interactions contributed to the immunoregulatory imbalance and tumor-promoting crosstalk characteristic of SCLC progression.

Discussion

SCLC is the result of multiple gene activations and multiple signaling pathway abnormalities. With the development of molecular biotechnology, some studies focus on the gene level to elucidate the occurrence and development of SCLC. Peifer et al. (33) found that in SCLC, in addition to the common gene mutations of TP53 and RB1, PTEN, CREBBP, EP300, SLIT2, MLL, COBL, and EPHA7 mutations also appeared. Rudin and Seshagiri et al. discovered significant mutations in 22 genes (34). Chan et al. discovered that high expression of PLCG2 subsets in SCLC was associated with immunosuppressive phenotype and poor prognosis (35). To demonstrate the changes in the heterogeneity and TME of SCLC, we integrated scRNA-seq, GSVA, and cell-cell interaction studies.

We used scRNA-seq to create a systematic map of SCLC heterogeneity in one patient. The cell makeup of SCLC and adjacent noncancerous tissue of this patient differs greatly. We found SCLC is mainly composed of neuroendocrine epithelial cells with high expression of ASCL1, NACM1, MLLT11 and TUBB2. ASCL1 promotes epithelial mesenchymal transformation (EMT), making neuroendocrine tumors more likely to invade and spread. Borromeo et al. found that 75% of SCLC cells depend on ASCL1 as a crucial transcription factor for their survival and growth (36). Stem cell markers of SCLC contain NCAMs (37). Meanwhile, Chimeric antigen receptor T lymphocytes that specifically target NCAM1 greatly reduce tumor burden in SCLC animal models (38). NCAM1 may be a new target for the treatment of SCLC. Neuroendocrine epithelial cells with high expression of NCAM1 may contain SCLC tumor stem cells, contributing to the development of chemo-resistance. TUBB2B can be used as a specific cell cycle related gene to evaluate the prognosis of lung adenocarcinoma (39). Overexpressing MLLT11 enhances NSCLCs’ ability to proliferate and migrate in vitro and promotes tumor growth in vivo (40). The role of TUBB2B and MLLT11 in SCLC remains to be further investigated. These findings suggest that neuroendocrine epithelial cells of tumor tissues might regulate TME in a neuroendocrine manner, which may be one of the reasons why SCLC has unique neuroendocrine symptoms. Owing to the limited sample and heterogeneous expression of key transcriptional regulators—where only a subset of cells expressed ASCL1, while NEUROD1, POU2F3, and YAP1 were rarely detected—definitive molecular subtyping was precluded. The observed ASCL1 predominance, however, aligns with an SCLC-A phenotype. Future studies with larger cohorts are warranted to validate this finding and to refine SCLC classification.

Our single-cell analysis revealed a notable upregulation of key oncogenic pathways—MYC targets, Hedgehog, TGF-β, Wnt/β-catenin, and Notch signaling—within neuroendocrine epithelial cells. This multi-pathway activation profile aligns with the concept of cellular plasticity and the presence of CSCs in SCLC. Specifically, amplification of MYC family members is a known driver of SCLC phenotypic heterogeneity and is frequently associated with non-neuroendocrine or variant states. While Hedgehog, TGF-β, and Wnt/β-catenin signaling have been consistently linked to enhanced proliferation, invasiveness, and therapy resistance in SCLC (41,42), the role of Notch signaling appears context-dependent and cell-state-specific. In contrast to its oncogenic role in NSCLC, Notch activation has been reported to suppress tumor growth in certain SCLC models, suggesting a tumor-suppressive function (43-45). However, its overexpression in our dataset, particularly within neuroendocrine cells, may reflect its alternative role in maintaining CSC populations. Indeed, Notch signaling has been implicated in CSCs’ self-renewal and plasticity in SCLC (46), and its co-activation with Wnt/β-catenin—a pathway also linked to platinum-based chemotherapy resistance (47,48)—suggests a convergent mechanism promoting stemness and therapeutic resilience. This pattern of pathway activation hints at a subpopulation of neuroendocrine cells with CSC-like properties, potentially driving tumor maintenance and adaptability. Conversely, a broad downregulation of IFN-α/γ response pathways was observed not only in neuroendocrine epithelial cells but also across most immune cell subsets within the TME. Given the critical role of IFN-α in modulating adaptive anti-tumor immunity via dendritic cells and T cells, and the direct pro-apoptotic and growth-inhibitory effects of IFN-γ on tumor cells, this suppression likely contributes to an immunosuppressive TME and facilitates immune evasion (49).

In our study, immunohistochemical analysis confirmed elevated expression of BEX1 and MAP1b in the tumor tissue of this SCLC patient. Single-cell resolution mapping further revealed their predominant localization within epithelial and neuroendocrine cell populations. This observation regarding BEX1 aligns with existing literature that implicates BEX1 in promoting proliferation and invasiveness of malignant cells, including those in lung cancer (50). Similarly, our findings on MAP1b extend previous reports that identified MAP1b as the autoantigen recognized by Purkinje cell cytoplasmic antibody type 2 (PCA-2), a well-characterized paraneoplastic biomarker associated with SCLC (51,52). Prior serological studies have suggested that MAP1B-IgG could serve as a potential marker for early SCLC detection, with one cohort study reporting that 80% of PCA-2 positive patients were subsequently diagnosed with SCLC (53,54). The novelty of our work lies in moving beyond these established associations to provide direct functional validation and cellular localization of both proteins in SCLC. Through targeted knockdown experiments, we demonstrated that suppression of BEX1 in NCI-H209 and MAP1b in NCI-H82 significantly impaired cell proliferation and migration while promoting apoptosis. This functional evidence substantially strengthens the case for their pathogenic roles in SCLC, suggesting they are not merely biomarkers but active contributors to tumor progression. Critically comparing our findings with earlier literature, we provide the first demonstration that MAP1b, previously recognized mainly as an autoantigen in paraneoplastic syndromes, possesses direct tumor-promoting functions in SCLC cells. Similarly, while BEX1 has been implicated in cancer cell proliferation (54), our study specifically establishes its functional significance in SCLC neuroendocrine cells. However, we acknowledge that our functional validation was limited to two SCLC cell lines, which may not fully represent the heterogeneity of SCLC. Future investigations employing broader model systems, including additional cell lines and patient-derived organoids, will be essential to further validate these findings and explore the therapeutic potential of targeting BEX1 and MAP1B in SCLC.

We analyzed the subtypes of T-NK cells between tumor and adjacent noncancerous tissue to fully comprehend immunity of SCLC. The pattern of immunosuppression in SCLC patient in this study is consistent with the findings of relevant studies, namely, SCLC is usually associated with higher immunosuppression, low levels of T cell infiltration, and reduced antigen presentation (55-57). C1b (CXCR2⁺CCL3⁺CX3CR1⁺NK/NK T cells) had a higher proportion in the tumor. CXCR2 is highly expressed in various types of tumors, which may be related to CXCR2 promoting the recruitment of tumor immune cells and preventing the accumulation of anti-tumor immune effector cells (58). CCL3 enhances anti-tumor immunity by promoting homing of dendritic cells in TME (59). However, a preclinical study showed that high levels of CCL3 allowed tumor cells to evade immune surveillance (60). CX3CR1, as a transmembrane protein and a chemokine, is distributed on lymphocytes with killer functions. CX3CR1 can be used to construct a TME risk model to predict lung cancer prognosis (61). Moreover, IL6 JAK STAT pathway was upregulated in C1b. JAK-STATs has a double-edged function in a wide range of cancer background studies. STAT3 and STAT5 are associated with tumor initiation and progression, but other members, notably STAT1 and STAT2, are essential for anti-tumor defense and maintenance of an effective long-term immune response through evolutionarily conserved programs (62). C1b may play a certain anti-tumor immune role by secreting integrins and chemokines, such as CCL3, CX3CR1, and CCL4L2, to recruit inflammatory cells to the tumor site.

Cells in the cellular microenvironment acquire regulatory signals from upstream pathways and then convey those signals to downstream cells, causing them to engage in the corresponding biological processes. Complex cell-cell interactions between various cell types, which are a hallmark of TME, control the growth, invasion, and metastasis of tumors. Our experimental results indicated that neuroendocrine epithelial cells and macrophages expressed high levels of adhesion molecules and chemokines, such as ALCAM, VISR, and CCR1, some of which may be closely related to T-NK cells. We also discovered that CXCR2⁺CCL3⁺CX3CR1⁺NK/NK T cells were enriched in the TME of SCLC. Based on the above results, we analyzed that the secretion of the corresponding adhesion factor (ALCAM) by neuroendocrine epithelial cells and the secretion of the corresponding chemokine (CCR1) by macrophages might act as ligands to recruit the above-mentioned immune cells to the tumor site and exert anti-tumor effects. These L-R pairs related to the upregulated genes of T-NK cells may be closely associated with the anti-tumor immune function in the TME of SCLC. The close interaction among immune cells still requires further research.

There are some limitations in this study. First, the sample size was relatively small, with only one case of SCLC tissue and matched non-cancerous tissue, which constrained the scope of our analysis. Second, the research was limited to investigating the effects of BEX1 and MAP1b knockdown in SCLC cell lines. Further validation through overexpression of these genes is required to confirm their roles in SCLC. Third, the patient’s SCLC was considered a second primary cancer; however, a potential link to the prior history of left-lung lymphoepithelial carcinoma cannot be ruled out. Therefore, additional research is needed to clarify the heterogeneity of SCLC.

Conclusions

We accurately analyzed SCLC and its TME at the single-cell transcription level from one patient. We found that the tumor tissue in this patient was mainly composed of neuroendocrine epithelial cells, and this cell population may contain SCLC tumor stem cells. Compared with the adjacent noncancerous tissues, the tumor tissue showed a state of immune decay. MAP1b and BEX1 were widely expressed in this tumor tissue, providing new targets for future treatment.

Acknowledgments

We thank the assistance of Lin Yin and Bihui Zhang from Accuramed (Guangzhou, China) Biotech Co., Ltd. in single cell transcriptome data analysis and language polishing.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1674/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1674/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1674/prf

Funding: This work was supported by the National Natural Science Foundation of China (No. 82102710) and the Guangxi Natural Science Foundation (No. 2023GXNSFBA026007).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1674/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by The First Affiliated Hospital of Guangxi Medical University’s ethics committee (No. 2023-E428-01) and informed consent was obtained from the individual participant.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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