Single-cell RNA sequencing and m6A RNA methylation sequencing and reveal cellular and molecular mechanisms of radiation combined with PD-1 blockade in NSCLC

doi:10.21203/rs.3.rs-3381071/v1

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Single-cell RNA sequencing and m6A RNA methylation sequencing and reveal cellular and molecular mechanisms of radiation combined with PD-1 blockade in NSCLC

https://doi.org/10.21203/rs.3.rs-3381071/v1

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Background: Lung cancer is the leading cause of cancer-related mortality, and more than 85% of lung cancer cases are non-small cell lung cancer (NSCLC). The heterogeneity and molecular basis of this disease remain incompletely understood.

Methods: To address this question, we have performed a single-cell RNA sequencing as well as m6A RNA methylation sequencing of matched untreated and radiation combined with PD-1 blockade NSCLC samples.

Results: A total of 21019 cells are categorized into eleven distinct cell types, including fibroblast, macrophages\monocyte, B cell, dendritic cell, endothelial cells, neutrophils, T\Natural killer (NK) cells, NK cells, and cancer stem cells (CSCs). Further analysis of the CSCs showed that radiotherapy combined with immunotherapy effectively reduced the number of CSCs and exhausted CD8+T cells, and increased the naive CD8+ T cells. Dysregulation of multiple signaling pathways, including the Wnt, is associated with lung cancer metastasis through the TCGA tumor dataset analysis. Wnt10b is a member of the Wnt family, which plays an important role in the Wnt signalling pathway. Moreover, N6-methyladenosine (m6A) sequencing has identified obesity-associated protein (FTO) as a direct target of m6A modification whose levels were regulated by Wnt10b, suggesting the FTO as a potential therapeutic target for treatment of lung cancer. Finally, our study reveals the potential pathogenesis of FTO/Wnt10b/b-catenin signaling pathway in NSCLC development.

Conclusions: Collectively, this study proves that radiation combined with PD-1 blockade inhibits the stemness of CSCs through the FTO/Wnt10b/β-catenin pathway in NSCLC, which might provide insights for cancer immunotherapies.

Single-cell RNA sequencing

m6A

Radiation combined with PD-1 blockade

NSCLC

Lung cancer is the leading cause of cancer-related mortality, and it results in 1.796 million deaths worldwide each year (18%)¹. More than 85% of lung cancer cases are non-small cell lung cancer (NSCLC), and lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC), which account for over 80% of NSCLC cases, are its two primary subtypes^2–3. In NSCLC, immune therapies have repeatedly shown therapeutic benefit and programmed death-1 (PD-1) blockade is characterized by long-lasting effectiveness that significantly improves overall survival.⁴ The majority of NSCLC patients do not, however, react to PD-Ligand 1 (PD-L1) inhibition as a single drug, and intratumoral immune infiltrates that are important in the response to these treatments are still poorly understood.⁵ To uncover the role of the distinct genetic profiles of lung cancer subtypes among patients, new research opportunities have arisen that might contribute to different therapeutic decisions.^6–7

The tumor microenvironment (TME) contains a variety of predictors of therapeutic response, such as genomic features, transcriptomic signatures, and epigenetic modifications. These factors also contribute to the heterogeneity of clinical responses among cancer subtypes and control favorable or unfavorable outcomes for tumor progression.^8–9 In addition to immune cells like macrophages, lymphocytes, monocytes, and dendritic cells (DC), the immune microenvironment also includes immune checkpoint molecules like PD-1, PD-L1, and cytotoxic T-lymphocyte antigen 4, many of which are regarded as potential biomarkers in clinical applications.¹⁰ Taking into account the complexity of the TME, the combination of multiple methods is particularly important. N6-methyladenosine (m6A) has been reported as an important mechanism of posttranscriptional regulation. To the best of our knowledge, the demethylase fat mass- and obesity-associated protein (FTO), an eraser of m6A, plays a role in cancer, however, very little is known about how these m6A regulators function in NSCLC. Single-cell RNA sequencing (scRNA-seq), which shows thorough transcriptome profiling at single-cell resolution with an unbiased catalog of cellular variety, is a potential method to deeply examine the immunological heterogeneity in the TME.¹¹

We examined the expression profiles, molecular characteristics, and prognostic significance of m6A regulators vie m6A RNA methylation sequencing in NSCLC. Additionally, we applied scRNA-seq to investigate the relationship between T cell populations and response to radiation combined with anti-PD-1 treatment in NSCLC tissue. Our findings could improve our capacity to design personalized treatment plans and provide theoretical basis for mechanistic research on NSCLC.

Cell lines and culture

Human normal lung epithelial cell lines (BEAS-2B), NSCLC cell lines (A549 and A549-DDP), and murine lung adenocarcinoma LLC (Lewis lung carcinoma) cell lines were used in this study. BEAS-2B, A549, and A549-DDP cells were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). BEAS-2B was maintained in DMEM (Gibco, Life technologies, USA). A549 and A549-DDP was maintained in F12 medium (Gibco, Life technologies, USA). All the culture medium was supplemented with 10% (v/v) fetal bovine serum (FBS, Hyclone, USA) and 1% penicillin/streptomycin (Gibco, Life technologies, USA). Culture flasks were kept at 37°C in a humid incubator with 5% CO2.

LLC-CSCs (Cancer stem cells) sorting

LLC cells were exposed to FITC-conjugated anti-CD44 mouse antibody (Stem Cell Technologies, USA) and were further labelled with dextran-coated magnetic nanoparticles using bispecific Tetrameric Antibody Complexes (TAC). These cells were subjected to immuno-magnetic cell separation and CD44 + cells were identified as pure CSCs.

Cell transfection and reagents

The overexpression and knockdown plasmids of FTO and Wnt10b were purchased from Gene Corporation (Wuhan, China). To construct the FTO and Wnt10b expression vector, the full-length open reading frame of FTO (NCBI accession number was NM_001363894.1) and Wnt10b (NCBI accession number was NM_003394.4) cDNA was cloned into the eukaryotic expression vector pcDNA3.1. The FTO and Wnt10b knockdown cDNA (FTO SS Sequence was 5’-GGGUCAUUACAGAGAUUAAAU-3’, FTO AS Sequence was 5’-UUAAUCUCUGUAAUGACCCUG-3’, Wnt10b SS Sequence was 5’-CGAAUAGACUAAGAUGAAAUG-3’, Wnt10b AS Sequence was 5’-UUUCAUCUUAGUCUAUUCGAG-3’) was cloned into pLVX-shRNA. Briefly, 4 µg of plasmids were used to transfect the LLC-CSC with Lipofectamine® 2000 (Thermo Fisher Scientific, Inc.) before they were incubated for 24 h in six-well plates. The control group was the empty vector.

In vivo tumorigenicity assay

Six-week-old male C57BL/6 mice were provided by Beijing Huafukang Biotechnology Co., Ltd. with the license number SCXK (Beijing, China) 2014-0004 Mice were housed in ventilated cages with up to four per cage in an animal barrier facility. All cages were sterilely changed weekly and supplied with hardwood bedding. All mice were maintained in a specific pathogen-free room at 22 to 26°C with a 12-h shift of light-dark schedule and fed with sterile pellet food and autoclaved water provided ad libitum. One week after adaptive feeding, the mice were inoculated with cells. The procedure of animal experiment is as follows:

Part1: A total of 1 × 104 CSCs were injected subcutaneously into mice. When the tumor volume reached 100mm³, mice were randomly divided into four groups(n = 6)–the saline group (Model), the radiotherapy group (Radio), the PD-1 inhibitor group (PD-1), and the radiation combined with PD-1 inhibitor group (Radio + PD-1). On the 7th day after CSCs implantation, 200uL of PD-1 inhibitor formulated at a concentration of 2 mg/kg in a 0.9% sodium chloride solution was intraperitoneally injected every 3 days for a total of 3 times with the control group being treated with the same volume of saline. An X-ray irradiator (X-RAD 320, Precision X-ray Inc.) was used to irradiate the tumor. Radiotherapy was performed on day 7 after CSCs implantation in Radio and Radio + PD-1. 2Gy per day for 4 days, a total of 8Gy. Body weight and tumor size were measured every 7 days. Allograft tumor size was calculated using the following formula: tumor volume (mm3) = width2 × length/2 (mm³). Mice were sacrificed on the 28th day after CSCs implantation. Blood and umor tissue were collected.

Part2: A total of 1 × 104 CSCs treated with buffer, overexpressed FTO, interfered FTO, buffer, overexpressed FTO were injected subcutaneously into mice, respectively. When the mean tumor volume reached 100mm³, mice were randomly divided into 5 groups(n = 6)–the buffer group (OE-NC), the overexpressed FTO group (OE-FTO), the interfered FTO group (si-FTO), the radiation combined with PD-1 inhibitor group (Radio + PD-1), and the radiation combined with PD-1 inhibitor and overexpressed FTO group (Radio + PD-1-FTO). The other methods are the same as in Part 1.

All of the mice were monitored every day. Once signs of morbidity were observed or the tumors exceeded 2,000 mm^{3 31}, the mice would be euthanized.

PET-CT scan

MicroPET was used and 18F-RGD PET scans were performed to assess tumor growth post-drug administration. The MicroPET protocol was as follows: 1 h prior to scanning, a single tracer 18F-RGD injection of 100 ± 20 µCi (100–200 µl; Jiangsu Institute of Nuclear Medicine, Jiangsu, China) was intravenously administered via the lateral tail vein. Mice were not required to fast. Normal eating continued following drug administration. A total of 1 h following administration, 10-min static MicroPET was performed. PET scans and image analysis were performed using an Inveonmicro PET (Siemens Healthineers, Erlangen, Germany). MicroPET data processing. Scans were reconstructed using Inveon Acquisition Workplace software (version 1.4; Siemens Healthineers), using a three-dimensional ordered-subset expectation maximization/maximum a posteriori algorithm with the following parameters: matrix, 128x128x159; pixel size, 0.86x0.86x0.8 mm, and β-value, 1.5, with uniform resolution. Acquisition time, 10 min; acquisition energy window, 350–650 keV. The Micro PET Analysis software (version ASI Pro 6.7.1.1; Siemens AG, Munich, Germany) was used to outline the brain, heart, liver, kidney and tumor tissue as the regions of interest (ROIS). The mean uptake value of radioactive material (PET units/g) of the region of interest was obtained. The radioactivity concentration (accumulation) within a tumor or an organ was obtained from mean pixel values within the multiple ROI volume, which had been converted to MBq/ml/min using a conversion factor. The conversion to MBq/g/min assumed a tissue density of 1 g/ml. Imaging ROI-derived %ID/g was calculated by dividing the ROIs by the administered activity injected dose per gram.

Preparation of single-cell suspensions

Freshly obtained resected tissues were rinsed with Hanks’ Balanced Salt Solution (HBSS) after the sacrifice, subsequently shredded on ice to smaller pieces with collagenase I/IV in HBSS, and incubated for 30 min at 37°C with manual shaking every 10 min. The digested tissues were then passed through a 70-µm nylon mesh filter, and the cell suspension were centrifuged at 500g for 5 min at 4°C. After removing the supernatant, the pelted cells were suspended in red blood lysis buffer, and next resuspended in buffer (0.04%BSA + PBS) after being washed with HBSS. Cell suspensions, after depleting dead cells through flow cytometry, were directly processed for single-cell RNA-seq, following the manufacturer’s instructions. Alternatively, cell suspensions were frozen in 20% Dimethyl Sulfoxide (DMSO) and Fetal Bovine Serum (FBS). The cDNA library was constructed within 24 h.

scRNA-seq

We mixed a single-cell suspension with 0.4% trypan blue dye at a ratio of 9:1. Cells were counted using a Countess®II Automated Cell Counter. The proportion of living cells was calculated and should exceed 90% for quality control, and the proper concentration of cells should be no less than 1000 cells/µL. We adopted short-read

long sequencing and microfluidic techniques to simultaneously analyze the transcriptome expression profile of 500–10,000 cells in each sample. PCR amplification was performed using cDNA as the template. First, the cDNA enzyme was broken into fragments of approximately 200–300 bp, and mixed with the sequencing joint P5 as well as the sequencing primer R1, which was the conventional process in traditional second-generation sequencing. Finally, PCR amplification was performed. In this way, we constructed a standard sequencing library.

The double-ended sequencing mode on the Illumina sequencing platform was utilized to conduct our high-throughput sequencing of constructed libraries. At the Read1 end, information regarding the 16 bp barcode and the 10 bp UMI was used to

quantify the cell number and expression level. At the Read2 end, the cDNA fragment served as the reference in genomic alignment to determine the gene to which the mRNA corresponded to. Libraries were sequenced on the Illumina NovaSeq 6000 platform. On average, each sample generated 200 Gb of raw data and a total of 3.9 T of data were available.

Cell subtype identification

We further clustered T cells, B cells, neutrophils, myeloid cells, fibroblasts, endothelial cells, alveolar cells, epithelial cells, and cancer cells individually. We set the resolution to 0.8 for T and B cells. For myeloid cells, endothelial cells and alveolar cells, the resolution was 0.6. For fibroblasts, neutrophils and epithelial cells, we set resolution to 0.4, 0.2, and 1.2, respectively. Within each lineage, we applied an iterative process to remove putative doublet clusters, if any, and reclustered the remaining cells. Putative doublets were identified by double positive expressions of the canonical marker genes of 11 major cell types discussed above. Within T lineage, we used the following markers for subtype identification: CD8 + exhausted T (CD8A, LAG3, and TIGIT), CD8 + effector T (CD8A, GNLY, GZMA, GZMK, GZMB, GZMH), CD4 + naïve T (CCR7, LEF1, IL7R, and SELL), CD4 + Tregs (FOXP3, IL2RA, and IKZF2), CD4 + proliferating (TOP2A, MKI67), and CD4 + Th17-like (KLRB1, RORC). Note that CD4 itself has low RNA expression levels and CD4 T cells were deducted by CD3 positive and CD8 negative. KLRC1, KLRD1, and NKG7 were used as the markers of NK cells. T cell subtypes were also predicted by singleR based on T cell annotations of public dataset GSE992549,36. Similarly, we distinguished follicular B cells (MS4A1, MHCII, CXCR4) from plasma cells (MZB1, JCHAIN, IgH) among the B cell lineage. Plasmacytoid DC (IL3RA, LILRA4, CLEC4C) was clustered in the B cell lineage. For the myeloid clusters, macrophages were positive for canonical marker CD68, and M2-like macrophage markers CD163 and MRC1/CD206. Other myeloid cell types were confirmed by specific marker genes including classical monocytes (CD14, LYZ, VCAN), cDC1 (XCR1, CLEC9A), cDC2 (FCER1A, CD1C), and mature DC (LAMP3). Within fibroblasts (DCN and COL1A1), RGS5 and CSPG4 was used to mark the pericytes. Myofibroblasts were identified by upregulation of ACTA2 and MYH11. For endothelial cells (PECAM1, VWF and ENG), tip cells are characterized by their markers and angiogenesis-related genes (KCNE3, ESM1, ANGPT2, and APLN). Vascular cells mainly consisted of VECs and AECs, respectively identified by their markers ACKR1 and GJA5. A subset of the cells expressing lymphatic markers such as PDPN and PROX1 were defined as LECs-like. Using representative markers for classical airway epithelial cell types, we identified alveolar Type 2 cells (SFTPC, SFTPA1, and ABCA3), club cells (SCGB1A1 and SCGB3A1), basal cells (KRT5, KRT6A, and KRT14), and ciliated cells (FOXJ1, TPPP3, and PIFO) for alveolar cells and other lung epithelial cells. Specific genes for cell-type identification are provided in Supplementary Table 2. For cancer clusters, clustering resolutions 0.2, 0.4, and 0.6 were all used to test the robustness of cell grouping.

scRNA-seq data preprocessing

The Cell Ranger software pipeline (version 3.1.0) provided by 10×Genomics was used to demultiplex cellular barcodes, map reads to the genome and transcriptome using the STAR aligner, and down-sample reads as required to generate normalized aggregate data across samples, producing a matrix of gene counts versus cells. We processed the unique molecular identifier (UMI) count matrix using the R package Seurat³² (version 3.0). To remove low quality cells and likely multiplet captures, which is a major concern in microdroplet-based experiments, we apply the criteria to filter out cells with UMI/gene numbers out of the limit of mean value +/- 2 fold of standard deviations assuming a Guassian distribution of each cells' UMI/gene numbers. Following visual inspection of the distribution of cells by the fraction of mitochondrial genes expressed, we further discarded low-quality cells where a certain percentage of counts belonged to mitochondrial genes. Library size normalization was performed in Seurat on the filtered matrix to obtain the normalized count.

Top variable genes across single cells were identified using the method described in Macosko et al³³. Briefly, the average expression and dispersion were calculated for each gene, genes were subsequently placed into several bins based on expression. Principal component analysis (PCA) was performed to reduce the dimensionality on the log transformed gene-barcode matrices of top variable genes. Cells were clustered based on a graph-based clustering approach, and were visualized in 2-dimension using tSNE. Likelihood ratio test that simultaneously test for changes in mean expression and in the percentage of expressed cells was used to identify significantly differentially expressed genes between clusters. Here, we use the R package SingleR³⁴, a novel computational method for unbiased cell type recognition of scRNA-seq to infer the cell of origin of each of the single cells independently and identify cell types. Differentially expressed genes (DEGs) were identified using the Seurat package. P value < 0.05 and |log2foldchange| > 1 (or |log2foldchange| > 0.58) was set as the threshold for significantly differential expression. GO enrichment and KEGG pathway enrichment analysis of DEGs were respectively performed using R based on the hypergeometric distribution.

Reverse transcription-quantitative PCR (RT‒qPCR)

Total RNA was extracted using the EasyPure RNA Kit (Cat# H30828; TransGen Biotech, Beijing, China). Single-strand cDNA was prepared from the purified RNA using oligo (dT) priming (Thermoscript RT kit; Invitrogen, Carlsbad, CA, USA) with the iQ™5 Real-Time PCR System (Bio-Rad Laboratories, Inc., USA). RT-qPCR reactions were performed according to the manufacturer's instructions. The thermocycling conditions were as follows: initial denaturation at 95°C for 30 sec; followed by 40 cycles of 95°C for 15 sec, and 60°C for 30 sec. The relative expression levels of mRNA were calculated using the 2^−ΔΔCt method ¹⁶ and normalized to that of GAPDH for mRNA. All assays were performed in triplicate and independently repeated three times. Data quantitation was performed by the relative standard curve method with those genes normalized to GAPDH mRNA levels. The sequences of primers were synthesized by Sangon (Shanghai, China) and are provided in Supplementary Material (Table S2).

RNA stability detection

Briefly, CSCs (4×10⁴) were incubated for 24 h in six-well plates. Cells from the first wells were collected up to 1ml Tri reagent and freeze at -80℃. The actinomycin D (Act-D; 10 µg/mL; Sigma-Aldrich, USA) was administrated to remaining five wells’ cells. Collect cells at 1, 2, 4, 6 and 8 h time points following actinomycin D addition and freeze the cell pellet in 1 ml of TRI Reagent as described above. Total RNA was extracted and reverse transcribed to cDNA as described before. The RNA expression of the first wells was used as a normalized. Wnt10b pre-mRNA forward primer sequences were 5’-TGAGCTCGGTGAGAGCAAAG-3’, reverse primer sequences were 5’-TTAAACCGTGGGGAGACTGC-3’. Wnt10b mature-mRNA forward primer sequences were 5’-GTGAGCGAGACCCCACTATG-3’, reverse primer sequences were 5’-CACATAGCAGCACCAGTGGA-3’.

Total m6A measurement

The total m6A content of Total RNA was determined using an m6A methylation quantification kit (EpiGentek, USA). Briefly, after total RNA was isolated and purified, the bind RNA was planted to the assay wells and cultured with the capture antibody. After that, the wells were washed, and the detection antibody and enhancer solution were added. The m6A level was detected according to the fluorescence after the wells were incubated with the fluoro developer solution.

MeRIP- qPCR

Reverse transcription was performed on 10 µl m6A PolyA + RNA from the MeRIP with the iScript cDNA synthesis kit (Bio-Rad Laboratories, Hercules, CA). After diluting cDNA two-fold, quantitative real-time PCR was performed using the ABI Prism 7900HT/FAST (Applied Biosystems, USA) and primers from Integrated DNA Technologies, Inc. (Coralville, Iowa). Primers used are listed in the follows. Primer efficiency was verified to be over 95% for all primer sets used. Quantification of mRNA from the MeRIP was carried out via 2-ΔΔct. Analysis against non-immunoprecipitated input RNA. All real-time PCR primer sets were designed so the products would span at least one intron (> 1 kb when possible), and amplification of a single product was confirmed by agarose gel visualization and/or melting curve analysis.

MTT assay

Cell viability was determined by MTT assay. After treatment, cells were incubated with the MTT solution (final concentration, 5 mg/ml) for 30 min. The dark blue formazan crystals formed in intact cells were solubilized with lysis buffer (100% ethanol) and the absorbance of samples was read at 540–595 nm with a microplate reader (Molecular Devices, Sunnylvale, CA, USA). Data were expressed as the percentage (%) of control (untreated cells).

Flow cytometry

Single cell suspensions from tumor or tumor-free tissues were obtained as described above and were incubated with FcBlock (clone 93, Biolegend) for 15min before staining with fluorescent conjugated antibodies for 45min at 4ºC. The cells were washed with staining buffer (PBS containing 0.5% BSA and 2 mM EDTA) and analyzed on a LSRII flow cytometer (BD). 7AAD was used to exclude dead cells and doublet cells were removed based on their forward/side scatter properties.

Apoptosis analysis

An annexin V-FITC/PI apoptosis detection kit (Hangzhou Lianke Biotechnology Co., Ltd., China) was used to measure the apoptotic rate of cells according to the protocols of the manufacturer. Briefly, 4×10⁴ cells were resuspended in 5 µl of annexin of V-FITC and 10 µl of PI. The cells were then incubated in the dark at room temperature for 15 min and analyzed by flow cytometry (BD FACSCantoII™; BD Biosciences) within 1 h.

Cell-cycle analysis

In brief, 1×104 cells were collected to fix in 75% ethanol overnight at 4℃. Then, the cells were collected by centrifugation, washed once with PBS, resuspended with 1 ml DNA Staining Solution (Hangzhou Lianke Biotechnology Co., Ltd., China) for 30 min in the dark. Next, the processing cells were measured for cell-cycle distribution via a flow cytometer (Beckman Coulter).

TUNEL assay

The apoptosis level of tendon tissue was detected by using the TUNEL kit according to the manufacturer's instructions (Boster, China). Frozen sections were fixed with 4% paraformaldehyde for 1 h, then digested by proteinase K for 10 min. Subsequently, frozen sections were incubated successively with TdT and BIO-d-UTP. Finally, blocking solution and SABC were added to connect the fluorescent radical group. Images were captured using a laser confocal microscope (Olympus, Japan).

Colony formation assay

For anchorage-independent growth, 1×104 cells were suspended per ml in 2 ml of 0.3% agar with 1% N2 Supplement (Invitrogen), 2% B27 Supplement (Invitrogen), 20 ng/ml human platelet growth factor (Sigma-Aldrich), 100 ng/ml epidermal growth factor (Invitrogen) and 1% antibiotic-antimycotic (Invitrogen) overlaid into 6-well plates containing a 0.5% agar base. Formed colonies were stained with 2% crystal violet on day 21, then colonies ≥ 0.2mm in diameter were observed and counted under microscope.

Tumor sphere formation assay

A total of 1 × 104 cells were seeded per well in ultra-low adhesion 6-well plates, containing 2mL warm StemXVivo serum-free tumor sphere media (CCM012, R&D Systems, Minneapolis, MN, USA) supplemented with 2 U/mL heparin (Sigma) and 0.5 µg/mL hydrocortisone (Sigma) following the manufacturer’s protocol, and cells incubated in 5% CO2 incubator, at 37°C for 7–10 days. Tumor spheres were then observed under microscope and those larger than 50 µm counted.

Hematoxylin-eosin (H&E) staining

The Allograft tumor tissues were collected for histological examination. The tumour tissues were fixed in 4% paraformaldehyde and embedded in paraffin. The slices (5 µm thickness) were stained with hematoxylin and eosin. The cancer embolus area was observed under an Olympus BX51 microscope.

Immunofluorescence staining

Briefly, tissue sections were permeabilized with 0.2% Triton X-100 for 15 min, blocked with 3% BSA for 1 h. The slides were incubated with anti-CD133 antibody (1:100 dilution of ab19898, Abcam, UK, or 1:100 dilution of MAB4310, Sigma-Aldrich, USA), anti-CD8 antibody (1:100 dilution of 53 − 6.7, Novusbio, USA), or anti-Wnt10b antibody (1:200 dilution of NBP2-49165, Novusbio, USA) overnight at 4°C. Subsequently, slides were incubated with the appropriate secondary antibodies (1:500 dilution of ab150077 or ab150167 or ab150157, Abcam, UK) for 2 h at room temperature after washing with PBS. DAPI (Solarbio) was used to stain the cell nuclei. Images were then obtained using an Olympus BX51 fluorescence microscope (Olympus Corporation).

Western blot assay

To determine the level of indicated proteins, tumor tissue and cells were lysed with RIPA peptide lysis buffer (Thermo Fisher Scientific, Inc., USA) containing 1% protease inhibitors (Pierce). The protein concentration was determined using a BCA protein assay kit (Thermo Fisher Scientific, MA, USA). Equal amounts (30 mg) of proteins were loaded, the PVDF membranes with transferred proteins were incubated with primary antibodies at 4°C overnight and HRP conjugated secondary antibodies at room temperature for 2h. Antibody information is shown in Supplementary Material (Table S2). The signal was developed by the enhanced chemiluminescence (ECL) reagent (Millipore, Bedford, MA, USA) and visualized by NIH ImageJ (Scion, Frederick, MD) for each group and then normalized with glyceraldehyde-3-phosphate dehydrogenase (GAPDH).

Bioinformatics assay

All datasets used in this study were available to the public. The expression of miRNAs, mRNAs in Gene Expression Omnibus (GEO) and TCGA dataset were obtained from the GEO website, TCGA official website and StarBase, the datasets were analyzed by GEO2R. The survival analysis was performed according to the GEPIA website and Kaplan-Meier Plotter. Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analysis was performed using the GSEA software.

Statistical analysis

The detailed statistical analysis has been described in figure legends. All data are expressed as the mean ± standard deviation (SD). Statistical comparisons between the two groups were made using a Student’s t test, and multiple group comparisons were analyzed by one-way ANOVA. Significant differences are indicated in the figures as follows: *P < 0.05, **P < 0.01, and ***P < 0.001. P values of < 0.05 are considered to represent a statistically significant difference. ns stands for no significance.

Radiation combined with PD-1 blockade has significant anti-tumor effects.

To verify the roles of radiation combined with PD-1 blockade on progress of NSCLC, mice bearing xenografts were implanted by subcutaneously injecting LLC derived CSCs. We used flow cytometry to isolate CSCs from LLC by related markers- CD44 and CD133 (Fig. 1A). The tumors were monitored and were isolated. As shown in Fig. 1B and 1C, the tumor sizes were smallest in Radio + anti-PD-1. PET-CT results showed that compared with the other three groups, the Radio + anti-PD-1 had the lowest tumor metabolic value (Fig. 1D). H&E staining showed that the necrotic area of tumor in Radio + anti-PD-1 was much smaller, even if there were some decreases in Radio group (Fig. 1E). Consistent with H&E results, the apoptosis in Radio + anti-PD-1 was the highest compared with the other three groups (Fig. 1F). These results indicated that radiation combined with PD-1 blockade inhibited NSCLC effectively. To investigate the effect of radiotherapy with or without PD-1 inhibitor on immune microenvironment of NSCLC, we examined CD3 and CD8 positive cells in tumor tissue. The results showed that the number of CD3 + and CD8 + cells increased significantly in Radio + PD-1 (Fig. 1G). Then, peripheral blood T cells in each group were sorted, IFN-γ, CD44, IL-4, and CD28 in each group were detected by qRT-PCR to evaluate the activation status. The result showed that treated with radiotherapy or PD-1 blockade or both promoted IFN-γ, CD44, and CD28 levels (P < 0.05), while radiation combined with PD-1 blockade displayed the most significant increase (Fig. 1H). Results As shown in Fig. 1I, the number of CD4 and CD8 positive T cell, Treg and NK cell were increased after radiotherapy or PD-1 treatment alone or both.

Radiation combined with PD-1 alters the cell types and phenotype of CSCs in NSCLC by single-cell transcriptomics.

To explore intratumor heterogeneity in NSCLC, we used nanopores to capture single cells through the BD Rhapsody platform to generate matched single-cell transcriptome profiles of Model and Radio + PD-1 from two animals (Fig. 2A). After initial quality control, single-cell transcriptomes were available for a total of 21019 cells for further analysis. By analyzing the variably-expressed genes in all cells, we identified the eleven major cell types in the nineteen cell clusters by UMAP. These cells included fibroblast (cluster 1, 8, 13, 16, and 18), macrophages\monocyte (cluster 3, 5, and 17), B cell (cluster 9,19), DC (cluster 11), endothelial cells (cluster 12), neutrophils (cluster 15), T\NK cell (cluster 7), NK (cluster 14), and cancer stem cells (cluster 2, 4, 6, and 10). We then performed differential gene expression analysis to determine the identity of these cell clusters. Each cluster was compared with other aggregated clusters to find unique gene signatures, and the top 10 significantly differentially expressed genes for each cluster were shown in the heat map, as shown in Fig. 2B. The eleven cell types in Model and Radio + PD-1 were displayed by UMAP plot and Violin plots (Fig. 2C-E). Therefore, these results reveal that NSCLC is highly heterogeneous and composed of diverse cell types. To investigate the effects of radiation combined with PD-1 blockade on CSCs, we performed a cluster analysis of stem cells. Compared with the Model, radiation combined with PD-1 blockade significantly reduced the number of CSCs (P < 0.05) (Fig. 2F). IF showed that compared with the Model group, the enrichment of CD133 + cells (CSCs) were slightly lower in anti-PD-1 or Radio group and this enrichment was lowest in Radio + anti-PD-1 group (Fig. 2G). In addition, we also examined the CSC phenotype. RT-qPCR and Western blot were performed the detect the levels of OCT4, SOX2, and β-catenin in lung cancer tissues after radiation combined with or without PD-1 blockade. The results showed that the inhibition of OCT4 and SOX2 was most significant in Radio + anti-PD-1 group (P < 0.05) (Fig. 2H-J).

Radiation combined with PD-1 affects T-cell subgroup.

To depict their intrinsic portrait, these T cells (cluster 7) were further divided into six subclusters (Fig. 3A). Unique gene signatures and the top 10 SDE genes were outlined for each T-cell subgroup (Table S3 and Fig. 3B). Cell clusters were specifically classified according to different molecular features of T cells among tissue-stratified clonotypes (Fig. 3C). The UMAP plot displays seven subclusters of T cells (Fig. 3D) and compared in Model and Radio + PD-1(Fig. 3E). The ratio of different subclusters of T types (Fig. 3F) and statistical analysis (Fig. 3G) were carried out in Model and Radio + PD-1. The results showed that the proportion of each T cell subtype was significantly changed in the Radio + PD-1 group.

Differentiation status of CD8 + T cell and its role in cell communication.

The tumor cells were stratified during cancer development using pseudo-time reconstruction (Fig. 4A). Trajectories that predicted the path of de novo tumor progression showed T-cell distributions in two animals. The population of exhausted CD8 + T cells in Radio + PD-1 group decreased while the population of naive CD8 + T cells increased along the trajectory, validating the reliability of the trajectory analysis (Fig. 4B). Our data showed dynamic gene expression profiles during the malignant evolution of tumor cells (Fig. 4C). To investigate the relationship between different CD8 + T cell subtypes and their relationship with CSCs, we analyzed the intercellular communication network. The network diagram of the strength of cell-to-cell interaction showed that different subtypes of CD8 + T cells could convert to each other and were all affected by CSCs (Fig. 4D, E).

Radiation combined with PD-1 blockade increases m6A methylation level and targets on Wnt10b in lung cancer tissues.

As RNA m6A methylation modification plays critical roles in pathological and physiological processes, we hypothesized that RNA m6A methylation modification also existed in the therapy of lung cancer. To verify this, tumor tissues of the Model and Radio + PD-1 were collected for m6A sequencing. The results showed that m6A peaks increased in Radio + PD-1 compared with the Model group (Fig. 5A). Next, we extracted the differentially expressed genes from m6A-seq and RNA-seq and classified the genes according to hyper methylation and down transcription (Hyper-down), hyper methylation and up transcription (Hyper-up), hypo methylation and down transcription (Hypo-down), hypo methylation and up transcription (Hypo-up). The proportion of Hyper-down was even higher than the other three groups, which were displayed in a four-quadrant diagram (Fig. 5B). By analysis of the frequency of m6A-peak in CDS region and 3’UTR region in the Model and Radio + PD-1, it was found that the frequency of m6A-peak in 3’UTR region in the RT + PD-1 group was significantly increased (Fig. 5C). Next, the transcriptome sequencing data were further extracted, and the differentially expressed genes between the two groups were extracted with P-vale < 0.05 and log2 (fold change) > 0.58. The data were displayed by volcano diagram (Fig. 5D). There were significantly up-regulated and down-regulated genes. Followed by m6A - seq data extraction, screening P - Vale < 0.05 and | log2 (a fold change) | > 0.58 the differences between the two groups of m6A modified genes for data extraction, and through the volcano's figure display, the result is shown in Fig. 5E.

To determine the major gene in the m6A methylation, we extracted the differentially expressed genes with highly m6A methylation and transcription down. A total of 34 differential genes with increased m6A modification and decreased transcription levels were selected (Amica1, Angptl7, Ankrd35, Arhgap27, Batf2, Bend6, Camk2b, Chga, Doc2a, Efnb1, En2, Evl, Fat4, Flrt2, Gfi1, Gjb3, Gm40447, Jph2, Jsrp1, Kif26b, Lysmd1, Mmp28, Mmrn2, Nlrc5, Nrg2, Palmd, Plxna2, Ptger4, Rgs1, Runx3, Sectm1a, Tnnt 1, Wnt10b, Xirp1), and the m6A data of the above differential genes were extracted, and it was found that the m6A of Wnt10b was significantly increased (Fig. 5F, G). Given that methylation level increased in lung cancer tissues of Radio + anti-PD-1 group, we collected tumor tissues to explore the methylation level of Wnt10b by MeRIP-RT-qPCR. The results showed that compared with Model, the methylation levels of Wnt10b in the Radio group, PD-1 group, and Radio + anti-PD-1 group were all increased (P < 0.05), and the Radio + anti-PD-1 group had the highest methylation level of Wnt10b (Fig. 5H).

Additionally, ingenuity Pathway Analysis (IPA)-performed GO analysis of transcripts showed Wnt10b The top five pathways (by P value) are shown in Fig. 5I. Wnt10b was predicted to be highly expressed in tumors, especially in lung cancer, in the TCGA database (Supplementary Fig. S1). Western blot and IF verified that after treating with radiation combined with PD-1 blockade, the protein expression of Wnt10b decreased most compared with Model, anti-PD-1 or Radio group (Fig. 5J, K, O). To further investigate the role of Wnt10b in CSCs, we knocked down Wnt10B expression. The results showed that the inhibition of OCT4 and SOX2 was significant in sh-Wnt10b group (P < 0.05). Meanwhile, β-catenin showed the same trend as OCT4 and SOX2 in vitro experiments (Fig. 5L-N). Next, we explored tumor cell apoptosis in vivo. TUNEL assay result showed that cell apoptosis increased after Wnt10b interference, and the apoptosis was more obvious after combined with PD-1 inhibition (Fig. 5P). These results showed that Wnt10b is a major gene involved in CSC proliferation and apoptosis.

FTO is involved in CSC proliferation and apoptosis.

To find the key regulatory molecules responsible for radiation combined with PD-1 blockade, we performed RNA-seq to detect the differential expression genes in Model and Radio + anti-PD-1 groups. The results showed that the gene expression of FTO was significantly inhibited in Radio + anti-PD-1 group (Fig. 6A). Western blot and RT-qPCR were used to detect the expression of Methylases (METTL3, METTL14, WTAP), demethylases (FTO, Alkbh5), reader recognition proteins (YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2) and Wnt5b. The results showed that the expression of methylases (METTL3, METTL14, WTAP) was increased (P < 0.05), while the expression of demethylases (FTO, Alkbh5) was decreased (P < 0.05) in the tumor-bearing mice after radiation combined with PD-1 blockade (Fig. 6B-D). The results showed that the methylation level of cancer mice might increase after radiation combined with PD-1 blockade, and the difference of the expression of the marker demethylase FTO was the most obvious.

In order to verify the existence of mRNA base modification behavior during treatment, we used colorimetric method to test the m6A level in the tissues of each group and compared the m6A modification status among the groups. Results As shown in Fig. 6E, compared with the Model group, tissue methylation occurred in both the Radio group and PD-1 group, and the overall degree was not high, and the methylation level was significantly increased after combined treatment (P < 0.05). In TCGA tumor dataset, expression of FTO in lung cancer was highly expressed (Supplementary Fig. S2). Analysis combined with Kaplan-Meier survival analysis and TCGA dataset were performed found that five-year survival rate was much higher in patients with low level of FTO compared with those with high level of FTO (Supplementary Fig. S3). These findings indicated that FTO expression was associated with patient survival and might contribute to cancer progression. To evaluate the expression of FTO in different cell line of lung tissue, we first detected the relative expression level of FTO in lung cancer cells (LLC, LLC-CSCs, A549 and A549-CSCs) and human lung bronchial epithelial cells (BEAS-2B). Compared with BEAS-2B cells, FTO expression was higher in lung cancer cells (LLC, LLC-CSCs, A549 and A549-CSCs) and those in LLC-CSCs was the highest (Fig. 6F). Thus, LLC-CSCs might be the most effective target for FTO which was the main target in this study.

As FTO was highly expressed in CSCs and associated with poorly patient survival, we investigated the roles of FTO in NSCLC. We synthetized shRNA to knock down FTO which was knocking down FTO expression effectively in LLC-CSCs (Fig. 6G, H). MTT showed that knocking down FTO expression inhibited cell viability of LLC-CSCs (Supplementary Fig. S4). Colony formation assay showed that compared with the control group, sh-FTO inhibited colony formation of LLC-CSCs (Fig. 6I). we also found that knocking down of FTO promoted cells apoptosis and cell cycle arrest through Tunel and flow cytometry (Fig. 6J-L). In addition, we found that sh-FTO inhibited sphere formation of LLC-CSCs (Fig. 7M). These results indicated that FTO is involved in CSC proliferation and apoptosis.

To further verify the role of FTO on tumorigenesis, CSCs transfected with FTO virus were inoculated to establish an animal model. The tumor formation and tumor size of the animals are shown in Fig. 6N and 6O. The tumor sizes were smaller in Radio + anti-PD-1 and siFTO. The TUNEL assay showed that apoptosis was the highest in Radio + anti-PD-1 and siFTO compared with the other two groups (Fig. 6P). In addition, IF results showed that CD8 + T cells increased significantly in Radio + PD-1 (Fig. 6Q). While CD133 + cell significantly increased in OE-FTO group and significantly decreased in Radio + anti-PD-1 group (Fig. 6Q). To further evaluate the effect of FTO on T cells activation, we performed flow cytometry to analyzed CD4+, CD8+, Treg, and NK cells after FTO interference or overexpression. The results showed that CD8 + T cells decreased significantly in OE-FTO group, whereas cells increased significantly in siFTO group and Radio + anti-PD-1 group (Fig. 6R). These findings demonstrated that radiation combined with PD-1 blockade has significant anti-tumor effects by downregulating FTO expression. Additionally, we explored how FTO regulates CSC stemness. RT-qPCR and Western blot results showed that the inhibition of OCT4 and SOX2 in siFTO group and Radio + anti-PD-1 group (P < 0.05) (Fig. 9K-M). Meanwhile, β-catenin showed the same trend as OCT4 and SOX2 in vivo and in vitro experiments (Fig. 6S-U). These findings demonstrated that radiation combined with or without PD-1 blockade regulated CSC stemness through downregulating FTO.

Wnt10b promotes FTO RNA methylation after treating with radiation combined with PD-1 blockade.

In addition, we found that the methylation level of Wnt10b was significantly increased in the CSCs-SH-FTO group (Fig. 7A). Further, knocking down of FTO led to a decrease both in pre-mRNA- Wnt10b and mature mRNA- Wnt10b levels (Fig. 7B, C). In addition, we verified this by examining tumor tissue in animals. FTO interference vector was constructed, and the expression levels of FTO and Wnt10b in tumor tissues were detected by WB and qPCR. The results showed that the transcription and translation levels of Wnt10b was decreased after interfering with FTO, further confirming that FTO inhibited Wnt10b expression (P < 0.05) (Fig. 7D. E). These results showed that the expression of FTO and Wnt10b was positively correlated. These results indicated that FTO could binding Wnt10b m6A to inhibit its translation.

NSCLC is one of the leading causes of cancer-related mortality worldwide. More than 60% of the patients have unresectable disease when they are diagnosed. The standard treatment for unresectable locally advanced NSCLC is radiation therapy (RT) combined with concomitant chemotherapy^12–13. In contrast with bulk sequencing, which averages the signals, scRNA-seq is uniquely capable of interrogating specific cellular subpopulations. However, in lung cancer, the immune landscape at single-cell resolution remains largely unknown, let alone the details of the distinct immune atlases between untreated and Radio + PD-1 blockade treatment. To bridge this gap, in this study, we first sought to provide an immune cellular atlas for lung cancer with a comparison between Model and Radio + PD-1 blockade treatment. We conducted scRNA-seq on 2 samples of untreated tumor and adjacent treated tissues from 2 NSCLC animals, comprehensively characterized the transcriptomic features of immune cells, decoded dynamic changes in cell percentage, and identified the heterogeneity of cell subtypes and intercellular interactions between untreated and Radio + PD-1 blockade treatment. Our results show that radiation combined with PD-1 blockade has an antitumor effect by reducing the number of CSCs and increasing the naive CD8 + T cells. The current study could help explain the difference in the two group through the insights into cellular composition, states and dynamics in the immune microenvironment, and paved the way for the development of future therapeutic targets for the lung cancer clinical workflow. Our findings will help guide future studies of NSCLC pathogenesis and treatment. However, due to the inherent genetic and immunogenicity diversity, we need to include a larger sample size for further validation to eliminate the heterogeneity generated by different individuals.

The existence of lung CSCs has been demonstrated by multiple research teams based mainly on biomarkers expression alone or in combination with functional assays. However, the different lung CSC types provides a great challenge for developing effective targeted therapy. In this study, we used chemotherapy and radiotherapy in combination or alone to treat NSCLC, and compared their effects on CSC markers to further explore the therapeutic mechanism of chemoradiotherapy. Previous studies have shown that OCT4 and SOX2 are involved in NSCLC as markers of stem cell properties of CSCs. Our studies showed also radiation combine with PD-1 blockade inhibited the expression of OCT4 and SOX2, suggesting that PD-1 blockade combined with radiotherapy acts as an antitumor role by inhibiting stemness. In addition, considering the principle of PD-1 inhibitor, we also examined the differentiation of immune cells. The results suggest that chemoradiotherapy can activate T cells, especially naive CD8 + T cells, which may be closely related to the regulation of tumor microenvironment.

In contrast to epigenetic modifications that regulate gene expression at the transcriptional level, such as DNA methylation and histone acetylation, the dissecting mechanism of m6A modification on RNA has greatly improved the understanding of gene regulation at the post-transcriptional level in eukaryotic cell¹⁴. Dynamic m6A modification of mRNA has been shown to be necessary for retaining cell growth, metabolism and differentiation^15–16. Tumor cells maintain their self-renewal and proliferation under different conditions by orchestrating abnormal m6A modifications of specific mRNAs^17–19. Previous studies have linked m6A modifications to the development of lung cancer²⁰. In this study, we also found that chemoradiotherapy increased the total m6A methylation level.

The Wnt signaling pathway regulates a variety of biological processes in the development of cells and the maintenance of tissues²¹. The Leder group originally identified Wnt10b in the mouse mammary gland in 1995²². Since then, it has been shown that Wnt10 plays crucial functions in a variety of tissues both during normal development (such as bone, adipocytes, teeth, skin, hair, immune system, muscle, placenta, and heart) and during illnesses (such as cancer, obesity, osteoporosis, and splithand/foot malformation (SHFM)). Relevant studies have also indicated that Wnt10b plays an important role in the occurrence and development of tumors, CSCs and immune regulation^23–24. Researches showed that Wnt10b stabilized and activated β-catenin, and increased cytoplasmic β-catenin then translocated into the nucleus where it banded to members of the T cell factor/lymphoid enhancer factor (TCF/LEF) transcription factor family to drive transcription of Wnt/β-catenin target genes^25–26. In colorectal cancer studies, overexpression of miR-148a can inhibit the expression of Wnt10b and the activity of β-catenin signaling pathway in tumor tissues, thereby inhibiting the expression of CSCs markers and improving chemotherapy sensitivity²⁷. However, the role of Wnt10b/β-catenin in NSCLC is unclear. Through m6A sequencing, transcriptomics and single-cell sequencing technology, we found a high copy number of Wnt10b gene in tumor samples, and Wnt10b was negatively correlated with CD8 + T cell enrichment.

we conducted multi-level and multi-angle studies from cells, tissues and clinical data to verify from different dimensions that radiotherapy combined with immunotherapy can modify the methylation of lung cancer Wnt10b and positively regulate CD8 + T cell enrichment.

FTO was first identified to be a tumor-promoting factor in acute myeloid leukemia as an m6A demethylase. FTO has since been widely reported to function as an oncogene in a variety of cancer types, including breast cancer, prostate cancer and cervical cancer²⁸. FTO mediates the m6A demethylation of mRNA, and regulates the stability of the m6A-modified RNA by cooperating with the m6A reader^29–30. In this work, we found both transcription and translation levels of FTO were increased in tumor tissues, indicating that gene amplification and m6A modification may work together mediating the up-regulation of FTO. We performed in vitro cell experiments and confirmed that FTO promotes the proliferation, differentiation and pellet formation of CSC and participates in tumor progression. According to the above results, chemoradiotherapy plays an anticancer role by regulating FTO to increase m6A methylation of mRNA and activate T cells. But the exact mechanism is unclear. In our study, MeRIP assay revealed that the m6A modified Wnt10b transcript increased in CSCs-SH-FTO cells. Additionally, we found that the half-life of pre-mRNA of Wnt10b became shorter after FTO interference, and the mature RNA was more obvious. This suggests that FTO plays a regulatory role in the transcriptional modification of Wnt10b. So, we speculate Wnt10b is a novel downstream target of FTO. We also observed that in functional rescue experiments, down-regulation of FTO almost completely alleviated the proliferation and tumor growth of NSCLC induced by radiotherapy plus PD-1 immunotherapy, while up-regulation FTO promoted the growth of NSCLCs and attenuated the effects of chemoradiotherapy in vivo. This suggests that FTO may not be the only upstream regulator of Wnt10b in NSCLCs, which needs to be further explored. To our knowledge, this is the first report to study the relationship between demethylase FTO and the efficacy of chemoradiotherapy, expanding our understanding of m6A methylation participating in the pathogenesis of NSCLC (Fig. 8).

In conclusion, our findings demonstrate that radiation combined with PD-1 blockade changes the stemness of CSCs and affects the proliferation of CSCs through the FTO/Wnt10b/β-catenin pathway, thereby effectively delaying tumor progression in NSCLC.

NSCLC	Non-small cell lung cancer
LUAD	Lung adenocarcinoma
LUSC	Lung squamous carcinoma
PD-1	Programmed death-1
PD-L1	PD-Ligand 1
TME	Tumor microenvironment
DC	Dendritic cells
m6A	N6-methyladenosine
FTO	Fat mass- and obesity-associated protein
scRNA-seq	Single-cell RNA sequencing
LLC	Lewis lung carcinoma
CSCs	Cancer stem cells
RT	Radiation therapy

Acknowledgements

Not applicable

Authors’ contributions

H.Z. ,L.K.and Z.W. designed the experiments. H.Y. and H.H. performed experiments. T.D. and H.G. provided materials and technical support. H.L., W.Z. and H.Y. performed experiments involved in virus infection. L.S. and J.H. analyzed the data. H.Y. and H.H. wrote the manuscript. All authors have read and approved the final manuscript.

Funding

This study was supported by National Natural Science Foundation of China (81860534, 82074144, 82260481); Natural Science Foundation of Inner Mongolia (2021MS08152, 2022MS08074, 2021JQ09); Inner Mongolia Autonomous Region science and technology planning project (201802152, 2019GG039 , 2019GG086, 2021GG0167); Scientific and Technological Innovative Research Team for Inner Mongolia Medical University of Transformation application of organoid in medical and industrial interdiscipline (YKD2022TD002); Major Project of Inner Mongolia Medical University (YKD2022ZD002); Radiotherapy discipline construction project of National Cancer Regional Medical Center for Inner Mongolia Medical University (YKD2022XK009); Radiobiology system and team construction of radiotherapy for Inner Mongolia Medical University (YKD2022XK014); The Zhi Yuan Talent Projects of Inner Mongolia Medical University (ZY0202011, ZY0130014).

Data Availability

The authors declare that all data supporting the findings of this study are available within the article and supplementary material.

Competing interests

All authors have read the journal’s policy on disclosure of potential conflicts of interest and declared no conflict of interest.

Ethics approval and consent to participate

This study has been approved by the Animal Research Committee at Mongolia Medical University, with ethical approval number YKD202002012, and all experimental protocols were conducted in accordance with institutional guidelines.

Consent for publication

Not applicable.

Author details

¹Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, Huhhot 010020, Inner Mongolia Autonomous Region, China;

²Key Laboratoy of Radiation Physics and Biology of Inner Mongolia Medical University, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, Huhhot 010020, Inner Mongolia Autonomous Region, China;

³Department of Pediatric Hematology and Oncology, Inner Mongolia Autonomous Region People’s Hospital, Huhhot 010020, Inner Mongolia Autonomous Region, China;

⁴Department of Abdominal Surgery, Affiliated Hospital of Inner Mongolia Medical University, Huhhot 010020, Inner Mongolia Autonomous Region, China;

⁵Department of Radiotherapy,Affiliated Hospital of Hebei University,212 East Yuhua Road,Baoding,071000 Hebei China.

⁶The Laboratory for Tumor Molecular Diagnosis, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, Huhhot 010020, Inner Mongolia Autonomous Region, China.

Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Ca. Cancer. J. Clin. 71, 209-249 (2021).
Shi, J.F. et al. Clinical characteristics and medical service utilization of lung cancer in China, 2005-2014: Overall design and results from a multicenter retrospective epidemiologic survey. Lung Cancer 128, 91-100 (2019).
Thai, A.A., Solomon, B.J., Sequist, L.V., Gainor, J.F. & Heist, R.S. Lung cancer. Lancet 398, 535-554 (2021).
Grant, M.J., Herbst, R.S. & Goldberg, S.B. Selecting the optimal immunotherapy regimen in driver-negative metastatic NSCLC. Nat. Rev. Clin. Oncol. 18, 625-644 (2021).
Camidge, D.R., Doebele, R.C. & Kerr, K.M. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat. Rev. Clin. Oncol. 16, 341-355 (2019).
Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519-525 (2012).
Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543-550 (2014).
Quail, D.F. & Joyce, J.A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423-1437 (2013).
Lei, Y. et al. Applications of single-cell sequencing in cancer research: progress and perspectives. J. Hematol. Oncol. 14, 91 (2021).
Ribas, A. & Wolchok, J.D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350-1355 (2018).
Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277-1289 (2018).
Auperin, A. et al. Meta-analysis of concomitant versus sequential radiochemotherapy in locally advanced non-small-cell lung cancer. J. Clin. Oncol. 28, 2181-2190 (2010).
Vansteenkiste, J. et al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 24 Suppl 6, vi89-vi98 (2013).
Frye, M., Harada, B.T., Behm, M. & He, C. RNA modifications modulate gene expression during development. Science 361, 1346-1349 (2018).
Ries, R.J. et al. m(6)A enhances the phase separation potential of mRNA. Nature 571, 424-428 (2019).
Zhang, C. et al. m(6)A modulates haematopoietic stem and progenitor cell specification. Nature 549, 273-276 (2017).
Vu, L.P., Cheng, Y. & Kharas, M.G. The Biology of m(6)A RNA Methylation in Normal and Malignant Hematopoiesis. Cancer Discov. 9, 25-33 (2019).
Liu, J. et al. m(6)A mRNA methylation regulates AKT activity to promote the proliferation and tumorigenicity of endometrial cancer. Nat. Cell Biol. 20, 1074-1083 (2018).
Barbieri, I. et al. Promoter-bound METTL3 maintains myeloid leukaemia by m(6)A-dependent translation control. Nature 552, 126-131 (2017).
Qian, X. et al. LCAT3, a novel m6A-regulated long non-coding RNA, plays an oncogenic role in lung cancer via binding with FUBP1 to activate c-MYC. J. Hematol. Oncol. 14, 112 (2021).
Logan, C.Y. & Nusse, R. The Wnt signaling pathway in development and disease. Annu. Rev. Cell Dev.Biol. 20, 781-810 (2004).
Lee, F.S., Lane, T.F., Kuo, A., Shackleford, G.M. & Leder, P. Insertional mutagenesis identifies a member of the Wnt gene family as a candidate oncogene in the mammary epithelium of int-2/Fgf-3 transgenic mice. Proc. Natl. Acad. Sci. U. S. A. 92, 2268-2272 (1995).
Perkins, R.S., Singh, R., Abell, A.N., Krum, S.A. & Miranda-Carboni, G.A. The role of WNT10B in physiology and disease: A 10-year update. Front. Cell. Dev. Biol. 11, 1120365 (2023).
Madueke, I. et al. The role of WNT10B in normal prostate gland development and prostate cancer. Prostate 79, 1692-1704 (2019).
Abiola, M. et al. Activation of Wnt/beta-catenin signaling increases insulin sensitivity through a reciprocal regulation of Wnt10b and SREBP-1c in skeletal muscle cells. Plos One 4, e8509 (2009).
Lee, J.G. & Heur, M. WNT10B enhances proliferation through beta-catenin and RAC1 GTPase in human corneal endothelial cells. J. Biol. Chem. 290, 26752-26764 (2015).
Shi, L., Xi, J., Xu, X., Peng, B. & Zhang, B. MiR-148a suppressed cell invasion and migration via targeting WNT10b and modulating beta-catenin signaling in cisplatin-resistant colorectal cancer cells. Biomed. Pharmacother. 109, 902-909 (2019).
Yang, S. et al. m(6)A mRNA demethylase FTO regulates melanoma tumorigenicity and response to anti-PD-1 blockade. Nat. Commun. 10, 2782 (2019).
Su, R. et al. Targeting FTO Suppresses Cancer Stem Cell Maintenance and Immune Evasion. Cancer Cell 38, 79-96 (2020).
Tao, L. et al. FTO modifies the m6A level of MALAT and promotes bladder cancer progression. Clin. Transl. Med. 11, e310 (2021).
Lu, C.S. et al. Oct4 promotes M2 macrophage polarization through upregulation of macrophage colony-stimulating factor in lung cancer. J. Hematol. Oncol. 13, 62 (2020).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411-420 (2018).
Macosko, E.Z. et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 161, 1202-1214 (2015).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163-172 (2019).

No competing interests reported.

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Single-cell RNA sequencing and m6A RNA methylation sequencing and reveal cellular and molecular mechanisms of radiation combined with PD-1 blockade in NSCLC

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Abstract

Figures

Background

Materials and methods

Cell lines and culture

LLC-CSCs (Cancer stem cells) sorting

Cell transfection and reagents

In vivo tumorigenicity assay

PET-CT scan

Preparation of single-cell suspensions

scRNA-seq

Cell subtype identification

scRNA-seq data preprocessing

RNA stability detection

Total m6A measurement

MeRIP- qPCR

MTT assay

Flow cytometry

Apoptosis analysis

Cell-cycle analysis

TUNEL assay

Colony formation assay

Tumor sphere formation assay

Hematoxylin-eosin (H&E) staining

Western blot assay

Bioinformatics assay

Statistical analysis

Results

Discussion

Abbreviations

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References

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