FCRLB-mediated dual control of tumor metabolism and macrophage polarization promotes lung cancer malignancy

FCRLB-mediated dual control of tumor metabolism and macrophage polarization promotes lung cancer malignancy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article FCRLB-mediated dual control of tumor metabolism and macrophage polarization promotes lung cancer malignancy Nueraili Maihemuti, Yueli Shi, Sujing Jiang, Pan Liu, Zhen Shi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7784814/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 5 You are reading this latest preprint version Abstract Background Anti–PD-1 therapy has improved outcomes in non-small cell lung cancer (NSCLC), yet primary and acquired resistance remain common. Pinpointing regulators of tumor–immune crosstalk is therefore critical to enhance therapeutic efficacy. Methods We integrated multi-omics sequencing with ensemble machine-learning to nominate prognostic genes in NSCLC, followed by pathway and intercellular-signaling analyses (pseudotime analysis, GSVA, and functional enrichment). We generated shRNA-mediated FCRLB-knockdown NSCLC cell lines and performed molecular and immunological assays. A murine lung-cancer xenograft model was used to validate the role of the candidate gene in tumor progression and its impact on the tumor microenvironment (TME). Results We identified FCRLB as a key gene influencing lung cancer prognosis via transcriptomic gene screening, and elucidated its unique roles in tumor cells and macrophages. FCRLB-highly expressing tumor cells exhibited high activation of the PI3K signaling and reactive oxygen species (ROS) pathways, regulated metabolic pathways such as pyridine metabolism and terpenoid quinone biosynthesis, and induced C-C motif chemokine ligand 2 (CCL2) production. Additionally, high FCRLB expression in monocyte-derived macrophages (mo-Macs) promoted M2-like polarization, thereby further exacerbating lung cancer malignancy. Conclusions Our data identify and functionally validate FCRLB as a pro-tumor regulator in NSCLC.FCRLB regulates the secretion of multiple cytokines (including CCL2) by tumor cells via the ROS pathway and PI3K signaling pathway, thereby promoting M2 polarization of macrophages. Additionally, FCRLB exerts a positive regulatory effect on the malignant progression of NSCLC. This finding provides a potential novel target for overcoming immunotherapys resistance in NSCLC. None-small cell lung cancer Immunotherapy Machine learning multi-omics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Lung cancer is well-recognized for its high incidence and mortality rates, imposing a substantial burden on public health. Surgical resection combined with chemotherapy remains the foundational treatment modality for non-small cell lung cancer (NSCLC) ( 1 – 3 ).Immunotherapies—such as anti-PD-1/PD-L1 agents have significantly enhanced the survival prognosis of NSCLC patients and ushered in revolutionary advances in NSCLC treatment( 4 , 5 ). However, the clinical efficacy of ( 6 ) is severely limited, with only about 20% of lung cancer patients responding to treatment. This low response rate is largely attributed to the immunosuppressive tumor microenvironment (TME), with tumor-associated macrophages (TAMs) being a major contributor. As the most abundant immune cell population in the TME, monocyte-derived macrophages (mo-Macs) are recruited by tumor-secreted cytokines such as CCL2 and CSF-1 and polarized in to pro- tumorigenesis M2 phenotypes. Crucially, they potently suppress the infiltration and function of cytotoxic T cells, promote distant metastasis, and are strongly correlated with poor clinical outcomes( 7 – 9 ). And targeting the recruitment of mo-Macs is currently a critical strategy for improving the efficacy of immunotherapies( 10 , 11 ). Therefore, investigating how tumor cells drive immunotherapy resistance by modulating macrophages is crucial for developing new strategies to target macrophage recruitment and re-sensitize tumors to treatment. Machine learning has been maturely utilized in the screening of prognostic targets for NSCLC. By assessing the strengths and limitations of multiple algorithms, implementing the optimal prognostic model construction strategy, and further evaluating target features via single-cell transcriptome sequencing or multi-omics integrative analysis, this approach can assist researchers in rapidly and accurately identifying key prognostic genes in tumors. For instance, a recent study integrated machine learning with single-cell transcriptomics to screen for key prognostic genes in lung adenocarcinoma (LUAD) cells, and subsequently analyzed these genes via functional validation assays( 12 – 14 ). Machine learning enables researchers to screen for prognostic genes in a high-throughput manner, and the integration of multiple algorithms for constructing prognostic models with multi-omics sequencing further greatly improves the accuracy of such predictions—thus establishing it as a crucial approach in the research of the NSCLC immune landscape. In this study, to identify key mediators of NSCLC progress and immunotherapy resistance, we integrated multi-omics data and machine learning, and identified Fc receptor-like B (FCRLB) as a critical prognostic gene in NSCLC. Single-cell analyses localized FCRLB expression and linked it to ROS/PI3K pathway activity. Further mechanistic investigations revealed that FCRLB⁺ tumor cells, through ROS/PI3K and CCL2-mediated crosstalk, orchestrate a pro-tumorigenic TME by recruiting M2 macrophages, thereby nominating it as a promising therapeutic target for overcoming immunotherapy resistance. 2. Materials and methods 2.1 Ordinary transcriptome data availability The regular transcriptome data was obtained from GSE101929, consisting of 32 patients and 34 healthy individuals. The lung adenocarcinoma data (541 tumors and 59 healthy individuals) and lung squamous cell carcinoma data (502 tumors and 51 healthy individuals) were obtained from the TCGA database. R package Limma is used for differential analysis, R package org.Hs.eg.db and clusterProfiler are used for GO and KEGG enrichment, R package randomForest is used for random forest screening with an importance threshold of 0.0003, selected as important genes for subsequent Cox analysis, and R package glmnet, survivminer, and survival are used for Cox regression analysis. 2.2 Acquisition and processing of scRNA-seq data Single-cell dataset is from GSE131907. The Seurat software package is used for preliminary analysis of single-cell data. The cell quality control steps have been completed. Data normalization and selection of highly variable genes (2000) were selected. The functions used for data conversion were NormalizeData, FindVariableFeatures and ScaleData in the Seurat package. The dimensionality reduction methods UMAP, UMAP and clustering algorithm Louvian were used later, all from Seurat. The FindAllMarkers function to calculate the differential genes between Cluster or cell types, with pvalue less than 0.05, log2FC greater than 0.25, and the expression ratio should be greater than 0.1. VlnPlot and FeaturePlot functions are used to localize the violin graph and feature map of genes. The dim parameter is 15 and the resolution is 1. Cell annotations are given in the original text. The FindMarkers function is used for differential analysis of single-cell data, the R package ggplot2 is used for data visualization, and the R package pheatmap is used for heat map drawing. 2.3 Single-cell GSVA analysis and Single-cell cell analysis The GSVA software package is used for cell subpopulation GSVA analysis, and the pathway score gene set uses the HallMark gene set in msigDB and the KEGG gene set in C2. The subtype classification of cells was analyzed by the Seurat software package. The dimensionality reduction method UMAP, UMAP and clustering algorithm Louvian all came from Seurat. The dims parameter of tumor cell subtype classification was 15 and the resolution parameter was 0.001; the dims parameter of macrophage subtype classification was 15 and the resolution parameter was 0.01. The Nebulosa software package was used for gene colocalization analysis of macrophage subtypes. count (FCRLB) > 0 was defined as FCRLB + cells, and count (FCRLB) = 0 was defined as FCRLB-cell. The Cellchat package is used for cell communication analysis of single-cell data. Single cell data of macrophages and tumor cell subpopulations were combined into single cell communication data. The Monocle2 package is used for quasi-timed analysis of single-cell data. Gene expression trend min_expr = 0.5, and the remaining parameters are default. The IrGSEA package is used for single-cell gene set analysis of single-cell data, tumor cells, and macrophage cells for single-cell gene set analysis. IrGSEA 1.0 uses the AUCell, UCell, Singapore, and ssgsea algorithms to calculate Hallmark gene set scores, and the difference analysis is used to calculate the Wilcoxon rank sum test (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.001, ****p < 0.0001). All data processing, statistical analysis and plotting were performed in R 4.3.3. 2.4 Metabolomics data acquisition and analysis Metabolomic data were obtained from the literature( 15 ). R package ggplot2 is used for data visualization. For visualization: Use the R package ggplot2 to generate metabolite abundance heatmaps, based on the processed metabolome data. Use the pheatmap package to draw a dual-index heatmap: Dual index: Row index (metabolite clustering) + column index. Blue-white-red tricolor mapping for standardized abundance values; row-wise clustering via Euclidean distance combined with Ward.D2 method. 2.5 Construction of FCRLB NSCLC knockdown cell line Select two human lung cancer cell lines, NCI-H226 (Pricella, CL-0396) and NCI-H1975 (Pricella, CL-0298), and murine NSCLC cell line LLC (Pricella, CL-0140), and culture them in a cell culture incubator with RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% penicillin/streptamycin. Construct a short hairpin RNA (shRNA) lentiviral vector (YouBio) containing a specific sequence (Table 1 ) and package the lentivirus. Following lentivirus infection for 24 hours, the medium was replaced, and successfully infected cells were selected using puromycin (Solarbio, P8230). Knockdown efficiency was assessed using qRT-PCR analysis. 2.6 RNA extraction and qRT-PCR The RNA extraction process was performed using an RNA rapid extraction kit (RN001, Shanghai Yishan Biotechnology) following the manufacturer's protocols. Total RNA was reverse transcribed into cDNA using HiScript III all-in-one RT SuperMix Perfect for qPCR (R333-01, Vazyme). The qRT-PCR was conducted using ChamQ Universal SYBR qPCR Master Mix (Q711-02) in a CFX96 Touch Real Time PCR Detection System (BIO-RAD). Fluorescence data was collected at the end of each extension step. The specificity of the amplification products was verified by melting curve analysis, and primer sequences are listed in Table 1 . 2.7 Western blot (WB) assay Cells and tissues with similar density and well condition was added RIPA lysis buffer (Beyotime, P0013C) mixed with PMSF and phosphorylase inhibitor, thoroughly lyse on ice, sonicate the cells, centrifuge, and 5x DualColor Protein Loading Buffer (HANGZHOU FUDE BIOLOGICAL TECHNOLOGY, FD006) was added to the supernatant. Separate the protein using 10% SDS-PAGE and transfer the membrane. After blocking, the following primary antibodies: FCRLB (Proteintech, 17157-1-AP), β-actin (Cell Signaling, 4967S), p-PI3K (Cell Signaling,17366), p-AKT (Cell Signaling, 13038S), mTOR (Cell Signaling, 2972S) were used to incubate the membrane overnight. And the next day applied Anti rabbit IgG, Incubate HRP linked Antibody (Cell Signaling, 7074S) for one hour. The ChemiDoc™ Touch Imaging System (BIO-RAD) is used for chemiluminescence imaging scanning. 2.8 Construction of xenograft model in mice Expand shctrl-LLC cells and shFCRLB-LLC cells in good condition. Obtain 6-week-old healthy male C57BL/6 mice from GemPharmatech company. Digest, centrifuge, and resuspend two types of LLC cells, adjusting the cell concentration to 1×107/mL. Subcutaneous inject 100 µL of cell suspension into the abdomen of mice using a syringe. Waiting for 7 days until a visible tumor grows, and then measure the tumor volume and mass every other day. Sacrifice mice after 21 days. After isolating the tumor tissue, we subjected it to paraffin embedding and sectioning, followed by immunohistochemical (IHC) staining to detect the expression levels of CD86, CD206, and CCL2. This animal study was reviewed and approved by the Animal Experiment Ethics Committee of Zhejiang University before implementation (ZJU20250631). 2.9 FCRLB knockout NSCLC cell line proliferation ability test After knocking out the FCRLB gene in the cell line, alteration in cell proliferation ability were detected using Cell Counting Kit-8 (CCK8) (Beyotime, C0038) reagent. After digestion and centrifugation of the cells, use the complete medium for resuspension. After counting, adjust the cell concentration to 3×10 4 /mL. Add 100 µ L cells into the 96 well plate. After 0 h, 24 h, 48 h and 72 h of cell adhesion, add 10 µ LCCK8 reagent to incubate for 2 h. Then use the microplate reader (Tecan) to collect the absorbance of the sample at 450 nm wavelength. 2.10 Wound healing assay H1975 and H226 cells (stably transfected with shCtrl or shFCRLB) in an appropriate growth state were seeded in 6-well plates and incubated overnight. The seeding density was adjusted to ensure the cells formed a confluent monolayer covering the entire well surface by the next day. Vertical lines were drawn on the bottom of each well with a marker pen, and several observation points were marked along each line. A sterile pipette tip was used to gently scratch the confluent cell monolayer along the pre-drawn vertical lines, and images of cells adjacent to the observation points were captured under a microscope. After subsequent incubation in serum-free medium for 24 h, images were captured again at the same positions. The scratch area was quantified using ImageJ software. 2.11 Transwell migration and invasion assay of NSCLC cells The migration assay involves digesting and centrifuging the processed NSCLC cells, then resuspending them in a serum-free medium containing 0.5% BSA. The resuspended cells are then counted and adjusted to a concentration of 2×10 5 /mL. 200µL of the cell suspension is added to the upper chamber of an 8µm transwell chamber (Corning,3422), and the chamber is then transferred to a 24-well plate containing complete medium for 24 hours of culture. The chamber is collected, washed three times, fixed with 4% PFA (Beyotime, P0099) at room temperature for 20 minutes, and stained with crystal violet staining solution (Beyotime, C0121) for 1 hour. The cells above the filter membrane are gently wiped off with a cotton swab, washed three times, and then photographed under a microscope with five randomly selected fields. The invasion assay involves diluting matrigel and spreading it on the upper chamber of the transwell chamber. After the gel solidifies, the remaining steps are the same as those in the migration experiment. 2.12 Mitochondrial Reactive Oxygen Species Detection Two well-conditioned human NSCLC cell lines (shctrl and shFCRLB) were seeded in black-bottom, black-walled 96-well plates at a density of 1×10⁵ cells/well and incubated overnight. Following a 1-hour incubation with a mitochondrial ROS fluorescent probe (Elabscience, E-BC-F008), fluorescence signal intensity was measured using a microplate reader(Tecan) in top-read mode at excitation/emission (Ex/Em) wavelengths of 510/610 nm, with the signal intensity of the blank control group (containing probe only) subtracted for normalization. 2.13 Quantification of CCL2 Protein Concentration Cell supernatants were collected from two NSCLC cell lines transfected with shCtrl or shFCRLB as experimental samples. CCL2 levels were quantified using a CCL2 ELISA kit (absin, abs510026). For detection, 100 µL of each sample or standard was added to the microplate wells and incubated for 2 hours. After washing, the primary antibody was added and incubated for another 2 hours. Subsequently, horseradish peroxidase (HRP) conjugate, chromogenic substrate, and stop solution (provided in the kit) were added sequentially. Absorbance was measured at 450 nm using a microplate reader (Tecan). CCL2 concentrations in the samples were calculated based on the standard curve, which was derived from the linear relationship between standard concentrations and their corresponding absorbance values. 2.14 assessment of FCRLB-Mediated Macrophage Polarization In Vitro Six-week-old healthy male C57BL/6 mice were intraperitoneally injected with thioglycolate broth (Merck, 70157) for 4 consecutive days, then sacrificed after 4 days of reaction. Inject cold PBS into the abdominal cavity with a syringe, aspirate the liquid, filter through a 70 µm cell sieve (Yeasen, 84702ES50), centrifuge, and resuspend with red blood cell lysis buffer (Beyotime, C3702). After lysis, centrifuge to collect cells. Plate LLC cells (shCtrl and shFCRLB) at appropriate density, culture for 24 hours to collect supernatant, filter through a 0.22 µm membrane, and add to adherent mouse macrophages for 24-hour co-culture. Incubate the treated macrophages with CD86(Elabscience, E-AB-F0994D) and CD206(Elabscience, E-AB-F1135E) antibodies in the dark for 15 minutes, then detect cell polarization by flow cytometry. 2.15 Statistical Analysis All graphs were generated using GraphPad Prism (10.1.2). Statistical analyses of intergroup differences were performed using Student’s t-test, one-way analysis of variance (one-way ANOVA), and two-way analysis of variance (two-way ANOVA). Differences were considered statistically significant at p < 0.05. Table 1 Oligonucleotides used in this research Nucleic acid type Nucleic acid sequence Human shRNA Scrambled control TTCTCCGAACGTGTCACGTAA FCRLB-1# CCGCAAGCATTCTCTTTGAAT FCRLB-2# GCGAATTTCTTTCAAAGCCAT Mouse shRNA FCRLB GCTACTCTGGAGAAGCCTATA Human primer 18s sense CAGCCACCCGAGATTGAGCA 18s anti-sense TAGTAGCGACGGGCGGTGTG FCRLB sense AGTGGGCAAGCTGCTACTC FCRLB anti-sense CCGCTCCCCTTTGAAGATGG Mouse primer ACTIN sense GTGACGTTGACATCCGTAAAGA ACTIN anti-sense GCCGGACTCATCGTACTCC FCRLB sense ACCACCATCTTCAAGGGAGAG FCRLB anti-sense TACCAGAGAGTGCTAATGGGC 3. Results 3.1 Transcriptome data analysis of NSCLC Through differentially analysis of LUAD, lung squamous cell carcinoma (LUSC) data and GSE101929, we identified 5895 downregulated genes and 6034 upregulated genes (Fig S1 B) in the LUAD data, respectively; 7843 downregulated genes and 7957 upregulated genes (Fig S1 C) in the LUSC data; and 2526 downregulated genes and 2444 upregulated genes in the GSE101929 dataset (Fig. 1 A). GO and KEGG enrichment of up-regulated genes revealed BP pathways such as chromosome segregation, DNA templated DNA replication, and DNA replication, The chromosome region, condensed chromosome and other CC pathways, as well as single stranded DNA helicase activity, catalytic activity, acting on DNA and other MF pathways, were significantly enriched (Fig. 1 B), while the Biosynthesis of cofactors, one carbon pool by folate and carbon metabolism pathways were significantly enriched (Fig. 1 C). Subsequently, we conducted RF gene screening and Cox regression analysis on LUAD and LUSC using co upregulated genes (Fig. 1 D-G), and identified 12 genes that significantly affect the overall prognosis of lung cancer. 3.2 Single-cell analysis of NSCLC To further analyze the mechanism of the selected key genes, we used single-cell data GSE131907. We first conducted single-cell analysis on a total of 208506 cells (Fig. 2 A&Fig S2 A-C), calculated all cell markers (Fig. 2 B), and extracted single-cell data from tumor samples for subsequent analysis. We also performed expression localization analysis on twelve key genes (Fig. 2 C). The results showed that SLC7A5, IER5L, ALDOA, FERMT1, and FCRLB genes were relatively highly expressed, but SLC7A5( 16 ), IER5L( 17 ), ALDOA( 18 ), FERMT1( 19 ) genes were studied more extensively, while the mechanism of FCRLB gene in lung cancer was less studied. Therefore, we chose FCRLB gene as the key gene for subsequent analysis. Through genetic mapping analysis, we found that the FCRLB gene is highly expressed in ts2 tumor cells and m0 Mac. Subsequently, we used the EMT gene set to score all tumor cells (Fig. 2 D-E), and found that the EMT gene was highly expressed in ts2 and tumor ECs cells, indicating a higher degree of malignancy in ts2 cells. 3.3 Single-cell pseudo-temporal analysis To further characterize ts2 cells, we first performed single-cell pseudo temporal analysis (Fig. 3 A). Through analysis, it was found that ts2 is a mature differentiated cell, concentrated in the late stage of development. Ts2 is a mature differentiated cancer cell with specific functions. And through time-series gene analysis, we identified key genes that are highly expressed during ts2 differentiation (Fig. 3 B-C), such as A1BG, ACAP3, AAMDC, etc. Subsequently, in order to further analyze the function of ts2 cells, we used single-cell gene set analysis (Fig. 3 D) and found that ts2 cells highly expressed the reactive oxygen species pathway. After analyzing the key genes of the reactive oxygen species pathway (Fig. 3 E), PRDX4 and ATOX1 genes were identified as key genes in the pathway. Subsequently, we used UCell, AUCell, ssgsea, and Singscore for analysis and found that ts2 cells were highly expressed in the reactive oxygen species pathway among the four algorithm scores (Fig. 3 F-I). Subsequently, we used GSVA scoring to evaluate the Hallmark gene of MSIGDB on TS2 cells and FCRLB + TS2 cells (Fig. 3 J-K). We found that both ts2 and FCRLB + ts2 overexpress the PI3K-AKT-MTOR signaling pathway. At the same time, we also conducted KEGG pathway analysis on FCRLB + ts2 (Fig. 3 L), and found that FCRLB + ts2 overexpresses key tumor related pathways such as TGF-BETA and Apoptosis. 3.4 Cell Communication Analysis To analyze the overall role of FCRLB in cancer regulation, we selected tumor cells and cancer cells for cell communication analysis (Fig. 4 A-D). Through pathway analysis and receptor pairing analysis, we found that ts2 can specifically affect mo-Mac through VEGF, which is consistent with the results of gene set analysis, promoting M2-like macrophage polarization and further deteriorating the tumor microenvironment (Fig. 4 E). 3.5 Single-cell metabolic analysis In order to further elucidate the role of the FCRLB gene in the malignant progression of cancer cells, we divided the FCRLB gene expression into FCRLB + ts2 cells and FCRLB-ts2 cells, and performed single-cell metabolic analysis on these two cells (Fig. 5 A). The results showed that the metabolic pathways of pyridine metabolism and Ubquinone and other terpenoid quinone biosynthesis were significantly upregulated (Fig. 5 B). After analyzing the metabolomics of lung cancer blood samples and tissue samples, we found that the metabolic pathways of pyridine metabolism and Ubquinone and other terpenoid quinone biosynthesis were also significantly enriched (Fig. 5 B). And at the same time, we selected key metabolites of the pyramididine metabolism and Ubquinone and other terpenoid quinone biosynthesis metabolic pathways from metabolomics data (Fig. 5 C-D, F). 3.6 mo-Mac subgroup analysis Subsequently, we conducted a detailed analysis of another key localization cell-mo-Mac. We first analyzed the immune cell microenvironment of single-cell normal samples and tumor samples, and found that the infiltration rate of mo-Macs significantly increased in lung cancer (Fig. 6 A-B). Subsequently, we conducted gene set analysis on three types of Macs (Fig. 6 C-D) and found that mo Macs highly expressed the Hedgehog pathway. Through key gene analysis of the Hedgehog pathway, we identified the VEGF gene as the pathway key gene (Fig. 6 E). At the same time, we used FCRLB expression to group mo-Macs (Fig. 6 F), and then performed GSVA analysis on FCRLB + mo-Macs and mo Macs (Fig. 6 G-H). The results showed that mo-Macs overexpressed pathways such as inflammatory response and hypoxia, while FCRLB + mo-Macs overexpressed the reactive oxygen species pathway and PI3K-AKT-MTOR signaling pathway. According to the previous study( 20 ), Mac can achieve M2 canopy polarization through the Hedgehog pathway. We used the M2 marker for localization and found (Fig. 6 I) that the co localization result of CD163 and CD206 genes was mo-Mac, which is consistent with our hypothesis. Pseudo-temporal analysis revealed that mo-Macs are likely undergoing a dynamic process of differentiation or functional remodeling (Fig. 6 J). 3.7 Validation that the FCRLB gene promotes malignant progression of NSCLC To confirm the role of FCRLB in promoting the malignant progression of NSCLC, we utilized shRNA-mediated knockdown to downregulate FCRLB expression in these two cell lines and verified the knockdown efficiency (Fig. 7 A-B). Knockdown of FCRLB can also significantly inhibit the growth rate of tumor cells (Fig. 7 C-D). We further investigated the role of FCRLB in promoting metastasis and invasion; consistent with our predictions, the metastatic and invasive capacities of NSCLC cell lines were significantly impaired following FCRLB knockdown (Fig. 7 E-H). We further verified the function of fcrlb in NSCLC. Mitochondrial reactive oxygen species levels were significantly reduced following FCRLB knockdown (Fig. 8 A). Further quantification of CCL2 protein levels in cell supernatants demonstrated that FCRLB can stimulate CCL2 production (Fig. 8 B), while FCRLB knockdown decreased the activation of the PI3K-Akt signaling pathway upstream of CCL2 (Fig. 8 C-D). Results from macrophage polarization assays demonstrated that FCRLB knockdown suppressed M2 polarization (Fig. 8 E). We established a mouse subcutaneous tumor model using FCRLB-knockdown LLC1 cells (Fig. 9 A). Compared with the shctrl group, FCRLB knockdown significantly reduced tumor volume and weight (Fig. 9 B-D). IHC staining (Fig. 9 E) of tumor samples demonstrated increased infiltration of M2-like macrophages and elevated CCL2 expression levels in the presence of FCRLB. 4. Discussion Tumor-infiltrating macrophages, despite their phagocytic capacity to clear tumor cells, are predominantly polarized into pro-tumor M2 cells. These cells extensively secrete immunosuppressive factors (like IL-10, ( 21 , 22 )) and angiogenesis factors (like VEGF, MMP2) while highly expressing immune checkpoints such as PD-L1. They are widely recognized as core contributors to tumor progression and immune suppression, severely hindering the efficacy of immunotherapy and accelerating tumor growth. Therefore, reducing macrophage recruitment and M2 polarization is considered key to eliminating the adverse effects associated with TAMs( 21 – 23 ). Interestingly, previous studies have demonstrated that tumors can remodel the tumor microenvironment by secreting metabolites. These metabolites modulate macrophage polarization toward the M2 subtype via the Hedgehog signaling pathway, thereby promoting malignant proliferation( 24 , 25 ). Through identification, it has been demonstrated that metabolites modulate the chemotaxis and infiltration of TAMs by regulating the expression of multiple cytokines, including CCL2, in tumor cells( 26 ). The Hedgehog signaling pathway is recognized as a critical driver of tumor progression across diverse tumor models and is linked to enhanced macrophage infiltration and polarization( 24 , 27 , 28 ). Tumor-derived CCL2 serves to recruit macrophages and promote M2 polarization, with activation of the Hedgehog signaling pathway in TAMs exerting a critical role in this process( 29 , 30 ). In this study, we found that FCRLB gene was highly expressed in ts2 tumor cells, and EMT gene set score showed that ts2 cells were highly malignant. Signaling pathway enrichment analysis revealed that mo-Mac cells exhibit upregulation of the Hedgehog signaling pathway. Analyses of FCRLB-positive and FCRLB-negative subgroups yielded results similar to those from the ts2 analysis. CCL2, a downstream effector molecule of the PI3K signaling pathway( 31 ), effectively activate the Hedgehog signaling pathway to induce macrophage M2-polarization, as noted previously. Furthermore, results from cell-cell communication analyses support specific CCL2-mediated crosstalk between ts2 and mo-Mac cell populations. To obtain a more comprehensive understanding of the malignant functions of FCRLB, metabolic analyses of FCRLB-positive and FCRLB-negative populations revealed significant upregulation of pyrimidine metabolism as well as ubiquinone and other terpenoid-quinone biosynthesis pathways, with key metabolites identified. Results from HPLC further demonstrated changes in these metabolites following FCRLB overexpression and knockout. FCRLB, a newly identified member of the Fc receptor-like family, was initially believed to exert an immunomodulatory role( 32 , 33 ). Previous studies have demonstrated that FCRLB plays a critical role in the inhibitory pathway of B cell signaling and can effectively suppress key downstream signaling pathways in B cells( 34 ). Some studies have demonstrated that FCRLB serve as a favorable prognostic factor in chronic lymphocytic leukemia and regulate immune activation processes during malignant progression( 35 , 36 ). However, research on this gene remains limited; its specific functions, involved signaling networks, and regulatory roles in solid tumors are still poorly understood. Herein, through the integration of multi-omics and machine learning, advanced transcriptome analyses, and validation in cellular models, animal models, and clinical samples, we have, for the first time, comprehensively investigated the characteristics of FCRLB in promoting malignant progression in NSCLC and elucidated its regulatory role in tumor-macrophage crosstalk. This not only broadens our understanding of the signaling networks underlying tumor remodeling of the TME but also identifies highly promising therapeutic targets. Our work also has some limitations. Firstly, although we have discovered the pro-tumor regulatory role of FCRLB in NSCLC, the specific signaling mechanism is still unclear, and its natural ligand is still unknown. Further research on FCRLB monoclonal antibodies and epigenetics is needed to investigate the association between malignant pathways and FCRLB. Secondly, although CCL2 has been found to play an important role in the ts2-mo-Mac signaling interaction in our cell communication research and has received significant attention as a recognized macrophage infiltration factor, other FCRLB mediated cytokines also require comprehensive research. Multi factor testing may provide us with stronger support in subsequent research. Then, further exploration is needed to identify small molecule targeted drugs for FCRLB, including screening small molecule drug libraries, screening suitable small molecule compounds, and verifying their functions in animal models, which can better demonstrate the clinical potential of this target. 5. Conclusion We screened the key gene FCRLB that affects the prognosis of lung cancer through transcriptome gene screening, and analyzed the unique role of FCRLB in tumor cells and macrophages. Tumor cells with high expression of FCRLB showed high activation of PI3K and reactive oxygen pathways, regulating pyridine metabolism and other terpenoid quinone biosynthesis pathways, inducing CCL2 expression. Also, high FCRLB expression in mo-Mac, promote M2-like polarization, and further exacerbate the malignancy of lung cancer. Abbreviations FCRLB Fc receptor-like B NSCLC non-small cell lung cancer ROS reactive oxygen species CCL2 C-C motif chemokine ligand 2 TME tumor microenvironment TAMs tumor-associated macrophages mo-Macs monocyte-derived macrophages LUAD lung adenocarcinoma WB Western blot CCK8 Cell Counting Kit-8 one-way ANOVA one-way analysis of variance two-way ANOVA two-way analysis of variance LUSC lung squamous cell carcinoma Declarations Ethics approval and consent to participate The animal study was reviewed and approved by the Animal Experiment Ethics Committee of Zhejiang University before implementation (ZJU20250631). Consent for publication Consent to publish was obtained from the study participants. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was funded by the National Natural Science Foundation of China (No. 82203608 to X.Z.Y. and No. 32300753 to S.Y.L.), Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China (LHDMY23H160002 to X.Z.Y.). Author contribution Nueraili Maihemuti, Yueli Shi, Sujing Jiang, Pan Liu, Zhen Shi and Zhiyong Xu designed the study, performed experiments, analyzed data, and wrote the manuscript. Nueraili Maihemuti, Yueli Shi and Sujing Jiang performed experiments and analyzed data. Zhiyong Xu, Zhen Shi and Pan Liu conceived and designed the study and wrote the manuscript. Acknowledgements The authors acknowledge the data provided by databases including TCGA and GEO. Appreciation is expressed to the reviewers and editors for their constructive comments. References Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10-45. 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1","display":"","copyAsset":false,"role":"figure","size":5356512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of common upregulated genes and their clinical relevance in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot illustrating differentially expressed genes (DEGs) from GSE dataset analysis. Red dots denote upregulated genes, blue dots denote downregulated genes, and gray dots denote genes with no significant difference (|log₂fold change (FC)| \u0026gt; 1, adjusted P \u0026lt; 0.05).(B) Gene Ontology (GO) enrichment analysis of common upregulated genes, showing significantly enriched biological processes, cellular components, or molecular functions.(C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of common upregulated genes, highlighting significantly enriched signaling pathways.(D) Random Forest (RF) feature importance scoring plot for common upregulated genes in the LUAD dataset, ranking genes by their importance in predicting clinical outcomes.(E) Forest plot of Cox proportional hazards regression analysis for common upregulated genes in LUAD, displaying hazard ratios (HRs) and 95% confidence intervals (CIs) to assess their prognostic significance.(F) RF feature importance scoring plot for common upregulated genes in the LUSC dataset, ranking genes by their importance in predicting clinical outcomes.(G) Forest plot of Cox proportional hazards regression analysis for common upregulated genes in LUSC, displaying HRs and 95% CIs to assess their prognostic significance.(H–J) Receiver operating characteristic (ROC) curves for validating the predictive performance of the gene signature in external datasets, with area under the curve (AUC) values indicated.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/88018ff5eed4271be748619d.png"},{"id":94841714,"identity":"d206a2ee-8ff6-4ee0-bf5b-dd26fbd6f99b","added_by":"auto","created_at":"2025-10-31 09:30:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4221193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptomic characterization of lung cancer cells and analysis of key gene/EMT signature distribution.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) UMAP plot of dimensionality reduction for lung cancer single-cell RNA sequencing (scRNA-seq) data. (B) Heatmap of marker genes for lung cancer single-cell clusters. Rows represent cluster-specific marker genes. (C) Single-cell localization feature plot of key genes in lung cancer. (D) UMAP plot of dimensionality reduction for tumor cells isolated from lung cancer scRNA-seq data. (E) UMAP plot of lung cancer tumor cells overlaid with EMT gene set enrichment scores.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/5d0875cea4ebc8475502b829.png"},{"id":94985314,"identity":"b5dff377-eb61-4b7a-abf8-2ef6e418ffd9","added_by":"auto","created_at":"2025-11-03 06:57:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6195252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePseudotime trajectory analysis and pathway enrichment scoring of lung cancer single cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Single-cell pseudotime trajectory plot of lung cancer cells. (B) Temporal expression profiles of the top 10 differentially expressed genes (DEGs) along pseudotime. (C) Temporal heatmap of key genes and their co-expression modules along pseudotime. Rows represent either target key genes. (D) Hallmark gene set enrichment score plot along pseudotime. The x-axis denotes pseudotime.(E) Analysis plot of key genes in the reactive oxygen species (ROS) pathway.(F) UCell algorithm: x-axis = UCell score for the ROS pathway (higher scores = stronger pathway activation), y-axis = cell density. Colors distinguish (G) AUCell algorithm: Same structure as (F), with x-axis = AUCell score for the ROS pathway. (H) singscore algorithm: Same structure as (F), with x-axis = singscore score for the ROS pathway. (I) ssGSEA algorithm: Same structure as (F), with x-axis = ssGSEA score for the ROS pathway. (J–K) Heatmaps of Hallmark pathway enrichment scores calculated via GSVA. (L) Heatmap of KEGG pathway enrichment scores calculated via GSVA.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/636fc27a0d8385864d98ab70.png"},{"id":94841718,"identity":"aa502dbe-8741-49a5-9b18-cca8114a52b7","added_by":"auto","created_at":"2025-10-31 09:30:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4427163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell-cell communication profiling in the lung cancer microenvironment using single-cell RNA sequencing (scRNA-seq) data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Quantification plot of cell-cell communication intensity and quantity. (B) Cell-cell communication pathway network plot. (C) Chord diagram of cell-cell communication. (D) VEGF signaling pathway communication intensity plot. (E) Ligand-receptor (L-R) pair analysis plot for the VEGF signaling pathway.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/251e2dca61e86116e36ba562.png"},{"id":94841722,"identity":"659665be-f0b3-4507-9f7e-8d36b4e0f496","added_by":"auto","created_at":"2025-10-31 09:30:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5086559,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic characterization of lung cancer across single-cell, serum, and tissue samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Dot plot for single-cell metabolic analysis of lung cancer cells. (B) Box plot for single-cell metabolic analysis of lung cancer cells. (C) Heatmap of key metabolites in serum samples from lung cancer patients vs. healthy controls. (D) Heatmap of key metabolites in lung cancer tissue samples vs. adjacent normal tissue samples. (E) Heatmap of key metabolic pathways in lung cancer vs. normal samples. (F) Key metabolites.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/bd15c5fc263b97565ca267f2.png"},{"id":94984960,"identity":"1e4fc081-1783-4d82-a89e-71f9cec9f88e","added_by":"auto","created_at":"2025-11-03 06:57:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5904148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell characterization of immune cell subsets, IL-6-JAK-STAT pathway activity, and mo-Mac (monocyte-derived macrophage) dynamics in normal vs. lung cancer samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Cell proportion plot of single-cell populations in normal samples. (B) Cell proportion plot of single-cell populations in lung cancer samples. (C) Analysis plot of macrophage (macrophage) gene sets. (D) Half-violin plot of IL-6-JAK-STAT pathway activity scored by four algorithms. (E) Key gene analysis plot of the IL-6-JAK-STAT pathway. (F) Volcano plot of differential gene expression between FCRLB⁺ mo-Mac and FCRLB⁻ mo-Mac subsets. (G) Heatmap of Hallmark pathway enrichment scores (via GSVA) across cell subsets/sample groups. (H) Heatmap of Hallmark pathway enrichment scores (via GSVA) specifically in mo-Mac cell. (I) Co-localization plot of CD163 and CD206 genes. (J) Single-cell pseudotime trajectory plot of macrophages.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/c9c6784925fd5d9b334a1e65.png"},{"id":94841721,"identity":"11256079-110a-4709-bbb0-412d70db85db","added_by":"auto","created_at":"2025-10-31 09:30:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7231713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFCRLB knockdown impairs proliferation, migration, and invasion of lung cancer cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) Quantitative real-time PCR (qRT-PCR) analysis of FCRLB mRNA expression in H1975 and H226 cells transduced with control shRNA (shCtrl), or two independent FCRLB-targeting shRNAs (shFCRLB-1#, shFCRLB-2#). (C-D) Cell proliferation was assessed by CCK-8 assay in H1975 (C) and H226 (D) cells transduced with shCtrl, shFCRLB-1#, or shFCRLB-2# over a 3-day time course. (E-F) Wound healing assays were performed in H1975 (E) and H226 (F) cells transduced with shCtrl, shFCRLB-1#, or shFCRLB-2#; representative images of wound areas at 0 h and 24 h (left panels) and quantification of wound closure rate (right panels) are shown.(G) Transwell migration assays of H1975 and H226 cells transduced with shCtrl, shFCRLB-1#, or shFCRLB-2#; representative crystal violet-stained images of migrated cells are shown.(H) Transwell invasion assays (using Matrigel-coated inserts) of H1975 and H226 cells transduced with shCtrl, shFCRLB-1#, or shFCRLB-2#; representative crystal violet-stained images of invaded cells (left panels) and quantification of invaded cell numbers (right panels) are shown***P ≤ 0.001, **P ≤ 0.002, *P ≤ 0.05.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/644c4826a581b628687dd76b.png"},{"id":94985329,"identity":"124c9a3a-f66b-486d-bfdf-3720410fe8a4","added_by":"auto","created_at":"2025-11-03 06:57:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3494960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of FCRLB knockdown on mitochondrial ROS levels, CCL2 secretion, PI3K-AKT pathway activation, and macrophage polarization in non-small cell lung cancer (NSCLC) cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Mitochondrial reactive oxygen species (ROS) levels in two NSCLC cell lines transduced with control shRNA (shCtrl), shFCRLB-1#, or shFCRLB-2# were detected using a fluorescence microplate reader at an excitation wavelength (Ex) of 510 nm and an emission wavelength (Em) of 610 nm. (B) ELISA was performed to measure the expression level of CCL2 in the cell supernatant of two NSCLC cell lines transduced with shCtrl, shFCRLB-1#, or shFCRLB-2#. (C–D) WB analysis was used to detect the protein levels of key components in the PI3K-AKT signaling pathway in two NSCLC cell lines transduced with shCtrl, shFCRLB-1#, or shFCRLB-2#. (E) Flow cytometry was employed to assess the effect of cell supernatant from LLC1 cells transduced with shCtrl or shFCRLB on the polarization of primary macrophages. ***P ≤ 0.001, **P ≤ 0.002, *P ≤ 0.05.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/0f6cf60d6898958e80712ee6.png"},{"id":94841725,"identity":"8bf1ed35-b330-4646-930e-ac27cf98c630","added_by":"auto","created_at":"2025-10-31 09:30:56","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":8119782,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn vivo validation of FCRLB function in a murine LLC subcutaneous xenograft model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Quantitative real-time PCR (qRT-PCR) analysis of FCRLB knockdown efficiency in LLC cells. (B) Representative images of tumors isolated from C57BL/6 mice after subcutaneous xenograft of shctrl- or shFCRLB-transduced LLC cells. (C) Tumor weight comparison between shctrl and shFCRLB xenograft groups. (D) Tumor growth curves of shctrl and shFCRLB xenografts. (E) Immunohistochemical (IHC) staining results of CD86, CD206, and CCL2 in shCtrl and shFCRLB tumors. ***P ≤ 0.001, **P ≤ 0.002, *P ≤ 0.05.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/c80af6f351bafe76bd4dac6a.png"},{"id":103252613,"identity":"13b87591-b36d-447d-8840-cacdf98f2569","added_by":"auto","created_at":"2026-02-23 16:15:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":51475068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/b07d0687-4a51-4b40-9b48-9033cdedea67.pdf"},{"id":94985469,"identity":"f73ee217-f304-4aaf-b23c-568939e1783f","added_by":"auto","created_at":"2025-11-03 06:58:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9600633,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7784814/v1/0f6fba97de8d5194d3b6c55e.pdf"}],"financialInterests":"","formattedTitle":"FCRLB-mediated dual control of tumor metabolism and macrophage polarization promotes lung cancer malignancy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer is well-recognized for its high incidence and mortality rates, imposing a substantial burden on public health. Surgical resection combined with chemotherapy remains the foundational treatment modality for non-small cell lung cancer (NSCLC) (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).Immunotherapies\u0026mdash;such as anti-PD-1/PD-L1 agents have significantly enhanced the survival prognosis of NSCLC patients and ushered in revolutionary advances in NSCLC treatment(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, the clinical efficacy of (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) is severely limited, with only about 20% of lung cancer patients responding to treatment. This low response rate is largely attributed to the immunosuppressive tumor microenvironment (TME), with tumor-associated macrophages (TAMs) being a major contributor. As the most abundant immune cell population in the TME, monocyte-derived macrophages (mo-Macs) are recruited by tumor-secreted cytokines such as CCL2 and CSF-1 and polarized in to pro- tumorigenesis M2 phenotypes. Crucially, they potently suppress the infiltration and function of cytotoxic T cells, promote distant metastasis, and are strongly correlated with poor clinical outcomes(\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). And targeting the recruitment of mo-Macs is currently a critical strategy for improving the efficacy of immunotherapies(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Therefore, investigating how tumor cells drive immunotherapy resistance by modulating macrophages is crucial for developing new strategies to target macrophage recruitment and re-sensitize tumors to treatment.\u003c/p\u003e\u003cp\u003eMachine learning has been maturely utilized in the screening of prognostic targets for NSCLC. By assessing the strengths and limitations of multiple algorithms, implementing the optimal prognostic model construction strategy, and further evaluating target features via single-cell transcriptome sequencing or multi-omics integrative analysis, this approach can assist researchers in rapidly and accurately identifying key prognostic genes in tumors. For instance, a recent study integrated machine learning with single-cell transcriptomics to screen for key prognostic genes in lung adenocarcinoma (LUAD) cells, and subsequently analyzed these genes via functional validation assays(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Machine learning enables researchers to screen for prognostic genes in a high-throughput manner, and the integration of multiple algorithms for constructing prognostic models with multi-omics sequencing further greatly improves the accuracy of such predictions\u0026mdash;thus establishing it as a crucial approach in the research of the NSCLC immune landscape.\u003c/p\u003e\u003cp\u003eIn this study, to identify key mediators of NSCLC progress and immunotherapy resistance, we integrated multi-omics data and machine learning, and identified Fc receptor-like B (FCRLB) as a critical prognostic gene in NSCLC. Single-cell analyses localized FCRLB expression and linked it to ROS/PI3K pathway activity. Further mechanistic investigations revealed that FCRLB⁺ tumor cells, through ROS/PI3K and CCL2-mediated crosstalk, orchestrate a pro-tumorigenic TME by recruiting M2 macrophages, thereby nominating it as a promising therapeutic target for overcoming immunotherapy resistance.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Ordinary transcriptome data availability\u003c/h2\u003e\u003cp\u003eThe regular transcriptome data was obtained from GSE101929, consisting of 32 patients and 34 healthy individuals. The lung adenocarcinoma data (541 tumors and 59 healthy individuals) and lung squamous cell carcinoma data (502 tumors and 51 healthy individuals) were obtained from the TCGA database. R package Limma is used for differential analysis, R package org.Hs.eg.db and clusterProfiler are used for GO and KEGG enrichment, R package randomForest is used for random forest screening with an importance threshold of 0.0003, selected as important genes for subsequent Cox analysis, and R package glmnet, survivminer, and survival are used for Cox regression analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Acquisition and processing of scRNA-seq data\u003c/h2\u003e\u003cp\u003eSingle-cell dataset is from GSE131907. The Seurat software package is used for preliminary analysis of single-cell data. The cell quality control steps have been completed. Data normalization and selection of highly variable genes (2000) were selected. The functions used for data conversion were NormalizeData, FindVariableFeatures and ScaleData in the Seurat package. The dimensionality reduction methods UMAP, UMAP and clustering algorithm Louvian were used later, all from Seurat. The FindAllMarkers function to calculate the differential genes between Cluster or cell types, with pvalue less than 0.05, log2FC greater than 0.25, and the expression ratio should be greater than 0.1. VlnPlot and FeaturePlot functions are used to localize the violin graph and feature map of genes. The dim parameter is 15 and the resolution is 1. Cell annotations are given in the original text. The FindMarkers function is used for differential analysis of single-cell data, the R package ggplot2 is used for data visualization, and the R package pheatmap is used for heat map drawing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Single-cell GSVA analysis and Single-cell cell analysis\u003c/h2\u003e\u003cp\u003eThe GSVA software package is used for cell subpopulation GSVA analysis, and the pathway score gene set uses the HallMark gene set in msigDB and the KEGG gene set in C2. The subtype classification of cells was analyzed by the Seurat software package. The dimensionality reduction method UMAP, UMAP and clustering algorithm Louvian all came from Seurat. The dims parameter of tumor cell subtype classification was 15 and the resolution parameter was 0.001; the dims parameter of macrophage subtype classification was 15 and the resolution parameter was 0.01. The Nebulosa software package was used for gene colocalization analysis of macrophage subtypes. count (FCRLB)\u0026thinsp;\u0026gt;\u0026thinsp;0 was defined as FCRLB\u0026thinsp;+\u0026thinsp;cells, and count (FCRLB)\u0026thinsp;=\u0026thinsp;0 was defined as FCRLB-cell.\u003c/p\u003e\u003cp\u003eThe Cellchat package is used for cell communication analysis of single-cell data. Single cell data of macrophages and tumor cell subpopulations were combined into single cell communication data. The Monocle2 package is used for quasi-timed analysis of single-cell data. Gene expression trend min_expr\u0026thinsp;=\u0026thinsp;0.5, and the remaining parameters are default.\u003c/p\u003e\u003cp\u003eThe IrGSEA package is used for single-cell gene set analysis of single-cell data, tumor cells, and macrophage cells for single-cell gene set analysis. IrGSEA 1.0 uses the AUCell, UCell, Singapore, and ssgsea algorithms to calculate Hallmark gene set scores, and the difference analysis is used to calculate the Wilcoxon rank sum test (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003cp\u003eAll data processing, statistical analysis and plotting were performed in R 4.3.3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Metabolomics data acquisition and analysis\u003c/h2\u003e\u003cp\u003eMetabolomic data were obtained from the literature(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). R package ggplot2 is used for data visualization. For visualization: Use the R package ggplot2 to generate metabolite abundance heatmaps, based on the processed metabolome data. Use the pheatmap package to draw a dual-index heatmap: Dual index: Row index (metabolite clustering)\u0026thinsp;+\u0026thinsp;column index. Blue-white-red tricolor mapping for standardized abundance values; row-wise clustering via Euclidean distance combined with Ward.D2 method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Construction of FCRLB NSCLC knockdown cell line\u003c/h2\u003e\u003cp\u003eSelect two human lung cancer cell lines, NCI-H226 (Pricella, CL-0396) and NCI-H1975 (Pricella, CL-0298), and murine NSCLC cell line LLC (Pricella, CL-0140), and culture them in a cell culture incubator with RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% penicillin/streptamycin. Construct a short hairpin RNA (shRNA) lentiviral vector (YouBio) containing a specific sequence (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and package the lentivirus. Following lentivirus infection for 24 hours, the medium was replaced, and successfully infected cells were selected using puromycin (Solarbio, P8230). Knockdown efficiency was assessed using qRT-PCR analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 RNA extraction and qRT-PCR\u003c/h2\u003e\u003cp\u003eThe RNA extraction process was performed using an RNA rapid extraction kit (RN001, Shanghai Yishan Biotechnology) following the manufacturer's protocols. Total RNA was reverse transcribed into cDNA using HiScript III all-in-one RT SuperMix Perfect for qPCR (R333-01, Vazyme). The qRT-PCR was conducted using ChamQ Universal SYBR qPCR Master Mix (Q711-02) in a CFX96 Touch Real Time PCR Detection System (BIO-RAD). Fluorescence data was collected at the end of each extension step. The specificity of the amplification products was verified by melting curve analysis, and primer sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Western blot (WB) assay\u003c/h2\u003e\u003cp\u003eCells and tissues with similar density and well condition was added RIPA lysis buffer (Beyotime, P0013C) mixed with PMSF and phosphorylase inhibitor, thoroughly lyse on ice, sonicate the cells, centrifuge, and 5x DualColor Protein Loading Buffer (HANGZHOU FUDE BIOLOGICAL TECHNOLOGY, FD006) was added to the supernatant. Separate the protein using 10% SDS-PAGE and transfer the membrane. After blocking, the following primary antibodies: FCRLB (Proteintech, 17157-1-AP), β-actin (Cell Signaling, 4967S), p-PI3K (Cell Signaling,17366), p-AKT (Cell Signaling, 13038S), mTOR (Cell Signaling, 2972S) were used to incubate the membrane overnight. And the next day applied Anti rabbit IgG, Incubate HRP linked Antibody (Cell Signaling, 7074S) for one hour. The ChemiDoc\u0026trade; Touch Imaging System (BIO-RAD) is used for chemiluminescence imaging scanning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Construction of xenograft model in mice\u003c/h2\u003e\u003cp\u003eExpand shctrl-LLC cells and shFCRLB-LLC cells in good condition. Obtain 6-week-old healthy male C57BL/6 mice from GemPharmatech company. Digest, centrifuge, and resuspend two types of LLC cells, adjusting the cell concentration to 1\u0026times;107/mL. Subcutaneous inject 100 \u0026micro;L of cell suspension into the abdomen of mice using a syringe. Waiting for 7 days until a visible tumor grows, and then measure the tumor volume and mass every other day. Sacrifice mice after 21 days. After isolating the tumor tissue, we subjected it to paraffin embedding and sectioning, followed by immunohistochemical (IHC) staining to detect the expression levels of CD86, CD206, and CCL2. This animal study was reviewed and approved by the Animal Experiment Ethics Committee of Zhejiang University before implementation (ZJU20250631).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 FCRLB knockout NSCLC cell line proliferation ability test\u003c/h2\u003e\u003cp\u003eAfter knocking out the FCRLB gene in the cell line, alteration in cell proliferation ability were detected using Cell Counting Kit-8 (CCK8) (Beyotime, C0038) reagent. After digestion and centrifugation of the cells, use the complete medium for resuspension. After counting, adjust the cell concentration to 3\u0026times;10\u003csup\u003e4\u003c/sup\u003e/mL. Add 100 \u0026micro; L cells into the 96 well plate. After 0 h, 24 h, 48 h and 72 h of cell adhesion, add 10 \u0026micro; LCCK8 reagent to incubate for 2 h. Then use the microplate reader (Tecan) to collect the absorbance of the sample at 450 nm wavelength.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Wound healing assay\u003c/h2\u003e\u003cp\u003eH1975 and H226 cells (stably transfected with shCtrl or shFCRLB) in an appropriate growth state were seeded in 6-well plates and incubated overnight. The seeding density was adjusted to ensure the cells formed a confluent monolayer covering the entire well surface by the next day. Vertical lines were drawn on the bottom of each well with a marker pen, and several observation points were marked along each line. A sterile pipette tip was used to gently scratch the confluent cell monolayer along the pre-drawn vertical lines, and images of cells adjacent to the observation points were captured under a microscope. After subsequent incubation in serum-free medium for 24 h, images were captured again at the same positions. The scratch area was quantified using ImageJ software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Transwell migration and invasion assay of NSCLC cells\u003c/h2\u003e\u003cp\u003eThe migration assay involves digesting and centrifuging the processed NSCLC cells, then resuspending them in a serum-free medium containing 0.5% BSA. The resuspended cells are then counted and adjusted to a concentration of 2\u0026times;10\u003csup\u003e5\u003c/sup\u003e/mL. 200\u0026micro;L of the cell suspension is added to the upper chamber of an 8\u0026micro;m transwell chamber (Corning,3422), and the chamber is then transferred to a 24-well plate containing complete medium for 24 hours of culture. The chamber is collected, washed three times, fixed with 4% PFA (Beyotime, P0099) at room temperature for 20 minutes, and stained with crystal violet staining solution (Beyotime, C0121) for 1 hour. The cells above the filter membrane are gently wiped off with a cotton swab, washed three times, and then photographed under a microscope with five randomly selected fields. The invasion assay involves diluting matrigel and spreading it on the upper chamber of the transwell chamber. After the gel solidifies, the remaining steps are the same as those in the migration experiment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Mitochondrial Reactive Oxygen Species Detection\u003c/h2\u003e\u003cp\u003eTwo well-conditioned human NSCLC cell lines (shctrl and shFCRLB) were seeded in black-bottom, black-walled 96-well plates at a density of 1\u0026times;10⁵ cells/well and incubated overnight. Following a 1-hour incubation with a mitochondrial ROS fluorescent probe (Elabscience, E-BC-F008), fluorescence signal intensity was measured using a microplate reader(Tecan) in top-read mode at excitation/emission (Ex/Em) wavelengths of 510/610 nm, with the signal intensity of the blank control group (containing probe only) subtracted for normalization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Quantification of CCL2 Protein Concentration\u003c/h2\u003e\u003cp\u003eCell supernatants were collected from two NSCLC cell lines transfected with shCtrl or shFCRLB as experimental samples. CCL2 levels were quantified using a CCL2 ELISA kit (absin, abs510026). For detection, 100 \u0026micro;L of each sample or standard was added to the microplate wells and incubated for 2 hours. After washing, the primary antibody was added and incubated for another 2 hours. Subsequently, horseradish peroxidase (HRP) conjugate, chromogenic substrate, and stop solution (provided in the kit) were added sequentially. Absorbance was measured at 450 nm using a microplate reader (Tecan). CCL2 concentrations in the samples were calculated based on the standard curve, which was derived from the linear relationship between standard concentrations and their corresponding absorbance values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 assessment of FCRLB-Mediated Macrophage Polarization In Vitro\u003c/h2\u003e\u003cp\u003eSix-week-old healthy male C57BL/6 mice were intraperitoneally injected with thioglycolate broth (Merck, 70157) for 4 consecutive days, then sacrificed after 4 days of reaction. Inject cold PBS into the abdominal cavity with a syringe, aspirate the liquid, filter through a 70 \u0026micro;m cell sieve (Yeasen, 84702ES50), centrifuge, and resuspend with red blood cell lysis buffer (Beyotime, C3702). After lysis, centrifuge to collect cells. Plate LLC cells (shCtrl and shFCRLB) at appropriate density, culture for 24 hours to collect supernatant, filter through a 0.22 \u0026micro;m membrane, and add to adherent mouse macrophages for 24-hour co-culture. Incubate the treated macrophages with CD86(Elabscience, E-AB-F0994D) and CD206(Elabscience, E-AB-F1135E) antibodies in the dark for 15 minutes, then detect cell polarization by flow cytometry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll graphs were generated using GraphPad Prism (10.1.2). Statistical analyses of intergroup differences were performed using Student\u0026rsquo;s t-test, one-way analysis of variance (one-way ANOVA), and two-way analysis of variance (two-way ANOVA). Differences were considered statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOligonucleotides used in this research\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNucleic acid type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNucleic acid sequence\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman shRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScrambled control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTTCTCCGAACGTGTCACGTAA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCRLB-1#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCGCAAGCATTCTCTTTGAAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCRLB-2#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCGAATTTCTTTCAAAGCCAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMouse shRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCRLB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCTACTCTGGAGAAGCCTATA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman primer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18s sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCAGCCACCCGAGATTGAGCA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18s anti-sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAGTAGCGACGGGCGGTGTG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCRLB sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGTGGGCAAGCTGCTACTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCRLB anti-sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCGCTCCCCTTTGAAGATGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMouse primer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACTIN sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGTGACGTTGACATCCGTAAAGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACTIN anti-sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCCGGACTCATCGTACTCC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCRLB sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACCACCATCTTCAAGGGAGAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCRLB anti-sense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTACCAGAGAGTGCTAATGGGC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Transcriptome data analysis of NSCLC\u003c/h2\u003e\u003cp\u003eThrough differentially analysis of LUAD, lung squamous cell carcinoma (LUSC) data and GSE101929, we identified 5895 downregulated genes and 6034 upregulated genes (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB) in the LUAD data, respectively; 7843 downregulated genes and 7957 upregulated genes (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC) in the LUSC data; and 2526 downregulated genes and 2444 upregulated genes in the GSE101929 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). GO and KEGG enrichment of up-regulated genes revealed BP pathways such as chromosome segregation, DNA templated DNA replication, and DNA replication, The chromosome region, condensed chromosome and other CC pathways, as well as single stranded DNA helicase activity, catalytic activity, acting on DNA and other MF pathways, were significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), while the Biosynthesis of cofactors, one carbon pool by folate and carbon metabolism pathways were significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Subsequently, we conducted RF gene screening and Cox regression analysis on LUAD and LUSC using co upregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-G), and identified 12 genes that significantly affect the overall prognosis of lung cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Single-cell analysis of NSCLC\u003c/h2\u003e\u003cp\u003eTo further analyze the mechanism of the selected key genes, we used single-cell data GSE131907. We first conducted single-cell analysis on a total of 208506 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026amp;Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-C), calculated all cell markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and extracted single-cell data from tumor samples for subsequent analysis. We also performed expression localization analysis on twelve key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The results showed that SLC7A5, IER5L, ALDOA, FERMT1, and FCRLB genes were relatively highly expressed, but SLC7A5(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), IER5L(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), ALDOA(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), FERMT1(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) genes were studied more extensively, while the mechanism of FCRLB gene in lung cancer was less studied. Therefore, we chose FCRLB gene as the key gene for subsequent analysis. Through genetic mapping analysis, we found that the FCRLB gene is highly expressed in ts2 tumor cells and m0 Mac. Subsequently, we used the EMT gene set to score all tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E), and found that the EMT gene was highly expressed in ts2 and tumor ECs cells, indicating a higher degree of malignancy in ts2 cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Single-cell pseudo-temporal analysis\u003c/h2\u003e\u003cp\u003eTo further characterize ts2 cells, we first performed single-cell pseudo temporal analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Through analysis, it was found that ts2 is a mature differentiated cell, concentrated in the late stage of development. Ts2 is a mature differentiated cancer cell with specific functions. And through time-series gene analysis, we identified key genes that are highly expressed during ts2 differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C), such as A1BG, ACAP3, AAMDC, etc. Subsequently, in order to further analyze the function of ts2 cells, we used single-cell gene set analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) and found that ts2 cells highly expressed the reactive oxygen species pathway. After analyzing the key genes of the reactive oxygen species pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), PRDX4 and ATOX1 genes were identified as key genes in the pathway. Subsequently, we used UCell, AUCell, ssgsea, and Singscore for analysis and found that ts2 cells were highly expressed in the reactive oxygen species pathway among the four algorithm scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-I). Subsequently, we used GSVA scoring to evaluate the Hallmark gene of MSIGDB on TS2 cells and FCRLB\u0026thinsp;+\u0026thinsp;TS2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ-K). We found that both ts2 and FCRLB\u0026thinsp;+\u0026thinsp;ts2 overexpress the PI3K-AKT-MTOR signaling pathway. At the same time, we also conducted KEGG pathway analysis on FCRLB\u0026thinsp;+\u0026thinsp;ts2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL), and found that FCRLB\u0026thinsp;+\u0026thinsp;ts2 overexpresses key tumor related pathways such as TGF-BETA and Apoptosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Cell Communication Analysis\u003c/h2\u003e\u003cp\u003eTo analyze the overall role of FCRLB in cancer regulation, we selected tumor cells and cancer cells for cell communication analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D). Through pathway analysis and receptor pairing analysis, we found that ts2 can specifically affect mo-Mac through VEGF, which is consistent with the results of gene set analysis, promoting M2-like macrophage polarization and further deteriorating the tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Single-cell metabolic analysis\u003c/h2\u003e\u003cp\u003eIn order to further elucidate the role of the FCRLB gene in the malignant progression of cancer cells, we divided the FCRLB gene expression into FCRLB\u0026thinsp;+\u0026thinsp;ts2 cells and FCRLB-ts2 cells, and performed single-cell metabolic analysis on these two cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The results showed that the metabolic pathways of pyridine metabolism and Ubquinone and other terpenoid quinone biosynthesis were significantly upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). After analyzing the metabolomics of lung cancer blood samples and tissue samples, we found that the metabolic pathways of pyridine metabolism and Ubquinone and other terpenoid quinone biosynthesis were also significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). And at the same time, we selected key metabolites of the pyramididine metabolism and Ubquinone and other terpenoid quinone biosynthesis metabolic pathways from metabolomics data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D, F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.6 mo-Mac subgroup analysis\u003c/h2\u003e\u003cp\u003eSubsequently, we conducted a detailed analysis of another key localization cell-mo-Mac. We first analyzed the immune cell microenvironment of single-cell normal samples and tumor samples, and found that the infiltration rate of mo-Macs significantly increased in lung cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Subsequently, we conducted gene set analysis on three types of Macs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D) and found that mo Macs highly expressed the Hedgehog pathway. Through key gene analysis of the Hedgehog pathway, we identified the VEGF gene as the pathway key gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). At the same time, we used FCRLB expression to group mo-Macs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), and then performed GSVA analysis on FCRLB\u0026thinsp;+\u0026thinsp;mo-Macs and mo Macs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG-H). The results showed that mo-Macs overexpressed pathways such as inflammatory response and hypoxia, while FCRLB\u0026thinsp;+\u0026thinsp;mo-Macs overexpressed the reactive oxygen species pathway and PI3K-AKT-MTOR signaling pathway. According to the previous study(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), Mac can achieve M2 canopy polarization through the Hedgehog pathway. We used the M2 marker for localization and found (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI) that the co localization result of CD163 and CD206 genes was mo-Mac, which is consistent with our hypothesis. Pseudo-temporal analysis revealed that mo-Macs are likely undergoing a dynamic process of differentiation or functional remodeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Validation that the FCRLB gene promotes malignant progression of NSCLC\u003c/h2\u003e\u003cp\u003eTo confirm the role of FCRLB in promoting the malignant progression of NSCLC, we utilized shRNA-mediated knockdown to downregulate FCRLB expression in these two cell lines and verified the knockdown efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). Knockdown of FCRLB can also significantly inhibit the growth rate of tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). We further investigated the role of FCRLB in promoting metastasis and invasion; consistent with our predictions, the metastatic and invasive capacities of NSCLC cell lines were significantly impaired following FCRLB knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-H). We further verified the function of fcrlb in NSCLC. Mitochondrial reactive oxygen species levels were significantly reduced following FCRLB knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Further quantification of CCL2 protein levels in cell supernatants demonstrated that FCRLB can stimulate CCL2 production (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), while FCRLB knockdown decreased the activation of the PI3K-Akt signaling pathway upstream of CCL2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-D). Results from macrophage polarization assays demonstrated that FCRLB knockdown suppressed M2 polarization (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). We established a mouse subcutaneous tumor model using FCRLB-knockdown LLC1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Compared with the shctrl group, FCRLB knockdown significantly reduced tumor volume and weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-D). IHC staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE) of tumor samples demonstrated increased infiltration of M2-like macrophages and elevated CCL2 expression levels in the presence of FCRLB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTumor-infiltrating macrophages, despite their phagocytic capacity to clear tumor cells, are predominantly polarized into pro-tumor M2 cells. These cells extensively secrete immunosuppressive factors (like IL-10, (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)) and angiogenesis factors (like VEGF, MMP2) while highly expressing immune checkpoints such as PD-L1. They are widely recognized as core contributors to tumor progression and immune suppression, severely hindering the efficacy of immunotherapy and accelerating tumor growth. Therefore, reducing macrophage recruitment and M2 polarization is considered key to eliminating the adverse effects associated with TAMs(\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Interestingly, previous studies have demonstrated that tumors can remodel the tumor microenvironment by secreting metabolites. These metabolites modulate macrophage polarization toward the M2 subtype via the Hedgehog signaling pathway, thereby promoting malignant proliferation(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Through identification, it has been demonstrated that metabolites modulate the chemotaxis and infiltration of TAMs by regulating the expression of multiple cytokines, including CCL2, in tumor cells(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The Hedgehog signaling pathway is recognized as a critical driver of tumor progression across diverse tumor models and is linked to enhanced macrophage infiltration and polarization(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Tumor-derived CCL2 serves to recruit macrophages and promote M2 polarization, with activation of the Hedgehog signaling pathway in TAMs exerting a critical role in this process(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we found that FCRLB gene was highly expressed in ts2 tumor cells, and EMT gene set score showed that ts2 cells were highly malignant. Signaling pathway enrichment analysis revealed that mo-Mac cells exhibit upregulation of the Hedgehog signaling pathway. Analyses of FCRLB-positive and FCRLB-negative subgroups yielded results similar to those from the ts2 analysis. CCL2, a downstream effector molecule of the PI3K signaling pathway(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), effectively activate the Hedgehog signaling pathway to induce macrophage M2-polarization, as noted previously. Furthermore, results from cell-cell communication analyses support specific CCL2-mediated crosstalk between ts2 and mo-Mac cell populations. To obtain a more comprehensive understanding of the malignant functions of FCRLB, metabolic analyses of FCRLB-positive and FCRLB-negative populations revealed significant upregulation of pyrimidine metabolism as well as ubiquinone and other terpenoid-quinone biosynthesis pathways, with key metabolites identified. Results from HPLC further demonstrated changes in these metabolites following FCRLB overexpression and knockout.\u003c/p\u003e\u003cp\u003eFCRLB, a newly identified member of the Fc receptor-like family, was initially believed to exert an immunomodulatory role(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Previous studies have demonstrated that FCRLB plays a critical role in the inhibitory pathway of B cell signaling and can effectively suppress key downstream signaling pathways in B cells(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Some studies have demonstrated that FCRLB serve as a favorable prognostic factor in chronic lymphocytic leukemia and regulate immune activation processes during malignant progression(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, research on this gene remains limited; its specific functions, involved signaling networks, and regulatory roles in solid tumors are still poorly understood. Herein, through the integration of multi-omics and machine learning, advanced transcriptome analyses, and validation in cellular models, animal models, and clinical samples, we have, for the first time, comprehensively investigated the characteristics of FCRLB in promoting malignant progression in NSCLC and elucidated its regulatory role in tumor-macrophage crosstalk. This not only broadens our understanding of the signaling networks underlying tumor remodeling of the TME but also identifies highly promising therapeutic targets.\u003c/p\u003e\u003cp\u003eOur work also has some limitations. Firstly, although we have discovered the pro-tumor regulatory role of FCRLB in NSCLC, the specific signaling mechanism is still unclear, and its natural ligand is still unknown. Further research on FCRLB monoclonal antibodies and epigenetics is needed to investigate the association between malignant pathways and FCRLB. Secondly, although CCL2 has been found to play an important role in the ts2-mo-Mac signaling interaction in our cell communication research and has received significant attention as a recognized macrophage infiltration factor, other FCRLB mediated cytokines also require comprehensive research. Multi factor testing may provide us with stronger support in subsequent research. Then, further exploration is needed to identify small molecule targeted drugs for FCRLB, including screening small molecule drug libraries, screening suitable small molecule compounds, and verifying their functions in animal models, which can better demonstrate the clinical potential of this target.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe screened the key gene FCRLB that affects the prognosis of lung cancer through transcriptome gene screening, and analyzed the unique role of FCRLB in tumor cells and macrophages. Tumor cells with high expression of FCRLB showed high activation of PI3K and reactive oxygen pathways, regulating pyridine metabolism and other terpenoid quinone biosynthesis pathways, inducing CCL2 expression. Also, high FCRLB expression in mo-Mac, promote M2-like polarization, and further exacerbate the malignancy of lung cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFCRLB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFc receptor-like B\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-small cell lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereactive oxygen species\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCCL2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-C motif chemokine ligand 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etumor microenvironment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTAMs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etumor-associated macrophages\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emo-Macs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emonocyte-derived macrophages\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elung adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWestern blot\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCCK8\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCell Counting Kit-8\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eone-way ANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eone-way analysis of variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003etwo-way ANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etwo-way analysis of variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elung squamous cell carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal study was reviewed and approved by the Animal Experiment Ethics Committee of Zhejiang University before implementation (ZJU20250631).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to publish was obtained from the study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (No. 82203608 to X.Z.Y. and No. 32300753 to S.Y.L.), Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China (LHDMY23H160002 to X.Z.Y.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNueraili Maihemuti, Yueli Shi, Sujing Jiang, Pan Liu, Zhen Shi and Zhiyong Xu designed the study, performed experiments, analyzed data, and wrote the manuscript. Nueraili Maihemuti, Yueli Shi and Sujing Jiang performed experiments and analyzed data. Zhiyong Xu, Zhen Shi and Pan Liu conceived and designed the study and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the data provided by databases including TCGA and GEO. Appreciation is expressed to the reviewers and editors for their constructive comments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10-45.\u003c/li\u003e\n\u003cli\u003eMeyer ML, Fitzgerald BG, Paz-Ares L, Cappuzzo F, J\u0026auml;nne PA, Peters S, et al. New promises and challenges in the treatment of advanced non-small-cell lung cancer. Lancet. 2024;404(10454):803-22.\u003c/li\u003e\n\u003cli\u003eLiu M, Hu S, Yan N, Popowski KD, Cheng K. 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Blood. 2008;112(1):179-87.\u003c/li\u003e\n\u003cli\u003eShea LK, Honjo K, Redden DT, Tabengwa E, Li R, Li FJ, et al. Fc receptor-like 2 (FCRL2) is a novel marker of low-risk CLL and refines prognostication based on IGHV mutation status. Blood Cancer J. 2019;9(6):47.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"None-small cell lung cancer, Immunotherapy, Machine learning, multi-omics","lastPublishedDoi":"10.21203/rs.3.rs-7784814/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7784814/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAnti\u0026ndash;PD-1 therapy has improved outcomes in non-small cell lung cancer (NSCLC), yet primary and acquired resistance remain common. Pinpointing regulators of tumor\u0026ndash;immune crosstalk is therefore critical to enhance therapeutic efficacy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe integrated multi-omics sequencing with ensemble machine-learning to nominate prognostic genes in NSCLC, followed by pathway and intercellular-signaling analyses (pseudotime analysis, GSVA, and functional enrichment). We generated shRNA-mediated FCRLB-knockdown NSCLC cell lines and performed molecular and immunological assays. A murine lung-cancer xenograft model was used to validate the role of the candidate gene in tumor progression and its impact on the tumor microenvironment (TME).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified FCRLB as a key gene influencing lung cancer prognosis via transcriptomic gene screening, and elucidated its unique roles in tumor cells and macrophages. FCRLB-highly expressing tumor cells exhibited high activation of the PI3K signaling and reactive oxygen species (ROS) pathways, regulated metabolic pathways such as pyridine metabolism and terpenoid quinone biosynthesis, and induced C-C motif chemokine ligand 2 (CCL2) production. Additionally, high FCRLB expression in monocyte-derived macrophages (mo-Macs) promoted M2-like polarization, thereby further exacerbating lung cancer malignancy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur data identify and functionally validate FCRLB as a pro-tumor regulator in NSCLC.FCRLB regulates the secretion of multiple cytokines (including CCL2) by tumor cells via the ROS pathway and PI3K signaling pathway, thereby promoting M2 polarization of macrophages. Additionally, FCRLB exerts a positive regulatory effect on the malignant progression of NSCLC. This finding provides a potential novel target for overcoming immunotherapys resistance in NSCLC.\u003c/p\u003e","manuscriptTitle":"FCRLB-mediated dual control of tumor metabolism and macrophage polarization promotes lung cancer malignancy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 09:30:51","doi":"10.21203/rs.3.rs-7784814/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-10-21T13:37:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T07:06:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-17T12:25:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-10-16T09:33:47+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor revision","date":"2025-10-15T14:38:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85513306-1739-4087-8c8f-e514e10a0d44","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:11:11+00:00","versionOfRecord":{"articleIdentity":"rs-7784814","link":"https://doi.org/10.1186/s12967-026-07872-1","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2026-02-16 15:56:52","publishedOnDateReadable":"February 16th, 2026"},"versionCreatedAt":"2025-10-31 09:30:51","video":"","vorDoi":"10.1186/s12967-026-07872-1","vorDoiUrl":"https://doi.org/10.1186/s12967-026-07872-1","workflowStages":[]},"version":"v1","identity":"rs-7784814","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7784814","identity":"rs-7784814","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}