OR7E47P regulates TGF-β secretion of fibroblast via ROBO2 pathway to modulate the TME in NSCLC

OR7E47P regulates TGF-β secretion of fibroblast via ROBO2 pathway to modulate the TME in NSCLC | 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 OR7E47P regulates TGF-β secretion of fibroblast via ROBO2 pathway to modulate the TME in NSCLC Haohan Zhang, Jie Wu, Lan Li, Jing Li, Xinyi Zhao, Hao Zhong, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5809404/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Whereas the role of OR7E47P in NSCLC is not clear, we explored the impact of OR7E47P on the prognosis of NSCLC patients and possible mechanisms through bioinformatics and experi-mental approaches. OR7E47P, underexpressed in NSCLC tumor tissues, is associated with better prognosis and enhanced immune benefits. Localization analyses showed OR7E47P may func-tions as a cytoplasmic processing pseudogene, regulating ROBO2 expression via the OR7E47P/miR-183-5p/ROBO2 pathway at the post-transcriptional level. Single-cell analyses re-vealed that ROBO2 reduces the tumor-supportive role of cancer-associated fibroblasts (CAFs) by promoting programmed cell death of FAP/TGFB1 + CAFs, enhancing immune infiltration, and suppressing the TGF-β/Smad signaling pathway in fibroblasts and NSCLC cells. Finally, we con-firmed our findings through experiments, where OR7E47P directly binds to miR-183-5p, with a binding site that allows for endogenous competition, leading to the upregulation of ROBO2 ex-pression and potentially regulate TGF-β secretion of fibroblast, thereby regulating the TME in NSCLC. OR7E47P NSCLC miR-183-5p Immunotherapy ROBO2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) being the predominant pathological subtype. In recent years, immunotherapy, particularly therapies targeting PD-1/PD-L1, has demonstrated significantly greater efficacy in prolonging overall survival (OS) and progression-free survival (PFS) in NSCLC patients compared to conventional chemotherapy. With the rapid advancements in sequencing technologies, the molecular characteristics and comprehensive landscape of NSCLC are being increasingly elucidated [1–3] . Various studies were focused on identifying novel therapeutic strategies and targets to enhance the effectiveness of treatment for NSCLC, particularly through targeted and immunotherapeutic approaches. With the widespread adoption and advancement of sequencing technologies, pseudogenes have increasingly entered the research spotlight. As non-coding genes with high sequence homology to functional protein-coding genes, pseudogenes serve as crucial regulatory factors influencing gene expression at epigenetic, transcriptional, and post-transcriptional levels. They are involved in various biological processes, including cell differentiation and apoptosis, and their dysregulation is closely associated with the development and progression of multiple diseases, including malignancies [4–8] . Olfactory receptors (ORs), the largest G protein-coupled receptor superfamily, are predominantly expressed in the cilia of olfactory sensory neurons, where they facilitate the recognition of diverse odors [9] . ORs also play critical roles in tumorigenesis, cancer progression, and antitumor therapy [10–13] . Among the OR family, only 40% of OR genes possess intact coding regions, with potential functional roles inferred, while the remaining OR genes are classified as pseudogenes [14] . One such pseudogene, olfactory receptor family 7 subfamily E member 47 pseudogene (OR7E47P), is an unprocessed pseudogene widely expressed in lung tissue. However, its role in lung cancer remains unreported. This study explores the impact of OR7E47P expression on the prognosis of NSCLC patients, utilizing publicly available databases and immunotherapy data from our institution. Subsequently, the OR7E47P/miR-183-5p/ROBO2 pathway’s localization and function within the NSCLC tumor microenvironment were investigated and validated through approaches such as Lnclocator analysis, in situ hybridization on tissue microarrays, and single-cell analysis. Finally, in vitro experiments were conducted to confirm the role of this pathway. 2 Results 2.1 OR7E47P is Closely Associated with Prognosis, Clinical Characteristics, and Treatment Sensitivity in NSCLC Patients In this study, we compared the expression of OR7E47P between normal tissues and NSCLC patient cohorts using data from the TCGA and GEO databases. The expression of OR7E47P in normal tissues was significantly higher than in LUAD and LUSC patient samples across both datasets (Fig. 1 A and 1 B). To investigate the relationship between OR7E47P expression and patient prognosis, we categorized NSCLC patients based on OR7E47P expression levels and plotted Kaplan-Meier (KM) survival curves. Log-rank test and multivariate Cox regression analy-sis revealed that higher OR7E47P expression was significantly associated with better overall survival (OS) in NSCLC patients (P = 0.0026). Furthermore, multivariate analysis considering factors such as age, gender, and TNM stage identified low OR7E47P expres-sion as an independent poor prognostic factor for NSCLC (OR7E47P high expression group vs. low expression group, HR = 0.668, P = 0.0242) (Fig. 1 C). To further validate these findings, we collected NSCLC tissue microarrays and paraf-fin-embedded tissue samples from 53 NSCLC patients who underwent immunotherapy at the Cancer Center of Wuhan University People's Hospital from June 2020 to February 2023. In situ hybridization (ISH) was performed on tissue microarrays, and comparison of paired normal and tumor tissues confirmed that OR7E47P expression was significantly lower in lung tumor tissues than in adjacent normal tissues. Additionally, M stage analy-sis showed that M0 stage patients had significantly higher OR7E47P expression com-pared to M1 stage patients. For TNM staging, patients in stages I/II exhibited higher OR7E47P expression than those in stages III/IV. Similar results were obtained in the pa-tient cohort from Wuhan University People's Hospital (Fig. 1 E). Moreover, in the cohort of NSCLC patients receiving immunotherapy at Wuhan Uni-versity People's Hospital, high OR7E47P expression was significantly associated with better OS and PFS compared to low expression groups. The objective response rate (ORR) (68.8% vs. 34.4%) and disease control rate (DCR) (93.8% vs. 65.6%) were also significantly higher in the high OR7E47P expression group (Fig. 1 F). 2.2 Subcellular Localization and Pathway Prediction of OR7E47P In this study, Lnclocator was utilized to analyze the subcellular localization of OR7E47P transcripts. By integrating the contribution scores of each nucleotide for locali-zation, we predicted that OR7E47P is primarily located in the cytoplasm (Fig. 2 A). Analysis of the OR7E47P sequence using the NCBI and Genecards databases revealed that OR7E47P exhibits characteristics typical of a retrotransposed pseudogene. Based on this, we hypothesize that OR7E47P functions as a processed pseudogene and plays a role post-transcriptionally through the competing endogenous RNA (ceRNA) mechanism. Building on this hypothesis, we performed a comprehensive analysis in the TCGA database, where we selected 438 miRNAs associated with OR7E47P and 622 miRNAs that showed differential expression between high and low OR7E47P expression groups (with 296 miRNAs found in the intersection). Among these, miR-183-5p was identified as the only miRNA that satisfies all the criteria for potential regulation by OR7E47P. Addi-tionally, Annolnc prediction indicated that miR-183-5p and miR-338-3p could be jointly regulated by all three OR7E47P transcript variants. Therefore, miR-183-5p is the most likely miRNA targeted and regulated by OR7E47P. Using RNAhybrid, we predicted the potential binding sites between miR-183-5p and OR7E47P. The results revealed three pos-sible binding sites, suggesting that miR-183-5p and OR7E47P might interact through these sites (Fig. 2 B). Further, based on the ceRNA hypothesis, we screened for target genes potentially regulated by OR7E47P via miR-183-5p. Using the Starbase database and differential ex-pression filtering, we identified several candidate genes including TAC1, FBN2, LIN28B, SCARNA1, NKX2-4, SNORD15B, NKX2-5, CA12, ROBO2, GJA3, and AKAP12 as potential targets of the OR7E47P/miR-183-5p axis (Fig. 2 C). To refine these candidates, we used univariate Cox regression analysis in the TCGA and KM-plotter databases to calculate HR and P-values. Genes that showed consistent trends (HR > 1 or HR < 1) and P-values < 0.05 were selected as candidate target genes, with ROBO2, FBN2, and NKX2-5 being identified as the final candidates (Fig. 2 D). Subse-quently, using methods such as TargetScanSites, we predicted potential binding sites for the candidate genes. Among the five methods, three predicted a binding site between ROBO2 and miR-183-5p, suggesting a higher likelihood of interaction compared to the other genes (Fig. 2 E). Therefore, we conclude that ROBO2 is the most likely target gene regulated by miR-183-5p. 2.3 Subcellular Localization and Pathway Prediction of OR7E47P In this study, in situ hybridization experiments on paraffin-embedded tissue slices from clinical patients revealed that OR7E47P is expressed not only in tumor cells but also in stromal cells (Fig. 3 A). To further investigate the expression and regulatory function of OR7E47P and ROBO2 in the TME, we analyzed the GSE127465 single-cell RNA-seq dataset. The TME cells from NSCLC were annotated into seven subgroups: B cells, epithe-lial cells, fibroblasts, monocytes, neutrophils, NK cells, and T cells. OR7E47P was pre-dominantly expressed in epithelial cells and fibroblasts, while ROBO2 expression was primarily seen in fibroblasts (Fig. 3 B). This suggests that the OR7E47P/miR-183-5p/ROBO2 axis likely exerts its main regulatory effects within fibro-blasts. To explore the potential regulatory role of this pathway in immune therapy, we fur-ther analyzed the GSE146100 immune therapy single-cell dataset. In this dataset, ROBO2 was found to be primarily expressed in fibroblasts, with some expression in epithelial cells. By comparing immune therapy responders (R) and non-responders (NR), we ob-served that ROBO2 expression was significantly higher in the fibroblast subpopulation of immune therapy responders (Fig. 3 C). Given that ROBO2 is mainly expressed in TME fibroblasts, we extracted all fibro-blasts from the dataset and performed dimensional reduction. We classified the fibro-blasts into four clusters and annotated them based on cell markers to assess the status and function of cancer-associated fibroblasts (CAFs) in the TME. Cluster C0 was characterized by high expression of FAP (Fibroblast Activation Protein Alpha) and TGFB1 (Transform-ing Growth Factor Beta 1), marking the presence of activated CAFs. Cluster C1 specifically expressed CFD (Complement Factor D), Cluster C2 exhibited expression of the fibroblast marker VIM (Vimentin), and Cluster C3 showed high expression of MCAM (Melanoma Cell Adhesion Molecule). Notably, ROBO2 was specifically highly expressed in FAP/TGFB1 + CAFs (Fig. 3 D). To accurately annotate the function of ROBO2 in FAP/TGFB1 + CAFs, we categorized these cells into high and low ROBO2 expression groups and performed differential gene expression analysis. The results revealed that in the high ROBO2 expression group, there was significant enrichment of pathways involved in immune response to tumor cells, programmed cell death involved in cell development, and T-cell receptor complex (Fig. 3 E). To further investigate how FAP/TGFB1 + CAFs influence the other cell populations in the TME, we assessed the differences in cell-cell communication when ROBO2 expression varied. The results were visualized to highlight the interactions (Fig. 3 F). In ROBO2 high expression CAFs, the TME fibroblasts were more susceptible to regulation by mac-rophage TNF signaling, which suppressed CAF growth. In contrast, ROBO2 low expres-sion CAFs were more prone to activation by TGF-β and Bone Morphogenetic Protein (BMP) signaling pathways. TGF-β 1/2/3 and BMP signaling are key sources of SMAD transcrip-tion factor activation, which mediates downstream effects. These findings suggest that ROBO2 in the TME, particularly within FAP/TGFB1 + CAFs, plays a crucial role in modulating tumor cell malignancy and immune responses, potentially impacting tumor progression and therapy responses. 2.4 OR7E47P upregulation significantly inhibits the proliferation and clonal formation ability of MRC-5 cells Building on the previous results, we first established an OR7E47P overexpression stable MRC-5 cell line using lentivirus (Fig. 4 A). The results showed that in the overexpression group, both OR7E47P and ROBO2 expression levels were significantly upregulated, while miR-183-5p ex-pression was significantly downregulated (Fig. 4 B). To further confirm the impact of OR7E47P overexpression on ROBO2 and related pathways, we performed Western blotting (WB) to detect the expression of ROBO2, TGF-β (TGFB1), and SMAD2 phosphorylation in fibroblasts after overexpression of OR7E47P. Compared with the control group, the ROBO2 protein expression was significantly upregulated, whereas the TGFB1 protein and the phosphorylation level of SMAD2 were significantl Furthermore, through the detection of cell proliferation activity by CCK-8 assay and colony formation assay on plate, we found that the proliferation activity and clonal formation ability of MRC-5 cells were significantly inhibited when OR7E47P was upregulated (Fig. 4 D and 4 E). The Transwell assay showed that the upregulation of OR7E47P in MRC-5 cells significantly in-hibited the cell migration and invasion ability (Fig. 4 F and 4 G). These findings suggest that OR7E47P can regulate the expression of miR-183-5p and ROBO2 in MRC-5 cells and inhibit the TGF-β/SMAD signaling pathway. This supports the hypothesis that OR7E47P affects ROBO2 expression through miR-183-5p regulation in fibroblasts, impacting key signaling pathways in TME. 2.5 Validation of OR7E47P Regulating ROBO2 Expression through miR-183-5p in vitro To verify that OR7E47P can target the expression of miR-183-5p, the study con-structed both wild-type (WT) and mutant (Mut) OR7E47P plasmids, integrating them into the 3' UTR of R-Luc. These constructs were co-transfected with miR-183-5p mimics into MRC-5 cells for luciferase activity assays. Compared with the miR-NC and mutant OR7E47P groups, the mutant OR7E47P group co-transfected with miR-183-5p mimics showed no significant change in luciferase activity, indicating that the mutant form of OR7E47P cannot interact with miR-183-5p. However, when wild-type OR7E47P was co-transfected with miR-183-5p, luciferase activity was significantly reduced compared to the miR-NC and wild-type OR7E47P groups, confirming that miR-183-5p directly targets and binds to OR7E47P (Fig. 5 A). To investigate the regulatory effects of miR-183-5p and ROBO2 on each other, the study performed a recovery experiment by reintroducing miR-183-5p mimics into OR7E47P-overexpressing stable MRC-5 cells. RT-qPCR analysis of ROBO2 and TGFB1 protein expression revealed that in the recovery group, ROBO2 expression was signifi-cantly reduced, while TGFB1 expression was significantly increased compared to the overexpression group (Figs. 5 B and 5 C). Subsequently, this study further compared the changes in cell phenotypes among the groups using CCK8 cell proliferation, scratch, and Transwell assays. In the recovery experiment, the cell phenotypes were all restored (Fig-ures 5D, 5E and 5F). In the same way as the above experiment, co-transfection of wild-type ROBO2 with miR-183-5p significantly reduced luciferase activity, indicating that miR-183-5p directly regulates the expression of ROBO2 (Fig. 5 G). Additionally, siRNA-mediated knock-down of ROBO2 was performed in the OR7E47P stable MRC-5 cell line, and the expres-sion levels of OR7E47P and miR-183-5p were analyzed by RT-qPCR. The results showed that the regulation of ROBO2 expression had no significant impact on OR7E47P and miR-183-5p expression. Furthermore, in the recovery group, ROBO2 expression was sig-nificantly reduced, and TGFB1 protein expression was significantly elevated compared to the overexpression group (Figures H and 5I). The phenotypic experiment also yielded similar results (Figs. 5 J, 5 K and 5 L). These results demonstrate that miR-183-5p can directly regulate the expression of ROBO2, and OR7E47P modulates the miR-183-5p/ROBO2 axis to influence the TGF-β signaling pathway. 2.6 Effect of Differential Expression of OR7E47P in MRC-5 Cells on TGF-β/Smad Signaling Pathway in A549 Cells To assess whether the differential expression of OR7E47P in MRC-5 cells influences A549 cells, a co-culture experiment was conducted (Fig. 6 A). ELISA was first used to measure the levels of TGF-β in both the upper and lower chambers of the co-culture sys-tem. The results showed that upon OR7E47P overexpression, the secretion of TGF-β from MRC-5 cells was significantly reduced (Fig. 6 B). Subsequently, proteins from A549 cells co-cultured with MRC-5 cells were extracted and analyzed by Western blotting (WB). The findings revealed that TGF-β/Smad signaling pathway proteins, specifically TGFB1 and phosphorylated SMAD2, showed significantly reduced expression in A549 cells co-cultured with OR7E47P-overexpressing MRC-5 cells (Fig. 6 C). These results suggest that OR7E47P in MRC-5 cells can modulate the TGF-β/Smad signaling pathway in A549 cells, potentially influencing tumor progression. 3 Methods Data Sources This study utilized multiple publicly available and institutional datasets. RNA se-quencing data (FPKM normalized) for NSCLC patients were downloaded from The Can-cer Genome Atlas (TCGA) via the Genomic Data Commons Data Portal ( https://portal.gdc.cancer.gov/ ). Corresponding clinical information for the TCGA cohort was retrieved from the UCSC Xena database ( https://xena.ucsc.edu/ ). Single-cell RNA se-quencing datasets, GSE127465 and GSE146100, were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/gds/ ). NSCLC tissue microar-rays (product number: ZL-LugA961) were procured from Shanghai Outdo Biotech Co., Ltd. Paraffin-embedded tumor tissue sections and corresponding clinical information were collected from 53 NSCLC patients who received immunotherapy at the Cancer Center of Renmin Hospital of Wuhan University between June 2020 and February 2023. These samples were used for in situ hybridization (ISH) analysis and subsequent investigations of the pseudogene OR7E47P. Cell Lines A549 (Human NSCLC Cell Line) was cultured in complete medium comprising 89% Ham's F-12K (Kaighn's) Medium, 10% fetal bovine serum (FBS), and 1% penicil-lin-streptomycin. MRC-5 (Human Fibroblast Cell Line) was cultured in complete medium comprising Minimum Essential Medium (MEM), 10% FBS, and 1% penicil-lin-streptomycin. The cells were maintained at 37°C in a 5% CO2 incubator. Single-Cell Analysis Methods The single-cell analysis workflow employed in this study involved the following steps. Single-cell RNA sequencing data were processed using the Seurat package in R. Key steps included data loading, normalization, scaling, principal component analysis (PCA), and clustering of subpopulations. The Uniform Manifold Approximation and Projection (UMAP) algorithm was applied for visualization. Differentially expressed markers for identified clusters were determined using the FindAllMarkers function with the default non-parametric Wilcoxon rank-sum test. Pre-liminary annotation of genes was conducted using the SingleR package, and the expres-sion of the target gene in different cellular subpopulations was analyzed. Gene Ontology (GO) gene sets (including Molecular Function, Cellular Component, and Biological Process) from the MSigDB database were utilized for GSEA.Differentially expressed genes between groups were filtered by setting a threshold of P 1 and P < 0.05 were considered significantly en-riched and were analyzed in the context of ROBO2 differential expression groups. The CellphonedDB package [15] was employed to analyze interactions among sub-populations within the NSCLC tumor microenvironment (TME). Raw count matrices and cell type annotation files, extracted from Seurat objects, were used as inputs. Interaction frequencies between pairs of subpopulations were calculated, and the intensity of poten-tial ligand-receptor interactions was predicted based on mean expression levels. Isolation of Total RNA and Reverse Transcription Quantitative Real-Time PCR (RT–qPCR) . Triquick Reagent (Solarbio, Beijing) was used to isolate total RNA from the cells. cDNA was used for quantitative real-time polymerase chain reaction (qRT-PCR) with forward and reverse primers and iTaq Universal SYBR Green PCR Master Mix (Bio-Rad, USA). The data were analyzed using the 2 − ΔΔCt method. The primers used in this study are shown in Table 1 . Table 1 Primers used in quantitative real-time PCR. Gene Forward primer(5′-3′) Reverse primer (5′-3′) OR7E47P ACAACTTGATTGCCTTACTAATGAC CATAAAATCAGCAAACAACTGACAG GAPDH TCGCCAGCCGAGCCACATC CGTTCTCAGCCTTGACGGTGC U6 CTCGCTTCGGCAGCACAT AACGCTTCACGAATTTGCGT ROBO2 CCAAAGTCCCGACTGAACGA TGGTGATCTGGAGGAGGAGG miR-183-5p GGCCTATGGCACTGGTAGAAT CTCAACTGGTGTCGTGGAGTC Western Blotting Analysis Total protein was isolated from cells using RIPA buffer (high) obtained from Solarbio (Beijing), and its concentration was determined with a bicinchoninic acid kit (Applygen, Beijing) following the manufacturer's guidelines. 20 µg protein was separated using 10% SDS polyacrylamide gel electrophoresis, transferred to polyvinylidene difluoride mem-branes (pore size: 0.45 µm, Merck Millipore, Ireland), blocked with 5% skimmed milk, and incubated with primary antibodies overnight at 4°C. The membranes were washed four times using 0.1% TBST, incubated with secondary antibodies, and were visualized using an ultrahigh-sensitive ECL kit (Biosharp, Beijing). Antibodies used were as follows: an-ti-ROBO2 (121635-1-AP, Proteintech), anti-TGFB1 (26155-1-AP, Proteintech), anti-SMAD2 (#5339, Cell Signalling), anti-Phospho-SMAD2 (#18338, Cell Signalling), anti-GAPDH (10494-1-AP, Proteintech). Image Lab software (Version 5.2; Bio-Rad Laboratories, USA) was employed to quantify the signal intensity. Dual-Luciferase Reporter Gene Assay Follow the instructions of the Dual Luciferase Reporter Gene Assay Kit (RG008, Be-yotime) and LimitlessTM ELZ Fushion kit (LEF01, ELK biotechnology). Target sequences for OR7E47P and ROBO2 were amplified and ligated into the pGL6 vector with overlapping sequences. After enzymatic digestion, the ligation products were recombined with the vector. The recombinant plasmids were transformed into high-efficiency DH5α competent cells (EC001, ELK biotechnology). Single colonies were selected after one day of incubation and subjected to sequencing to confirm successful vector construction. Cells were pre-pared and transfected with the reporter gene complexes. Following transfection, the cells were cultured for an additional 24 hours. A dual-luciferase reporter assay was performed to measure the luminescence signals. ELISA Assay Follow the instructions of the Human TGFb1 ELISA Kit (ELK1185, ELK Biotechnolo-gy). Add standards and samples to the wells of a microtiter plate. Add biotin-conjugated TGF-β1-specific antibodies to each well. Add horseradish peroxidase (HRP)-conjugated streptavidin and incubate. Add substrate solution to initiate the colorimetric reaction. Ob-serve color changes. Add sulfuric acid to stop the enzyme-substrate reaction. Measure the optical density (OD) at 450 nm using a spectrophotometer. Generate a standard curve based on the OD values of the standards and calculate the TGF-β1 concentration in the samples. Statistical Methods The optimal cutoff points for all continuous variables were determined using the "survminer" package in R. Spearman's correlation method was employed to calculate correlation coefficients and p-values for correlation analyses. Statistical differences in dis-tributions were assessed as follows: Kruskal-Wallis test for comparisons among three or more groups. Wilcoxon test for comparisons between two groups. Statistical analyses for experimental data were performed using GraphPad Prism 8.0. Quantitative data were ex-pressed as the mean ± standard deviation (x̄ ± s). Student’s t-test for pairwise comparisons between groups. One-Way ANOVA for analyzing differences among multiple treatment groups. Results were considered statistically significant if P < 0.05. 4 Discussion Based on the TCGA and GEPIA databases, as well as tissue microarrays and clinical samples from NSCLC patients at our institution, this study confirmed that OR7E47P ex-pression is significantly lower in lung tumor tissues compared to normal lung tissues. Furthermore, the expression of OR7E47P is closely associated with patient prognosis and clinical features. Pseudogenes can be classified into unitary pseudogenes, unprocessed pseudogenes, and processed pseudogenes. Among these, processed pseudog`enes, also known as retroposed pseudogenes, are generated through the reverse transcription of mRNA into cDNA, which is then integrated into the genome [16,17] . This process results in the creation of a pseudogene that is located at a position in the genome different from that of the parental gene, and often on a different chromosomeh analysis of the OR7E47P sequence via NCBI and Genecards databases, it was found that OR7E47P is located at 12q13.13, while its parental gene, OR7E24, is located at 19p13.2. Notably, OR7E47P contains repetitive sequences at both the 5' and 3' ends and retains a polyA tail. These features align with the characteristics of a retroposed pseudogene, which is generated through the reverse transcription process, and the presence of a polyA tail at the 3' end suggests that this pseudogene likely retains miRNA binding sites from the ancestral gene. As a result, this type of gene tends to have a higher likelihood of interacting with miRNAs, especially due to the preserved 3' end structure and the ability to act as a ceRNA (competing endogenous RNA), potentially sequestering miRNAs and affecting the expression of target genes. Additionally, OR7E47P, being a processed pseudogene, may regulate gene expression through the miRNA pathway, and functioning as a ceRNA to modulate the expression of target genes such as ROBO2. This study hypothesizes OR7E47P, through its interaction with miR-183-5p, may regulate the expression of ROBO2, and further highlighting the potential role of OR7E47P in modulating critical pathways like TGF-β/Smad signaling and influencing tumor progression [18,19] . Based on single-cell analysis, this study further investigates the functional role of the ROBO2 gene. As a homolog of the ROBO family, ROBO2 has been reported to exhibit tumor-suppressive functions in various cancers and is associated with patient prognosis. In the course of single-cell analysis, this study identified ROBO2 as highly expressed in the FAP/TGFB1 + CAFs cell subset. Among the various CAF subtypes, the interaction between FAP/TGFB1 + CAFs and macrophages is likely to play a crucial role in the remodeling of the extracellular matrix (ECM), with higher infiltration correlating with poorer patient survival and resistance to immunotherapy [20] . Furthermore, in ROBO2-high expressing FAP/TGFB1 + CAFs, the enrichment of pathways related to cellular programmed death, immune cell recruitment, and ECM regulation was observed. These findings suggest that ROBO2 may suppress the growth of FAP/TGFB1 + CAFs, while simultaneously promoting immune cell recruitment, thus inhibiting the development of a tumor-promoting TME. On the other hand, when ROBO2 expression varies in fibroblasts, significant changes in the ligand-receptor interactions between CAFs and other cell types were observed. In the ROBO2-high expression group, TGF-β and BMP signaling reception was lower compared to the low-expression group. Among the various pathways through which CAFs influence cellular states in TME [21] , TGF-β is frequently considered to play a pivotal role. In pathological conditions, the TGF-β signaling pathway drives tumor growth, aggressive phenotypes, immune evasion, and distant metastasis, including the dissemination of cancer cells [22] . Tumor cells and CAFs can cross-talk through the TGF-β pathway [23] , with TGF-β secreted by CAFs promoting the epithelial-mesenchymal transition (EMT) process in tumor cells [24–26] . This leads to immune dysfunction within the TME, facilitating tumor cell growth [27] and enhancing their metastatic potential [28] . Furthermore, cross-talk in the TGF-β-related pathways within the TME further activates the conversion of adipose-derived mesenchymal stem cells into CAFs [29] , exacerbating the immunosuppressive nature of the TME. The overexpression of ROBO2 may inhibit this process. This conclusion aligns with findings by Andreia V. Pinho et al. in pancreatic cancer, where the loss of ROBO2 signaling in pancreatic cancer cells reprogrammed the microenvironment, leading to significant activation of myCAFs and increased T cell infiltration, with a key role in TGF-β signaling [30] . Moreover, ROBO2-high FAP/TGFB1 + CAFs primarily act as IL-16 signal senders, interacting with macrophages and monocytes. This interaction likely induces the chemotaxis of macrophages and monocytes, promoting the secretion of cytokines and thus exerting a pro-inflammatory effect [31] . This study further demonstrated through luciferase assays that OR7E47P directly binds to miR-183-5p, with a binding site that allows for endogenous competition, leading to the upregulation of ROBO2 expression. Upon OR7E47P overexpression, TGFB1 expres-sion in MRC-5 cells was significantly downregulated, and SMAD2 phosphorylation levels were reduced. This finding is consistent with studies on ROBO2 in pancreatic cancer, where the activation of ROBO2 inhibits TGFB1 expression in pancreatic cancer cells, thus suppressing the activation of the TGF-β signaling pathway [30] . TGF-β as a crucial target for overcoming immune suppression in the TME [32,33] , and TGFB1 is a key activator of the TGF-β signaling pathway. Some researchers consider TGF-β to be a primary checkpoint in cancer, with its negative regulatory effects outweigh-ing those of other immune checkpoints [34] . Overexpression of OR7E47P suppresses the TGF-β1 pathway in MRC-5 cells, significantly downregulating the secretion of TGF-β1 cytokines. Moreover, the malignant phenotype of co-cultured tumor cells is also inhibited. These findings suggest that OR7E47P may serve as a potential therapeutic target in CAFs within the TME, playing a role in alleviating immune suppression and optimizing the ef-ficacy of immune therapies. 5 Conclusion The results of this study suggest that the pseudogene OR7E47P suppresses the ma-lignant phenotype of tumor cells in the NSCLC TME through intercellular communication, providing molecular targets and theoretical support for targeted therapy in NSCLC. However, the complex crosstalk between pathways within the cells indicates that a single gene may be involved in regulating multiple pathways simultaneously. Whether the reg-ulatory effect of OR7E47P on the TGF-β pathway is mediated through ROBO2 still re-quires further experimental validation. Declarations Funding This work was supported by grants from National Natural Science Foundation of China (grant No.82403850, No.82203502 and No.82273094) and Cross-innovation Talent Project at Renmin Hospital of Wuhan University (No. JCRCGW-2022-002). Conflict of interest The authors declare no conflict of interest. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Data availability The data that support the findings of this study are available in the Cancer Genome Atlas (TCGA) database at [https://portal.gdc.cancer.gov/], and the Gene Expres-sion Omnibus (GEO) database at [https://www.ncbi.nlm.nih.gov/geo/] reference number [GSE127465 and GSE146100]. Materials availability Not applicable Code availability Not applicable Author contribution Conceptualization, Jie Wu, Haohan Zhang, Bin Xu, and Qibin Song; methodology, Haohan Zhang and Jing Li; software, Haohan Zhang and Hao Zhong; validation, Xinyi Zhao and Yuchao Dan; investigation, Haohan Zhang; resources, Lan Li and Bin Xu; data curation, Haohan Zhang; writing—original draft preparation, Haohan Zhang; writing—review and editing, Haohan Zhang, Jie Wu and Zhangfan Mao; visualization, Haohan Zhang. All authors confirm that they contributed to manuscript reviews, critical revi-sion for important intellectual content, and read and approved the final draft for submission. All authors are also responsible for the manuscript content. References Tang S, Xue Y, Qin Z, et al.Counteracting lineage-specific transcription factor network finely tunes lung adeno-to-squamous transdifferentiation through remodeling tumor immune microenvironment[J].Natl Sci Rev,2023, 10 (4): nwad028. Borghaei H, Gettinger S, Vokes E E, et al.Five-Year Outcomes From the Randomized, Phase III Trials CheckMate 017 and 057: Nivolumab Versus Docetaxel in Previously Treated Non-Small-Cell Lung Cancer[J].Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology,2021, 39 (7): 723-733. Paz-Ares L, Vicente D, Tafreshi A, et al.A Randomized, Placebo-Controlled Trial of Pembrolizumab Plus Chemotherapy in Patients With Metastatic Squamous NSCLC: Protocol-Specified Final Analysis of KEYNOTE-407[J].Journal of Thoracic Oncology : Official Publication of the International Association For the Study of Lung Cancer,2020, 15 (10): 1657-1669. Singh R K, Singh D, Yadava A, et al.Molecular fossils "pseudogenes" as functional signature in biological system[J].Genes Genomics,2020, 42 (6): 619-630. Yu J, Zhang J, Zhou L, et al.The Octamer-Binding Transcription Factor 4 (OCT4) Pseudogene, POU Domain Class 5 Transcription Factor 1B (POU5F1B), is Upregulated in Cervical Cancer and Down-Regulation Inhibits Cell Proliferation and Migration and Induces Apoptosis in Cervical Cancer Cell Lines[J].Medical Science Monitor : International Medical Journal of Experimental and Clinical Research,2019, 25: 1204-1213. Pan Y, Zhan L, Chen L, et al.POU5F1B promotes hepatocellular carcinoma proliferation by activating AKT[J].Biomedicine & Pharmacotherapy = Biomedecine & Pharmacotherapie,2018, 100: 374-380. Hayashi H, Arao T, Togashi Y, et al.The OCT4 pseudogene POU5F1B is amplified and promotes an aggressive phenotype in gastric cancer[J].Oncogene,2015, 34 (2): 199-208. Huang J-L, Cao S-W, Ou Q-S, et al.The long non-coding RNA PTTG3P promotes cell growth and metastasis via up-regulating PTTG1 and activating PI3K/AKT signaling in hepatocellular carcinoma[J].Molecular Cancer,2018, 17 (1): 93. Glusman G, Bahar A, Sharon D, et al.The olfactory receptor gene superfamily: data mining, classification, and nomenclature[J].Mammalian Genome : Official Journal of the International Mammalian Genome Society,2000, 11 (11): 1016-1023. Vadevoo S M P, Gunassekaran G R, Lee C, et al.The macrophage odorant receptor Olfr78 mediates the lactate-induced M2 phenotype of tumor-associated macrophages[J].Proceedings of the National Academy of Sciences of the United States of America,2021, 118 (37). Martin A L, Anadon C M, Biswas S, et al.Olfactory Receptor OR2H1 Is an Effective Target for CAR T Cells in Human Epithelial Tumors[J].Molecular Cancer Therapeutics,2022, 21 (7): 1184-1194. Shibel R, Sarfstein R, Nagaraj K, et al.The Olfactory Receptor Gene Product, OR5H2, Modulates Endometrial Cancer Cells Proliferation via Interaction with the IGF1 Signaling Pathway[J].Cells,2021, 10 (6). Chen P, Wang W, Liu R, et al.Olfactory sensory experience regulates gliomagenesis via neuronal IGF1[J].Nature,2022, 606 (7914): 550-556. Chen Z, Huang Z, Chen L X.The Olfactory Receptor Pseudo-pseudogene: A Potential Therapeutic Target in Human Diseases[J].Biomedical and Environmental Sciences : BES,2018, 31 (2): 168-170. Efremova M, Vento-Tormo M, Teichmann S A, et al.CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes[J].Nat Protoc,2020, 15 (4): 1484-1506. Yu G, Yao W, Gumireddy K, et al.Pseudogene PTENP1 functions as a competing endogenous RNA to suppress clear-cell renal cell carcinoma progression[J].Mol Cancer Ther,2014, 13 (12): 3086-97. Zheng R, Du M, Wang X, et al.Exosome-transmitted long non-coding RNA PTENP1 suppresses bladder cancer progression[J].Mol Cancer,2018, 17 (1): 143. Bernasconi N L, Wormhoudt T A, Laird-Offringa I A.Post-transcriptional deregulation of myc genes in lung cancer cell lines[J].Am J Respir Cell Mol Biol,2000, 23 (4): 560-5. Gratacós F M, Brewer G.The role of AUF1 in regulated mRNA decay[J].Wiley Interdiscip Rev RNA,2010, 1 (3): 457-73. Qi J, Sun H, Zhang Y, et al.Single-cell and spatial analysis reveal interaction of FAP(+) fibroblasts and SPP1(+) macrophages in colorectal cancer[J].Nat Commun,2022, 13 (1): 1742. Alguacil-Núñez C, Ferrer-Ortiz I, García-Verdú E, et al.Current perspectives on the crosstalk between lung cancer stem cells and cancer-associated fibroblasts[J].Crit Rev Oncol Hematol,2018, 125: 102-110. Massagué J.TGFbeta in Cancer[J].Cell,2008, 134 (2): 215-30. Yoshida G J, Azuma A, Miura Y, et al.Activated Fibroblast Program Orchestrates Tumor Initiation and Progression; Molecular Mechanisms and the Associated Therapeutic Strategies[J].Int J Mol Sci,2019, 20 (9). Minton K.Extracellular matrix: Preconditioning the ECM for fibrosis[J].Nat Rev Mol Cell Biol,2014, 15 (12): 766-7. Su J, Morgani S M, David C J, et al.TGF-β orchestrates fibrogenic and developmental EMTs via the RAS effector RREB1[J].Nature,2020, 577 (7791): 566-571. Chakravarthy A, Khan L, Bensler N P, et al.TGF-β-associated extracellular matrix genes link cancer-associated fibroblasts to immune evasion and immunotherapy failure[J].Nat Commun,2018, 9 (1): 4692. Liu S, Ren J, Ten Dijke P.Targeting TGFβ signal transduction for cancer therapy[J].Signal Transduct Target Ther,2021, 6 (1): 8. Mao X, Xu J, Wang W, et al.Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives[J].Mol Cancer,2021, 20 (1): 131. Cho J A, Park H, Lim E H, et al.Exosomes from ovarian cancer cells induce adipose tissue-derived mesenchymal stem cells to acquire the physical and functional characteristics of tumor-supporting myofibroblasts[J].Gynecol Oncol,2011, 123 (2): 379-86. Pinho A V, Van Bulck M, Chantrill L, et al.ROBO2 is a stroma suppressor gene in the pancreas and acts via TGF-β signalling[J].Nat Commun,2018, 9 (1): 5083. Cruikshank W W, Lim K, Theodore A C, et al.IL-16 inhibition of CD3-dependent lymphocyte activation and proliferation[J].J Immunol,1996, 157 (12): 5240-8. Van Den Bulk J, De Miranda N, Ten Dijke P.Therapeutic targeting of TGF-β in cancer: hacking a master switch of immune suppression[J].Clin Sci (Lond),2021, 135 (1): 35-52. Ungefroren H.Blockade of TGF-β signaling: a potential target for cancer immunotherapy?[J].Expert Opin Ther Targets,2019, 23 (8): 679-693. Larson C, Oronsky B, Carter C A, et al.TGF-beta: a master immune regulator[J].Expert Opin Ther Targets,2020, 24 (5): 427-438. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5809404","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402483737,"identity":"9fac5df6-af47-4edd-8909-f3d7a0d2be7e","order_by":0,"name":"Haohan Zhang","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Haohan","middleName":"","lastName":"Zhang","suffix":""},{"id":402483738,"identity":"fd840e2d-31da-496c-9590-b864714f9d30","order_by":1,"name":"Jie Wu","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wu","suffix":""},{"id":402483739,"identity":"7d8c22ae-dfa7-46dd-965f-cf3ff504dffc","order_by":2,"name":"Lan Li","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Li","suffix":""},{"id":402483740,"identity":"cc1562db-aa00-47f9-b54e-90d7a0607f2d","order_by":3,"name":"Jing Li","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":402483741,"identity":"bfd59361-841e-4d42-87e5-3f5c12fb89e4","order_by":4,"name":"Xinyi Zhao","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Zhao","suffix":""},{"id":402483742,"identity":"a7b10646-264a-4e54-8187-049d2f4011bf","order_by":5,"name":"Hao Zhong","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zhong","suffix":""},{"id":402483743,"identity":"f0bf0371-be32-45b5-9058-217313f6fb1d","order_by":6,"name":"Yuchao Dan","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Yuchao","middleName":"","lastName":"Dan","suffix":""},{"id":402483744,"identity":"3ec7f5ec-76e1-4e32-bd15-0b7dc3b75256","order_by":7,"name":"Zhangfan Mao","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Zhangfan","middleName":"","lastName":"Mao","suffix":""},{"id":402483745,"identity":"28d038d5-1568-46db-87fd-70eec0ebe7be","order_by":8,"name":"Qibin Song","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Qibin","middleName":"","lastName":"Song","suffix":""},{"id":402483746,"identity":"7df9dea3-541f-4cda-9d69-02fda593af9c","order_by":9,"name":"Bin Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFACHoYDEAZzA5CwYTCACBKlhRGkJY04LQxIWg4T1mJwI/fg4QIGmzz+9sbGh1/bzidul0hgfPC2jUHeHIcWyRl5CYdnMKQVS5w52Gws23Y7ceeMBGbDuW0MhjsbsGvhl8gxOMzDcDhxg0Rim7Rk2+3cDTcS2KR52xgSDA5g18KGpuUcSAv7b3xaUGyR/Nh2AGwLMz4tkj3vEoBa0hJngPzCcC65fsOZh82Sc85JGG7AocXgeO7hzzwMNon97c0HH/4oszM2OJ588MObMht5XLaAAeM/CM3MywbmNgAJCTzqkbX++EOcwlEwCkbBKBhZAAA2jF+B/unANQAAAABJRU5ErkJggg==","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-01-11 12:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5809404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5809404/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74050353,"identity":"8173beb9-8612-498b-8ef2-49ccb253ed4b","added_by":"auto","created_at":"2025-01-17 09:50:02","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":340353,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between OR7E47P , clinical characteristics and NSCLC patient prognosis. (a) The expression of OR7E47P in tumor and normal tissues in the TCGA databases. (b) The expres-sion of OR7E47P in tumor and normal tissues in GEPIA databases. (c) The relationship between OR7E47P and the prognosis of NSCLC patients in TCGA database. (d-e) The relationship be-tween OR7E47P and clinical characteristics in NSCLC tissue microarray and the patient cohort from enmin Hospital of Wuhan University. (f) The relationship between OR7E47P and immu-notherapy prognosis in patients from Renmin Hospital of Wuhan University.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5809404/v1/ce1551fc5696a2a6e51dc34b.jpeg"},{"id":74050354,"identity":"e049ab76-2f1e-4de1-a410-5d687d9e8883","added_by":"auto","created_at":"2025-01-17 09:50:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":296738,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Predict OR7E47P expression in the nucleus and cytoplasm of cells in Lnclocator database; (b) Annolnc and RNAhybrid predicts potential target-regulating miRNAs for OR7E47P; (c) Predic-tion of potential target genes of miR-183-5p; (d) Univariate Cox regression analysis of potential target genes of miR-183-5p; (e) Predict binding sites of candidate target genes.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5809404/v1/af520a1b77a108a5c74d25d5.jpeg"},{"id":74050100,"identity":"c476daf7-322a-4cde-a0ce-1fdf6ce84c39","added_by":"auto","created_at":"2025-01-17 09:42:02","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":446568,"visible":true,"origin":"","legend":"\u003cp\u003e(a) In situ hybridization experiment verifies that OR7E47P is mainly stained in the cytoplasm; (b) The expression of OR7E47P and ROBO2 in different cell subpopulations of the TME of NSCLC in GSE127465; (c) The expression of ROBO2 in different cell subpopulations of the TME of NSCLC in GSE146100; (d) The subpopulation of CAFs in TME and the expression of characteristic genes; (e) The enriched pathway of high expression of ROBO2 in FAP/TGFB1+ CAFs subpopulation in TME; (f) The cellular communication situation in TME.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5809404/v1/0363b22eb8b1d877065810eb.jpeg"},{"id":74050102,"identity":"44559277-fe45-4ef7-a590-9ae974ce51d3","added_by":"auto","created_at":"2025-01-17 09:42:02","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":270172,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Fluorescence images of MRC-5 after lentiviral infection; (b) The basic expression of OR7E47P, miR-183-5p, and ROBO2; (c) Western Blot Analysis of the expression of ROBO2 and TGF-β path-way-related protein; (d) OR7E47P-NC and OR7E47P-overexpress group have been tested for via-bility. (e-g) A comparison of scratch assays, colony formation assays, migration assays and inva-sion assays between OR7E47P-NC and OR7E47P-overexpress group was performed. (ns：P\u0026gt;0.05；*：P\u0026lt;0.05; **: P \u0026lt;0.01; ***: P \u0026lt;0.001)\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5809404/v1/57e9adeb6ae4c15cf4b58db7.jpeg"},{"id":74050116,"identity":"c243631f-8162-4632-862b-4afdaa948c96","added_by":"auto","created_at":"2025-01-17 09:42:03","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":284790,"visible":true,"origin":"","legend":"\u003cp\u003e(a) NC mimic and miR183-5p mimic were co-transfected with plasmid miR-OR7E47P-WT lucif-erase vector or miR-OR7E47P-MUT vector into in MRC-5 cells, and the normalized relative lu-ciferase activities (Renilla/firefly) were analyzed; (b) The basic expression of OR7E47P, miR-183-5p, and ROBO2 between OR7E47P-NC, OR7E47P-overexpress and miR-183-5p minics group; (c) Western Blot Analysis of the expression of ROBO2 and TGF-β pathway-related protein; (d-f) A comparison of CCK8, scratch assays, migration assays and invasion assays between OR7E47P-NC, OR7E47P-overexpress and miR-183-5p minics group was performed. (g) NC mim-ic and miR183-5p mimic were co-transfected with plasmid miR-ROBO2-WT luciferase vector or miR- ROBO2-MUT vector into in MRC-5 cells, and the normalized relative luciferase activities (Renilla/firefly) were analyzed; (h) The basic expression of OR7E47P, miR-183-5p, and ROBO2 between OR7E47P-NC, OR7E47P-overexpress and miR-183-5p minics group; (i) Western Blot Analysis of the expression of ROBO2 and TGF-β pathway-related protein; (j-l) A comparison of CCK8, scratch assays, migration assays and invasion assays between OR7E47P-NC, RO-BO2-knockdown and miR-183-5p minics group was performed. (ns：P\u0026gt;0.05；*：P\u0026lt;0.05; **: P \u0026lt;0.01; ***: P \u0026lt;0.001).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5809404/v1/82bf5dac542c31ecc3dd06fe.jpeg"},{"id":74050107,"identity":"e88e409e-b392-4161-9466-4aca9c0df6c5","added_by":"auto","created_at":"2025-01-17 09:42:02","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":130376,"visible":true,"origin":"","legend":"\u003cp\u003e(a) A schematic diagram of MRC-5 cells co-cultured with A549 cells; (b) Differences in TGF-β levels in the upper and lower chambers of the co-culture after incubation; (c) Western Blot Anal-ysis of the expression of ROBO2 and TGF-β pathway-related protein after co-culture. (ns：P\u0026gt;0.05；*：P\u0026lt;0.05; **: P \u0026lt;0.01; ***: P \u0026lt;0.001).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5809404/v1/49901469bef781725fea3a4a.jpeg"},{"id":74955979,"identity":"d3449218-6ead-411f-b9dc-c3eba90d2595","added_by":"auto","created_at":"2025-01-28 17:31:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2583646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5809404/v1/4acbec8c-90e3-41d9-ada4-74ddc6ebb5f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"OR7E47P regulates TGF-β secretion of fibroblast via ROBO2 pathway to modulate the TME in NSCLC","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) being the predominant pathological subtype. In recent years, immunotherapy, particularly therapies targeting PD-1/PD-L1, has demonstrated significantly greater efficacy in prolonging overall survival (OS) and progression-free survival (PFS) in NSCLC patients compared to conventional chemotherapy. With the rapid advancements in sequencing technologies, the molecular characteristics and comprehensive landscape of NSCLC are being increasingly elucidated\u003csup\u003e[1\u0026ndash;3]\u003c/sup\u003e. Various studies were focused on identifying novel therapeutic strategies and targets to enhance the effectiveness of treatment for NSCLC, particularly through targeted and immunotherapeutic approaches.\u003c/p\u003e \u003cp\u003eWith the widespread adoption and advancement of sequencing technologies, pseudogenes have increasingly entered the research spotlight. As non-coding genes with high sequence homology to functional protein-coding genes, pseudogenes serve as crucial regulatory factors influencing gene expression at epigenetic, transcriptional, and post-transcriptional levels. They are involved in various biological processes, including cell differentiation and apoptosis, and their dysregulation is closely associated with the development and progression of multiple diseases, including malignancies\u003csup\u003e[4\u0026ndash;8]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOlfactory receptors (ORs), the largest G protein-coupled receptor superfamily, are predominantly expressed in the cilia of olfactory sensory neurons, where they facilitate the recognition of diverse odors\u003csup\u003e[9]\u003c/sup\u003e. ORs also play critical roles in tumorigenesis, cancer progression, and antitumor therapy\u003csup\u003e[10\u0026ndash;13]\u003c/sup\u003e. Among the OR family, only 40% of OR genes possess intact coding regions, with potential functional roles inferred, while the remaining OR genes are classified as pseudogenes\u003csup\u003e[14]\u003c/sup\u003e. One such pseudogene, olfactory receptor family 7 subfamily E member 47 pseudogene (OR7E47P), is an unprocessed pseudogene widely expressed in lung tissue. However, its role in lung cancer remains unreported.\u003c/p\u003e \u003cp\u003eThis study explores the impact of OR7E47P expression on the prognosis of NSCLC patients, utilizing publicly available databases and immunotherapy data from our institution. Subsequently, the OR7E47P/miR-183-5p/ROBO2 pathway\u0026rsquo;s localization and function within the NSCLC tumor microenvironment were investigated and validated through approaches such as Lnclocator analysis, in situ hybridization on tissue microarrays, and single-cell analysis. Finally, in vitro experiments were conducted to confirm the role of this pathway.\u003c/p\u003e "},{"header":"2 Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 OR7E47P is Closely Associated with Prognosis, Clinical Characteristics, and Treatment Sensitivity in NSCLC Patients\u003c/h2\u003e\n \u003cp\u003eIn this study, we compared the expression of OR7E47P between normal tissues and NSCLC patient cohorts using data from the TCGA and GEO databases. The expression of OR7E47P in normal tissues was significantly higher than in LUAD and LUSC patient samples across both datasets (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eTo investigate the relationship between OR7E47P expression and patient prognosis, we categorized NSCLC patients based on OR7E47P expression levels and plotted Kaplan-Meier (KM) survival curves. Log-rank test and multivariate Cox regression analy-sis revealed that higher OR7E47P expression was significantly associated with better overall survival (OS) in NSCLC patients (P\u0026thinsp;=\u0026thinsp;0.0026). Furthermore, multivariate analysis considering factors such as age, gender, and TNM stage identified low OR7E47P expres-sion as an independent poor prognostic factor for NSCLC (OR7E47P high expression group vs. low expression group, HR\u0026thinsp;=\u0026thinsp;0.668, P\u0026thinsp;=\u0026thinsp;0.0242) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eTo further validate these findings, we collected NSCLC tissue microarrays and paraf-fin-embedded tissue samples from 53 NSCLC patients who underwent immunotherapy at the Cancer Center of Wuhan University People\u0026apos;s Hospital from June 2020 to February 2023. In situ hybridization (ISH) was performed on tissue microarrays, and comparison of paired normal and tumor tissues confirmed that OR7E47P expression was significantly lower in lung tumor tissues than in adjacent normal tissues. Additionally, M stage analy-sis showed that M0 stage patients had significantly higher OR7E47P expression com-pared to M1 stage patients. For TNM staging, patients in stages I/II exhibited higher OR7E47P expression than those in stages III/IV. Similar results were obtained in the pa-tient cohort from Wuhan University People\u0026apos;s Hospital (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\n \u003cp\u003eMoreover, in the cohort of NSCLC patients receiving immunotherapy at Wuhan Uni-versity People\u0026apos;s Hospital, high OR7E47P expression was significantly associated with better OS and PFS compared to low expression groups. The objective response rate (ORR) (68.8% vs. 34.4%) and disease control rate (DCR) (93.8% vs. 65.6%) were also significantly higher in the high OR7E47P expression group (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Subcellular Localization and Pathway Prediction of OR7E47P\u003c/h2\u003e\n \u003cp\u003eIn this study, Lnclocator was utilized to analyze the subcellular localization of OR7E47P transcripts. By integrating the contribution scores of each nucleotide for locali-zation, we predicted that OR7E47P is primarily located in the cytoplasm (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Analysis of the OR7E47P sequence using the NCBI and Genecards databases revealed that OR7E47P exhibits characteristics typical of a retrotransposed pseudogene. Based on this, we hypothesize that OR7E47P functions as a processed pseudogene and plays a role post-transcriptionally through the competing endogenous RNA (ceRNA) mechanism.\u003c/p\u003e\n \u003cp\u003eBuilding on this hypothesis, we performed a comprehensive analysis in the TCGA database, where we selected 438 miRNAs associated with OR7E47P and 622 miRNAs that showed differential expression between high and low OR7E47P expression groups (with 296 miRNAs found in the intersection). Among these, miR-183-5p was identified as the only miRNA that satisfies all the criteria for potential regulation by OR7E47P. Addi-tionally, Annolnc prediction indicated that miR-183-5p and miR-338-3p could be jointly regulated by all three OR7E47P transcript variants. Therefore, miR-183-5p is the most likely miRNA targeted and regulated by OR7E47P. Using RNAhybrid, we predicted the potential binding sites between miR-183-5p and OR7E47P. The results revealed three pos-sible binding sites, suggesting that miR-183-5p and OR7E47P might interact through these sites (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eFurther, based on the ceRNA hypothesis, we screened for target genes potentially regulated by OR7E47P via miR-183-5p. Using the Starbase database and differential ex-pression filtering, we identified several candidate genes including TAC1, FBN2, LIN28B, SCARNA1, NKX2-4, SNORD15B, NKX2-5, CA12, ROBO2, GJA3, and AKAP12 as potential targets of the OR7E47P/miR-183-5p axis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eTo refine these candidates, we used univariate Cox regression analysis in the TCGA and KM-plotter databases to calculate HR and P-values. Genes that showed consistent trends (HR\u0026thinsp;\u0026gt;\u0026thinsp;1 or HR\u0026thinsp;\u0026lt;\u0026thinsp;1) and P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected as candidate target genes, with ROBO2, FBN2, and NKX2-5 being identified as the final candidates (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Subse-quently, using methods such as TargetScanSites, we predicted potential binding sites for the candidate genes. Among the five methods, three predicted a binding site between ROBO2 and miR-183-5p, suggesting a higher likelihood of interaction compared to the other genes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). Therefore, we conclude that ROBO2 is the most likely target gene regulated by miR-183-5p.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Subcellular Localization and Pathway Prediction of OR7E47P\u003c/h2\u003e\n \u003cp\u003eIn this study, in situ hybridization experiments on paraffin-embedded tissue slices from clinical patients revealed that OR7E47P is expressed not only in tumor cells but also in stromal cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). To further investigate the expression and regulatory function of OR7E47P and ROBO2 in the TME, we analyzed the GSE127465 single-cell RNA-seq dataset. The TME cells from NSCLC were annotated into seven subgroups: B cells, epithe-lial cells, fibroblasts, monocytes, neutrophils, NK cells, and T cells. OR7E47P was pre-dominantly expressed in epithelial cells and fibroblasts, while ROBO2 expression was primarily seen in fibroblasts (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). This suggests that the OR7E47P/miR-183-5p/ROBO2 axis likely exerts its main regulatory effects within fibro-blasts.\u003c/p\u003e\n \u003cp\u003eTo explore the potential regulatory role of this pathway in immune therapy, we fur-ther analyzed the GSE146100 immune therapy single-cell dataset. In this dataset, ROBO2 was found to be primarily expressed in fibroblasts, with some expression in epithelial cells. By comparing immune therapy responders (R) and non-responders (NR), we ob-served that ROBO2 expression was significantly higher in the fibroblast subpopulation of immune therapy responders (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eGiven that ROBO2 is mainly expressed in TME fibroblasts, we extracted all fibro-blasts from the dataset and performed dimensional reduction. We classified the fibro-blasts into four clusters and annotated them based on cell markers to assess the status and function of cancer-associated fibroblasts (CAFs) in the TME. Cluster C0 was characterized by high expression of FAP (Fibroblast Activation Protein Alpha) and TGFB1 (Transform-ing Growth Factor Beta 1), marking the presence of activated CAFs. Cluster C1 specifically expressed CFD (Complement Factor D), Cluster C2 exhibited expression of the fibroblast marker VIM (Vimentin), and Cluster C3 showed high expression of MCAM (Melanoma Cell Adhesion Molecule). Notably, ROBO2 was specifically highly expressed in FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eTo accurately annotate the function of ROBO2 in FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs, we categorized these cells into high and low ROBO2 expression groups and performed differential gene expression analysis. The results revealed that in the high ROBO2 expression group, there was significant enrichment of pathways involved in immune response to tumor cells, programmed cell death involved in cell development, and T-cell receptor complex (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\n \u003cp\u003eTo further investigate how FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs influence the other cell populations in the TME, we assessed the differences in cell-cell communication when ROBO2 expression varied. The results were visualized to highlight the interactions (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF). In ROBO2 high expression CAFs, the TME fibroblasts were more susceptible to regulation by mac-rophage TNF signaling, which suppressed CAF growth. In contrast, ROBO2 low expres-sion CAFs were more prone to activation by TGF-\u0026beta; and Bone Morphogenetic Protein (BMP) signaling pathways. TGF-\u0026beta; 1/2/3 and BMP signaling are key sources of SMAD transcrip-tion factor activation, which mediates downstream effects.\u003c/p\u003e\n \u003cp\u003eThese findings suggest that ROBO2 in the TME, particularly within FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs, plays a crucial role in modulating tumor cell malignancy and immune responses, potentially impacting tumor progression and therapy responses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 OR7E47P upregulation significantly inhibits the proliferation and clonal formation ability of MRC-5 cells\u003c/h2\u003e\n \u003cp\u003eBuilding on the previous results, we first established an OR7E47P overexpression stable MRC-5 cell line using lentivirus (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The results showed that in the overexpression group, both OR7E47P and ROBO2 expression levels were significantly upregulated, while miR-183-5p ex-pression was significantly downregulated (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eTo further confirm the impact of OR7E47P overexpression on ROBO2 and related pathways, we performed Western blotting (WB) to detect the expression of ROBO2, TGF-\u0026beta; (TGFB1), and SMAD2 phosphorylation in fibroblasts after overexpression of OR7E47P. Compared with the control group, the ROBO2 protein expression was significantly upregulated, whereas the TGFB1 protein and the phosphorylation level of SMAD2 were significantl Furthermore, through the detection of cell proliferation activity by CCK-8 assay and colony formation assay on plate, we found that the proliferation activity and clonal formation ability of MRC-5 cells were significantly inhibited when OR7E47P was upregulated (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). The Transwell assay showed that the upregulation of OR7E47P in MRC-5 cells significantly in-hibited the cell migration and invasion ability (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e\n \u003cp\u003eThese findings suggest that OR7E47P can regulate the expression of miR-183-5p and ROBO2 in MRC-5 cells and inhibit the TGF-\u0026beta;/SMAD signaling pathway. This supports the hypothesis that OR7E47P affects ROBO2 expression through miR-183-5p regulation in fibroblasts, impacting key signaling pathways in TME.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Validation of OR7E47P Regulating ROBO2 Expression through miR-183-5p in vitro\u003c/h2\u003e\n \u003cp\u003eTo verify that OR7E47P can target the expression of miR-183-5p, the study con-structed both wild-type (WT) and mutant (Mut) OR7E47P plasmids, integrating them into the 3\u0026apos; UTR of R-Luc. These constructs were co-transfected with miR-183-5p mimics into MRC-5 cells for luciferase activity assays. Compared with the miR-NC and mutant OR7E47P groups, the mutant OR7E47P group co-transfected with miR-183-5p mimics showed no significant change in luciferase activity, indicating that the mutant form of OR7E47P cannot interact with miR-183-5p. However, when wild-type OR7E47P was co-transfected with miR-183-5p, luciferase activity was significantly reduced compared to the miR-NC and wild-type OR7E47P groups, confirming that miR-183-5p directly targets and binds to OR7E47P (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eTo investigate the regulatory effects of miR-183-5p and ROBO2 on each other, the study performed a recovery experiment by reintroducing miR-183-5p mimics into OR7E47P-overexpressing stable MRC-5 cells. RT-qPCR analysis of ROBO2 and TGFB1 protein expression revealed that in the recovery group, ROBO2 expression was signifi-cantly reduced, while TGFB1 expression was significantly increased compared to the overexpression group (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). Subsequently, this study further compared the changes in cell phenotypes among the groups using CCK8 cell proliferation, scratch, and Transwell assays. In the recovery experiment, the cell phenotypes were all restored (Fig-ures 5D, 5E and 5F).\u003c/p\u003e\n \u003cp\u003eIn the same way as the above experiment, co-transfection of wild-type ROBO2 with miR-183-5p significantly reduced luciferase activity, indicating that miR-183-5p directly regulates the expression of ROBO2 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG). Additionally, siRNA-mediated knock-down of ROBO2 was performed in the OR7E47P stable MRC-5 cell line, and the expres-sion levels of OR7E47P and miR-183-5p were analyzed by RT-qPCR. The results showed that the regulation of ROBO2 expression had no significant impact on OR7E47P and miR-183-5p expression. Furthermore, in the recovery group, ROBO2 expression was sig-nificantly reduced, and TGFB1 protein expression was significantly elevated compared to the overexpression group (Figures H and 5I). The phenotypic experiment also yielded similar results (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eJ, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eK and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eL).\u003c/p\u003e\n \u003cp\u003eThese results demonstrate that miR-183-5p can directly regulate the expression of ROBO2, and OR7E47P modulates the miR-183-5p/ROBO2 axis to influence the TGF-\u0026beta; signaling pathway.\u003c/p\u003e\n \u003ch2\u003e2.6 Effect of Differential Expression of OR7E47P in MRC-5 Cells on TGF-\u0026beta;/Smad Signaling Pathway in A549 Cells\u003c/h2\u003e\n \u003cp\u003eTo assess whether the differential expression of OR7E47P in MRC-5 cells influences A549 cells, a co-culture experiment was conducted (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). ELISA was first used to measure the levels of TGF-\u0026beta; in both the upper and lower chambers of the co-culture sys-tem. The results showed that upon OR7E47P overexpression, the secretion of TGF-\u0026beta; from MRC-5 cells was significantly reduced (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eSubsequently, proteins from A549 cells co-cultured with MRC-5 cells were extracted and analyzed by Western blotting (WB). The findings revealed that TGF-\u0026beta;/Smad signaling pathway proteins, specifically TGFB1 and phosphorylated SMAD2, showed significantly reduced expression in A549 cells co-cultured with OR7E47P-overexpressing MRC-5 cells (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). These results suggest that OR7E47P in MRC-5 cells can modulate the TGF-\u0026beta;/Smad signaling pathway in A549 cells, potentially influencing tumor progression.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Methods","content":"\u003cp\u003e \u003cb\u003eData Sources\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study utilized multiple publicly available and institutional datasets. RNA se-quencing data (FPKM normalized) for NSCLC patients were downloaded from The Can-cer Genome Atlas (TCGA) via the Genomic Data Commons Data Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Corresponding clinical information for the TCGA cohort was retrieved from the UCSC Xena database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Single-cell RNA se-quencing datasets, GSE127465 and GSE146100, were obtained from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). NSCLC tissue microar-rays (product number: ZL-LugA961) were procured from Shanghai Outdo Biotech Co., Ltd. Paraffin-embedded tumor tissue sections and corresponding clinical information were collected from 53 NSCLC patients who received immunotherapy at the Cancer Center of Renmin Hospital of Wuhan University between June 2020 and February 2023. These samples were used for in situ hybridization (ISH) analysis and subsequent investigations of the pseudogene OR7E47P.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCell Lines\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA549 (Human NSCLC Cell Line) was cultured in complete medium comprising 89% Ham's F-12K (Kaighn's) Medium, 10% fetal bovine serum (FBS), and 1% penicil-lin-streptomycin. MRC-5 (Human Fibroblast Cell Line) was cultured in complete medium comprising Minimum Essential Medium (MEM), 10% FBS, and 1% penicil-lin-streptomycin. The cells were maintained at 37\u0026deg;C in a 5% CO2 incubator.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-Cell Analysis Methods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe single-cell analysis workflow employed in this study involved the following steps. Single-cell RNA sequencing data were processed using the Seurat package in R. Key steps included data loading, normalization, scaling, principal component analysis (PCA), and clustering of subpopulations. The Uniform Manifold Approximation and Projection (UMAP) algorithm was applied for visualization.\u003c/p\u003e \u003cp\u003eDifferentially expressed markers for identified clusters were determined using the FindAllMarkers function with the default non-parametric Wilcoxon rank-sum test. Pre-liminary annotation of genes was conducted using the SingleR package, and the expres-sion of the target gene in different cellular subpopulations was analyzed.\u003c/p\u003e \u003cp\u003eGene Ontology (GO) gene sets (including Molecular Function, Cellular Component, and Biological Process) from the MSigDB database were utilized for GSEA.Differentially expressed genes between groups were filtered by setting a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and used to calculate enrichment scores (Enrichment Score, ES). Pathways with an absolute value of normalized enrichment score (NES)\u0026thinsp;\u0026gt;\u0026thinsp;1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly en-riched and were analyzed in the context of ROBO2 differential expression groups.\u003c/p\u003e \u003cp\u003eThe CellphonedDB package\u003csup\u003e[15]\u003c/sup\u003e was employed to analyze interactions among sub-populations within the NSCLC tumor microenvironment (TME). Raw count matrices and cell type annotation files, extracted from Seurat objects, were used as inputs. Interaction frequencies between pairs of subpopulations were calculated, and the intensity of poten-tial ligand-receptor interactions was predicted based on mean expression levels.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIsolation of Total RNA and Reverse Transcription Quantitative Real-Time PCR (RT\u0026ndash;qPCR)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTriquick Reagent (Solarbio, Beijing) was used to isolate total RNA from the cells. cDNA was used for quantitative real-time polymerase chain reaction (qRT-PCR) with forward and reverse primers and iTaq Universal SYBR Green PCR Master Mix (Bio-Rad, USA). The data were analyzed using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method. The primers used in this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003ePrimers used in quantitative real-time PCR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward primer(5\u0026prime;-3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse primer (5\u0026prime;-3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR7E47P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACAACTTGATTGCCTTACTAATGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCATAAAATCAGCAAACAACTGACAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGCCAGCCGAGCCACATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGTTCTCAGCCTTGACGGTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTCGCTTCGGCAGCACAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAACGCTTCACGAATTTGCGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROBO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCAAAGTCCCGACTGAACGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGTGATCTGGAGGAGGAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-183-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGCCTATGGCACTGGTAGAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTCAACTGGTGTCGTGGAGTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eWestern Blotting Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTotal protein was isolated from cells using RIPA buffer (high) obtained from Solarbio (Beijing), and its concentration was determined with a bicinchoninic acid kit (Applygen, Beijing) following the manufacturer's guidelines. 20 \u0026micro;g protein was separated using 10% SDS polyacrylamide gel electrophoresis, transferred to polyvinylidene difluoride mem-branes (pore size: 0.45 \u0026micro;m, Merck Millipore, Ireland), blocked with 5% skimmed milk, and incubated with primary antibodies overnight at 4\u0026deg;C. The membranes were washed four times using 0.1% TBST, incubated with secondary antibodies, and were visualized using an ultrahigh-sensitive ECL kit (Biosharp, Beijing). Antibodies used were as follows: an-ti-ROBO2 (121635-1-AP, Proteintech), anti-TGFB1 (26155-1-AP, Proteintech), anti-SMAD2 (#5339, Cell Signalling), anti-Phospho-SMAD2 (#18338, Cell Signalling), anti-GAPDH (10494-1-AP, Proteintech). Image Lab software (Version 5.2; Bio-Rad Laboratories, USA) was employed to quantify the signal intensity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDual-Luciferase Reporter Gene Assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFollow the instructions of the Dual Luciferase Reporter Gene Assay Kit (RG008, Be-yotime) and LimitlessTM ELZ Fushion kit (LEF01, ELK biotechnology). Target sequences for OR7E47P and ROBO2 were amplified and ligated into the pGL6 vector with overlapping sequences. After enzymatic digestion, the ligation products were recombined with the vector. The recombinant plasmids were transformed into high-efficiency DH5α competent cells (EC001, ELK biotechnology). Single colonies were selected after one day of incubation and subjected to sequencing to confirm successful vector construction. Cells were pre-pared and transfected with the reporter gene complexes. Following transfection, the cells were cultured for an additional 24 hours. A dual-luciferase reporter assay was performed to measure the luminescence signals.\u003c/p\u003e \u003cp\u003e \u003cb\u003eELISA Assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFollow the instructions of the Human TGFb1 ELISA Kit (ELK1185, ELK Biotechnolo-gy). Add standards and samples to the wells of a microtiter plate. Add biotin-conjugated TGF-β1-specific antibodies to each well. Add horseradish peroxidase (HRP)-conjugated streptavidin and incubate. Add substrate solution to initiate the colorimetric reaction. Ob-serve color changes. Add sulfuric acid to stop the enzyme-substrate reaction. Measure the optical density (OD) at 450 nm using a spectrophotometer. Generate a standard curve based on the OD values of the standards and calculate the TGF-β1 concentration in the samples.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Methods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe optimal cutoff points for all continuous variables were determined using the \"survminer\" package in R. Spearman's correlation method was employed to calculate correlation coefficients and p-values for correlation analyses. Statistical differences in dis-tributions were assessed as follows: Kruskal-Wallis test for comparisons among three or more groups. Wilcoxon test for comparisons between two groups. Statistical analyses for experimental data were performed using GraphPad Prism 8.0. Quantitative data were ex-pressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s). Student\u0026rsquo;s t-test for pairwise comparisons between groups. One-Way ANOVA for analyzing differences among multiple treatment groups. Results were considered statistically significant if P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBased on the TCGA and GEPIA databases, as well as tissue microarrays and clinical samples from NSCLC patients at our institution, this study confirmed that OR7E47P ex-pression is significantly lower in lung tumor tissues compared to normal lung tissues. Furthermore, the expression of OR7E47P is closely associated with patient prognosis and clinical features.\u003c/p\u003e \u003cp\u003ePseudogenes can be classified into unitary pseudogenes, unprocessed pseudogenes, and processed pseudogenes. Among these, processed pseudog`enes, also known as retroposed pseudogenes, are generated through the reverse transcription of mRNA into cDNA, which is then integrated into the genome\u003csup\u003e[16,17]\u003c/sup\u003e. This process results in the creation of a pseudogene that is located at a position in the genome different from that of the parental gene, and often on a different chromosomeh analysis of the OR7E47P sequence via NCBI and Genecards databases, it was found that OR7E47P is located at 12q13.13, while its parental gene, OR7E24, is located at 19p13.2. Notably, OR7E47P contains repetitive sequences at both the 5' and 3' ends and retains a polyA tail. These features align with the characteristics of a retroposed pseudogene, which is generated through the reverse transcription process, and the presence of a polyA tail at the 3' end suggests that this pseudogene likely retains miRNA binding sites from the ancestral gene. As a result, this type of gene tends to have a higher likelihood of interacting with miRNAs, especially due to the preserved 3' end structure and the ability to act as a ceRNA (competing endogenous RNA), potentially sequestering miRNAs and affecting the expression of target genes.\u003c/p\u003e \u003cp\u003eAdditionally, OR7E47P, being a processed pseudogene, may regulate gene expression through the miRNA pathway, and functioning as a ceRNA to modulate the expression of target genes such as ROBO2. This study hypothesizes OR7E47P, through its interaction with miR-183-5p, may regulate the expression of ROBO2, and further highlighting the potential role of OR7E47P in modulating critical pathways like TGF-β/Smad signaling and influencing tumor progression\u003csup\u003e[18,19]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on single-cell analysis, this study further investigates the functional role of the ROBO2 gene. As a homolog of the ROBO family, ROBO2 has been reported to exhibit tumor-suppressive functions in various cancers and is associated with patient prognosis. In the course of single-cell analysis, this study identified ROBO2 as highly expressed in the FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs cell subset. Among the various CAF subtypes, the interaction between FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs and macrophages is likely to play a crucial role in the remodeling of the extracellular matrix (ECM), with higher infiltration correlating with poorer patient survival and resistance to immunotherapy\u003csup\u003e[20]\u003c/sup\u003e. Furthermore, in ROBO2-high expressing FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs, the enrichment of pathways related to cellular programmed death, immune cell recruitment, and ECM regulation was observed. These findings suggest that ROBO2 may suppress the growth of FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs, while simultaneously promoting immune cell recruitment, thus inhibiting the development of a tumor-promoting TME.\u003c/p\u003e \u003cp\u003eOn the other hand, when ROBO2 expression varies in fibroblasts, significant changes in the ligand-receptor interactions between CAFs and other cell types were observed. In the ROBO2-high expression group, TGF-β and BMP signaling reception was lower compared to the low-expression group. Among the various pathways through which CAFs influence cellular states in TME \u003csup\u003e[21]\u003c/sup\u003e, TGF-β is frequently considered to play a pivotal role. In pathological conditions, the TGF-β signaling pathway drives tumor growth, aggressive phenotypes, immune evasion, and distant metastasis, including the dissemination of cancer cells\u003csup\u003e[22]\u003c/sup\u003e. Tumor cells and CAFs can cross-talk through the TGF-β pathway \u003csup\u003e[23]\u003c/sup\u003e, with TGF-β secreted by CAFs promoting the epithelial-mesenchymal transition (EMT) process in tumor cells \u003csup\u003e[24\u0026ndash;26]\u003c/sup\u003e. This leads to immune dysfunction within the TME, facilitating tumor cell growth \u003csup\u003e[27]\u003c/sup\u003e and enhancing their metastatic potential \u003csup\u003e[28]\u003c/sup\u003e. Furthermore, cross-talk in the TGF-β-related pathways within the TME further activates the conversion of adipose-derived mesenchymal stem cells into CAFs\u003csup\u003e[29]\u003c/sup\u003e, exacerbating the immunosuppressive nature of the TME. The overexpression of ROBO2 may inhibit this process. This conclusion aligns with findings by Andreia V. Pinho et al. in pancreatic cancer, where the loss of ROBO2 signaling in pancreatic cancer cells reprogrammed the microenvironment, leading to significant activation of myCAFs and increased T cell infiltration, with a key role in TGF-β signaling\u003csup\u003e[30]\u003c/sup\u003e. Moreover, ROBO2-high FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs primarily act as IL-16 signal senders, interacting with macrophages and monocytes. This interaction likely induces the chemotaxis of macrophages and monocytes, promoting the secretion of cytokines and thus exerting a pro-inflammatory effect\u003csup\u003e[31]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study further demonstrated through luciferase assays that OR7E47P directly binds to miR-183-5p, with a binding site that allows for endogenous competition, leading to the upregulation of ROBO2 expression. Upon OR7E47P overexpression, TGFB1 expres-sion in MRC-5 cells was significantly downregulated, and SMAD2 phosphorylation levels were reduced. This finding is consistent with studies on ROBO2 in pancreatic cancer, where the activation of ROBO2 inhibits TGFB1 expression in pancreatic cancer cells, thus suppressing the activation of the TGF-β signaling pathway\u003csup\u003e[30]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTGF-β as a crucial target for overcoming immune suppression in the TME\u003csup\u003e[32,33]\u003c/sup\u003e, and TGFB1 is a key activator of the TGF-β signaling pathway. Some researchers consider TGF-β to be a primary checkpoint in cancer, with its negative regulatory effects outweigh-ing those of other immune checkpoints\u003csup\u003e[34]\u003c/sup\u003e. Overexpression of OR7E47P suppresses the TGF-β1 pathway in MRC-5 cells, significantly downregulating the secretion of TGF-β1 cytokines. Moreover, the malignant phenotype of co-cultured tumor cells is also inhibited. These findings suggest that OR7E47P may serve as a potential therapeutic target in CAFs within the TME, playing a role in alleviating immune suppression and optimizing the ef-ficacy of immune therapies.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe results of this study suggest that the pseudogene OR7E47P suppresses the ma-lignant phenotype of tumor cells in the NSCLC TME through intercellular communication, providing molecular targets and theoretical support for targeted therapy in NSCLC. However, the complex crosstalk between pathways within the cells indicates that a single gene may be involved in regulating multiple pathways simultaneously. Whether the reg-ulatory effect of OR7E47P on the TGF-β pathway is mediated through ROBO2 still re-quires further experimental validation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from National Natural Science Foundation of China (grant No.82403850, No.82203502 and No.82273094) and Cross-innovation Talent Project at Renmin Hospital of Wuhan University (No. JCRCGW-2022-002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available in the Cancer Genome Atlas (TCGA) database at [https://portal.gdc.cancer.gov/], and the Gene Expres-sion Omnibus (GEO) database at [https://www.ncbi.nlm.nih.gov/geo/] reference number [GSE127465 and GSE146100].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Jie Wu, Haohan Zhang, Bin Xu, and Qibin Song; methodology, Haohan Zhang and Jing Li; software, Haohan Zhang and Hao Zhong; validation, Xinyi Zhao and Yuchao Dan; investigation, Haohan Zhang; resources, Lan Li and Bin Xu; data curation, Haohan Zhang; writing\u0026mdash;original draft preparation, Haohan Zhang; writing\u0026mdash;review and editing, Haohan Zhang, Jie Wu and Zhangfan Mao; visualization, Haohan Zhang. All authors confirm that they contributed to manuscript reviews, critical revi-sion for important intellectual content, and read and approved the final draft for submission. All authors are also responsible for the manuscript content.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTang S, Xue Y, Qin Z, et al.Counteracting lineage-specific transcription factor network finely tunes lung adeno-to-squamous transdifferentiation through remodeling tumor immune microenvironment[J].Natl Sci Rev,2023, 10 (4): nwad028.\u003c/li\u003e\n\u003cli\u003eBorghaei H, Gettinger S, Vokes E E, et al.Five-Year Outcomes From the Randomized, Phase III Trials CheckMate 017 and 057: Nivolumab Versus Docetaxel in Previously Treated Non-Small-Cell Lung Cancer[J].Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology,2021, 39 (7): 723-733.\u003c/li\u003e\n\u003cli\u003ePaz-Ares L, Vicente D, Tafreshi A, et al.A Randomized, Placebo-Controlled Trial of Pembrolizumab Plus Chemotherapy in Patients With Metastatic Squamous NSCLC: Protocol-Specified Final Analysis of KEYNOTE-407[J].Journal of Thoracic Oncology : Official Publication of the International Association For the Study of Lung Cancer,2020, 15 (10): 1657-1669.\u003c/li\u003e\n\u003cli\u003eSingh R K, Singh D, Yadava A, et al.Molecular fossils \u0026quot;pseudogenes\u0026quot; as functional signature in biological system[J].Genes Genomics,2020, 42 (6): 619-630.\u003c/li\u003e\n\u003cli\u003eYu J, Zhang J, Zhou L, et al.The Octamer-Binding Transcription Factor 4 (OCT4) Pseudogene, POU Domain Class 5 Transcription Factor 1B (POU5F1B), is Upregulated in Cervical Cancer and Down-Regulation Inhibits Cell Proliferation and Migration and Induces Apoptosis in Cervical Cancer Cell Lines[J].Medical Science Monitor : International Medical Journal of Experimental and Clinical Research,2019, 25: 1204-1213.\u003c/li\u003e\n\u003cli\u003ePan Y, Zhan L, Chen L, et al.POU5F1B promotes hepatocellular carcinoma proliferation by activating AKT[J].Biomedicine \u0026amp; Pharmacotherapy = Biomedecine \u0026amp; Pharmacotherapie,2018, 100: 374-380.\u003c/li\u003e\n\u003cli\u003eHayashi H, Arao T, Togashi Y, et al.The OCT4 pseudogene POU5F1B is amplified and promotes an aggressive phenotype in gastric cancer[J].Oncogene,2015, 34 (2): 199-208.\u003c/li\u003e\n\u003cli\u003eHuang J-L, Cao S-W, Ou Q-S, et al.The long non-coding RNA PTTG3P promotes cell growth and metastasis via up-regulating PTTG1 and activating PI3K/AKT signaling in hepatocellular carcinoma[J].Molecular Cancer,2018, 17 (1): 93.\u003c/li\u003e\n\u003cli\u003eGlusman G, Bahar A, Sharon D, et al.The olfactory receptor gene superfamily: data mining, classification, and nomenclature[J].Mammalian Genome : Official Journal of the International Mammalian Genome Society,2000, 11 (11): 1016-1023.\u003c/li\u003e\n\u003cli\u003eVadevoo S M P, Gunassekaran G R, Lee C, et al.The macrophage odorant receptor Olfr78 mediates the lactate-induced M2 phenotype of tumor-associated macrophages[J].Proceedings of the National Academy of Sciences of the United States of America,2021, 118 (37).\u003c/li\u003e\n\u003cli\u003eMartin A L, Anadon C M, Biswas S, et al.Olfactory Receptor OR2H1 Is an Effective Target for CAR T Cells in Human Epithelial Tumors[J].Molecular Cancer Therapeutics,2022, 21 (7): 1184-1194.\u003c/li\u003e\n\u003cli\u003eShibel R, Sarfstein R, Nagaraj K, et al.The Olfactory Receptor Gene Product, OR5H2, Modulates Endometrial Cancer Cells Proliferation via Interaction with the IGF1 Signaling Pathway[J].Cells,2021, 10 (6).\u003c/li\u003e\n\u003cli\u003eChen P, Wang W, Liu R, et al.Olfactory sensory experience regulates gliomagenesis via neuronal IGF1[J].Nature,2022, 606 (7914): 550-556.\u003c/li\u003e\n\u003cli\u003eChen Z, Huang Z, Chen L X.The Olfactory Receptor Pseudo-pseudogene: A Potential Therapeutic Target in Human Diseases[J].Biomedical and Environmental Sciences : BES,2018, 31 (2): 168-170.\u003c/li\u003e\n\u003cli\u003eEfremova M, Vento-Tormo M, Teichmann S A, et al.CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes[J].Nat Protoc,2020, 15 (4): 1484-1506.\u003c/li\u003e\n\u003cli\u003eYu G, Yao W, Gumireddy K, et al.Pseudogene PTENP1 functions as a competing endogenous RNA to suppress clear-cell renal cell carcinoma progression[J].Mol Cancer Ther,2014, 13 (12): 3086-97.\u003c/li\u003e\n\u003cli\u003eZheng R, Du M, Wang X, et al.Exosome-transmitted long non-coding RNA PTENP1 suppresses bladder cancer progression[J].Mol Cancer,2018, 17 (1): 143.\u003c/li\u003e\n\u003cli\u003eBernasconi N L, Wormhoudt T A, Laird-Offringa I A.Post-transcriptional deregulation of myc genes in lung cancer cell lines[J].Am J Respir Cell Mol Biol,2000, 23 (4): 560-5.\u003c/li\u003e\n\u003cli\u003eGratac\u0026oacute;s F M, Brewer G.The role of AUF1 in regulated mRNA decay[J].Wiley Interdiscip Rev RNA,2010, 1 (3): 457-73.\u003c/li\u003e\n\u003cli\u003eQi J, Sun H, Zhang Y, et al.Single-cell and spatial analysis reveal interaction of FAP(+) fibroblasts and SPP1(+) macrophages in colorectal cancer[J].Nat Commun,2022, 13 (1): 1742.\u003c/li\u003e\n\u003cli\u003eAlguacil-N\u0026uacute;\u0026ntilde;ez C, Ferrer-Ortiz I, Garc\u0026iacute;a-Verd\u0026uacute; E, et al.Current perspectives on the crosstalk between lung cancer stem cells and cancer-associated fibroblasts[J].Crit Rev Oncol Hematol,2018, 125: 102-110.\u003c/li\u003e\n\u003cli\u003eMassagu\u0026eacute; J.TGFbeta in Cancer[J].Cell,2008, 134 (2): 215-30.\u003c/li\u003e\n\u003cli\u003eYoshida G J, Azuma A, Miura Y, et al.Activated Fibroblast Program Orchestrates Tumor Initiation and Progression; Molecular Mechanisms and the Associated Therapeutic Strategies[J].Int J Mol Sci,2019, 20 (9).\u003c/li\u003e\n\u003cli\u003eMinton K.Extracellular matrix: Preconditioning the ECM for fibrosis[J].Nat Rev Mol Cell Biol,2014, 15 (12): 766-7.\u003c/li\u003e\n\u003cli\u003eSu J, Morgani S M, David C J, et al.TGF-\u0026beta; orchestrates fibrogenic and developmental EMTs via the RAS effector RREB1[J].Nature,2020, 577 (7791): 566-571.\u003c/li\u003e\n\u003cli\u003eChakravarthy A, Khan L, Bensler N P, et al.TGF-\u0026beta;-associated extracellular matrix genes link cancer-associated fibroblasts to immune evasion and immunotherapy failure[J].Nat Commun,2018, 9 (1): 4692.\u003c/li\u003e\n\u003cli\u003eLiu S, Ren J, Ten Dijke P.Targeting TGF\u0026beta; signal transduction for cancer therapy[J].Signal Transduct Target Ther,2021, 6 (1): 8.\u003c/li\u003e\n\u003cli\u003eMao X, Xu J, Wang W, et al.Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives[J].Mol Cancer,2021, 20 (1): 131.\u003c/li\u003e\n\u003cli\u003eCho J A, Park H, Lim E H, et al.Exosomes from ovarian cancer cells induce adipose tissue-derived mesenchymal stem cells to acquire the physical and functional characteristics of tumor-supporting myofibroblasts[J].Gynecol Oncol,2011, 123 (2): 379-86.\u003c/li\u003e\n\u003cli\u003ePinho A V, Van Bulck M, Chantrill L, et al.ROBO2 is a stroma suppressor gene in the pancreas and acts via TGF-\u0026beta; signalling[J].Nat Commun,2018, 9 (1): 5083.\u003c/li\u003e\n\u003cli\u003eCruikshank W W, Lim K, Theodore A C, et al.IL-16 inhibition of CD3-dependent lymphocyte activation and proliferation[J].J Immunol,1996, 157 (12): 5240-8.\u003c/li\u003e\n\u003cli\u003eVan Den Bulk J, De Miranda N, Ten Dijke P.Therapeutic targeting of TGF-\u0026beta; in cancer: hacking a master switch of immune suppression[J].Clin Sci (Lond),2021, 135 (1): 35-52.\u003c/li\u003e\n\u003cli\u003eUngefroren H.Blockade of TGF-\u0026beta; signaling: a potential target for cancer immunotherapy?[J].Expert Opin Ther Targets,2019, 23 (8): 679-693.\u003c/li\u003e\n\u003cli\u003eLarson C, Oronsky B, Carter C A, et al.TGF-beta: a master immune regulator[J].Expert Opin Ther Targets,2020, 24 (5): 427-438.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"OR7E47P, NSCLC, miR-183-5p, Immunotherapy, ROBO2","lastPublishedDoi":"10.21203/rs.3.rs-5809404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5809404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhereas the role of OR7E47P in NSCLC is not clear, we explored the impact of OR7E47P on the prognosis of NSCLC patients and possible mechanisms through bioinformatics and experi-mental approaches. OR7E47P, underexpressed in NSCLC tumor tissues, is associated with better prognosis and enhanced immune benefits. Localization analyses showed OR7E47P may func-tions as a cytoplasmic processing pseudogene, regulating ROBO2 expression via the OR7E47P/miR-183-5p/ROBO2 pathway at the post-transcriptional level. Single-cell analyses re-vealed that ROBO2 reduces the tumor-supportive role of cancer-associated fibroblasts (CAFs) by promoting programmed cell death of FAP/TGFB1\u0026thinsp;+\u0026thinsp;CAFs, enhancing immune infiltration, and suppressing the TGF-β/Smad signaling pathway in fibroblasts and NSCLC cells. Finally, we con-firmed our findings through experiments, where OR7E47P directly binds to miR-183-5p, with a binding site that allows for endogenous competition, leading to the upregulation of ROBO2 ex-pression and potentially regulate TGF-β secretion of fibroblast, thereby regulating the TME in NSCLC.\u003c/p\u003e","manuscriptTitle":"OR7E47P regulates TGF-β secretion of fibroblast via ROBO2 pathway to modulate the TME in NSCLC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 09:41:58","doi":"10.21203/rs.3.rs-5809404/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0933b96-2512-4530-b677-367dbc366dd9","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-28T17:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-17 09:41:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5809404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5809404","identity":"rs-5809404","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}