Study of LY9 as a potential biomarker for prognosis and prediction of immunotherapy efficacy in lung adenocarcinoma.

Background: Lymphocyte antigen 9 (LY9) participates in the development of several tumors and diseases but has not been reported yet in lung adenocarcinoma (LUAD). Methods: First, we analyzed the expression and prognostic value of LY9 in pan-cancer, including LUAD. Additionally, we conducted a correlation analysis of LY9 expression in LUAD with immune cell infiltration using the TIMER database and the CIBERSORT algorithm, and with immune checkpoints using the GEPIA database. Also, we constructed a potential ceRNA network for LY9. Furthermore, we explored LY9-related pathways by Gene Set Enrichment Analysis (GSEA). Finally, validation of differential expression at the mRNA level was obtained from the GEO database. We collected LUAD tissues for Quantitative Real-time PCR (qRT-PCR) to verify the expression of LY9, CD8, and CD4 and calculated the correlation between them. We also conducted immunohistochemistry (IHC) to verify the protein expression of LY9. Results: Results showed that LY9 was highly expressed in various tumors, including LUAD. Besides, patients with high LY9 expression presented longer overall survival (OS) and more multiple lymphocyte infiltrations. The expression of LY9 in LUAD strongly and positively correlates with multiple immune cell infiltration and immune checkpoints. The functional enrichment analysis indicated that LY9 was involved in multiple immune-related pathways and non-small cell lung cancer. Moreover, a ceRNA regulatory network of LINC00943-hsa-miR-141-3p-LY9 might be involved. Finally, GSE68465 dataset confirmed differential expression of LY9 mRNA levels in LUAD and the qRT-PCR results verified LY9 had a strong and positive correlation with CD4 and CD8 T cells. Unfortunately, IHC did not detect the expression of LY9 protein level in tumor tissues and WB experiments validated the protein expression of LY9 in the OCI-AML-2 cell line. Conclusions: Therefore, we hypothesized that LY9 could serve as a potential, novel prognostic biomarker for LUAD and could predict immunotherapy efficacy at the mRNA level.

SPARK: A High-Efficiency Black-Box Domain Adaptation Framework for Source Privacy-Preserving Drowsiness Detection.

Developing an effective and efficient electroencephalography (EEG)-based drowsiness monitoring system is crucial for enhancing road safety and reducing the risk of accidents. For general usage, cross-subject evaluation is indispensable. Despite progress in unsupervised domain adaptation (UDA) and source-free domain adaptation (SFDA) methods, these often rely on the availability of labeled source data or white-box source models, posing potential privacy risks. This study explores a more challenging setting of UDA for EEG-based drowsiness detection, termed black-box domain adaptation (BBDA). In BBDA, adaptation in the target domain relies solely on a black-box source model, without access to the source data or parameters of the source model. Specifically, we propose a framework called Self-distillation and Pseudo-labelling for Ensemble Deep Random Vector Functional Link (edRVFL)-based Black-box Knowledge Adaptation (SPARK). SPARK employs entropy-based selection of high-confidence samples, which are then pseudo-labeled to train a student edRVFL network. Subsequently, ensemble self-distillation is performed to extract knowledge by training the edRVFL using refined labels introduced by ensemble learning. This process further improves the robustness of the student edRVFL network. The features of the edRVFL are beneficial for improving the computational efficiency of the framework, making it more suitable for tasks involving small datasets. The proposed SPARK framework is evaluated on two publicly available driver drowsiness datasets. Experimental results demonstrate its superior performance over strong baselines, while significantly reducing training time. These findings underscore the potential for practical integration of the proposed framework into drowsiness monitoring systems, thereby contributing substantially to the privacy preservation of source subjects.

Lateral Dorsal Basal Lung Resection Based on Functional Preserving Sublobectomy Method: Single-Center Experience.

PURPOSE: Functional preserving sublobectomy (FPSL), a novel balancing strategy for segmentectomy and wedge resection, allows rapid and accurate removal of invisible nodules without the use of any preoperative localization markers. This study aimed to share single-center experience of lateral dorsal basal lung resection based on FPSL, so as to provide new surgical options for thoracic surgeons. METHODS: A retrospective analysis was performed on 13 patients who underwent thoracoscopic basal lung resection after FPSL at XX hospital from January 2021 to August 2022. RESULTS: The operation was successfully performed in 13 patients by using FPSL, including 12 patients with malignant tumors. The mean operating time was 107.5 ± 25.6 min. The mean postoperative hospital stay was 3.7 ± 2.4 days. None of the patients needed extended excision, such as an entire basal or inferior lobectomy. CONCLUSION: Our single-center experience showed that the FPSL method only dealt with the target vessels, which greatly reduced the technical difficulty of surgery. In addition, both arteries and veins could be used as target vessels, and in particular cases such as undeveloped interlobar fissure, the operation could still be completed successfully. Lateral dorsal basal lung resection based on FPSL may be a new surgical option for surgeons.

Benchmarking EEG-based Cross-dataset Driver Drowsiness Recognition with Deep Transfer Learning.

It usually takes a long time to collect data for calibration when using electroencephalography (EEG) for driver drowsiness monitoring. Cross-dataset recognition is desirable since it can significantly save the calibration time when an existing dataset is used. However, the recognition accuracy is affected by the distribution drift problem caused by different experimental environments when building different datasets. In order to solve the problem, we propose a deep transfer learning model named Entropy-Driven Joint Adaptation Network (EDJAN), which can learn useful information from source and target domains simultaneously. An entropy-driven loss function is used to promote clustering of target-domain representations and an individual-level domain adaptation technique is proposed to alleviate the distribution discrepancy problem of test subjects. We use two public driving datasets SEEG-VIG and SADT to test the model on the cross-dataset setting. The proposed model achieved an accuracy of 83.3% when SADT is used as source domain and SEED-VIG is used as target domain and 76.7% accuracy on the reverse setting, which is higher than the other SOTA methods. The results are further analyzed with both global and local interpretation methods. Our work illuminates a promising direction of using EEG for calibration-free driver drowsiness recognition.

Construction of 11 metabolic-related lncRNAs to predict the prognosis in lung adenocarcinoma.

OBJECTIVE: To explore the metabolism-related lncRNAs in the tumorigenesis of lung adenocarcinoma. METHODS: The transcriptome data and clinical information about lung adenocarcinoma patients were acquired in TCGA (The Cancer Genome Atlas). Metabolism-related genes were from the GSEA (Gene Set Enrichment Analysis) database. Through differential expression analysis and Pearson correlation analysis, lncRNAs about lung adenocarcinoma metabolism were identified. The samples were separated into the training and validation sets in the proportion of 2:1. The prognostic lncRNAs were determined by univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) regression. A risk model was built using Multivariate Cox regression analysis, evaluated by the internal validation data. The model prediction ability was assessed by subgroup analysis. The Nomogram was constructed by combining clinical indicators with independent prognostic significance and risk scores. C-index, calibration curve, DCA (Decision Curve Analysis) clinical decision and ROC (Receiver Operating Characteristic Curve) curves were obtained to assess the prediction ability of the model. Based on the CIBERSORT analysis, the correlation between lncRNAs and tumor infiltrating lymphocytes was obtained. RESULTS: From 497 lung adenocarcinoma and 54 paracancerous samples, 233 metabolic-related and 11 prognostic-related lncRNAs were further screened. According to the findings of the survival study, the low-risk group had a greater OS (Overall survival) than the high-risk group. ROC analysis indicated AUC (Area Under Curve) value was 0.726. Then, a nomogram with T, N stage and risk ratings was developed according to COX regression analysis. The C-index was 0.743, and the AUC values of 3- and 5-year survival were 0.741 and 0.775, respectively. The above results suggested the nomogram had a good prediction ability. The results based on the CIBERSORT algorithm demonstrated the lncRNAs used to construct the model had a strong correlation with the polarization of immune cells. CONCLUSIONS: The study identified 11 metabolic-related lncRNAs for lung adenocarcinoma prognosis, on which basis a prognostic risk scoring model was created. This model may have a good predictive potential for lung adenocarcinoma.

TAGAP expression influences CD4+ T cell differentiation, immune infiltration, and cytotoxicity in LUAD through the STAT pathway: implications for immunotherapy.

Background: T-cell Activation GTPase Activating Protein (TAGAP) plays a role in immune cell regulation. This study aimed to investigate TAGAP's expression and its potential impact on CD4+ T cell function and prognosis in lung adenocarcinoma (LUAD). Methods: We analyzed TAGAP expression and its correlation with immune infiltration and clinical data in LUAD patients using multiple datasets, including The Cancer Genome Atlas (TCGA-LUAD), Gene Expression Omnibus (GEO), and scRNA-seq datasets. In vitro and in vivo experiments were conducted to explore the role of TAGAP in CD4+ T cell function, chemotaxis, and cytotoxicity. Results: TAGAP expression was significantly lower in LUAD tissues compared to normal tissues, and high TAGAP expression correlated with better prognosis in LUAD patients. TAGAP was positively correlated with immune/stromal/ESTIMATE scores and immune cell infiltration in LUAD. Single-cell RNA sequencing revealed that TAGAP was primarily distributed in CD4+/CD8+ T cells. In vitro experiments showed that TAGAP overexpression enhanced CD4+ T cell cytotoxicity, proliferation, and chemotaxis. Gene Set Enrichment Analysis (GSEA) indicated that TAGAP was enriched in the JAK-STAT signaling pathway. In vivo experiments in a xenograft tumor model demonstrated that TAGAP overexpression suppressed tumor growth and promoted CD4+ T cell cytotoxicity. Conclusions: TAGAP influences CD4+ T cell differentiation and function in LUAD through the STAT pathway, promoting immune infiltration and cytotoxicity. This study provides a scientific basis for developing novel LUAD immunotherapy strategies and exploring new therapeutic targets.

Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.

Introduction: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. Methods: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI. Results: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. Discussion: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.

A novel gene signature derived from the CXC subfamily of chemokine receptors predicts the prognosis and immune infiltration of patients with lung adenocarcinoma.

The highly malignant nature of lung adenocarcinoma (LUAD) makes its early diagnosis and prognostic assessment particularly important. However, whether the CXC subfamily of chemokine receptors (CXCR) is involved in the development and prognosis of LUAD remains unclear. Here, differentially expressed genes (DEGs) associated with overall survival (OS) were selected from the cancer genome atlas (TCGA) dataset using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Then, a prognostic gene signature was constructed, which was evaluated using Kaplan-Meier curves, receiver operating characteristics curves, nomogram curves, and an external gene expression omnibus (GEO) dataset. Finally, we verified the functions of the genes comprising the signature using the gene expression profiling interactive analysis (GEPIA) and the immune system interaction database (TISIDB) web portals. We constructed a 7-gene signature (SHC1, PRKCD, VEGFC, RPS6KA1, CAT, CDC25C, and GPI) that stratified patients into high- and low-risk categories. Notably, the risk score of the signature was a separate and effective predictor for OS (P < .001). Patients in the low-risk category had a better prognosis than those in the high-risk category. The receiver operating characteristics and nomogram curves verified the predictive power of the signature. Moreover, in both categories, biological processes and pathways associated with cell migration were enriched. Immune infiltration statuses differed between the 2 risk categories. Critically, the results from the GEPIA and TISIDB web portals indicated that the expression of the 7-gene signature was associated with survival, clinical stage, and immune subtypes of LUAD patients. We identified a CXCR-related gene signature that could assess prognosis and provide a reference for the diagnosis and treatment of LUAD.

Comprehensive analysis of angiogenesis subtype of squamous cell carcinoma.

BACKGROUND: Squamous cell carcinoma (SCC) is a disease with distinct management complexities as it displays a remarkably heterogeneous molecular subtype. However, the landscape of angiogenesis for SCC is not fully investigated. METHOD AND MATERIALS: The angiogenesis-related subtypes of SCC were established by using the ConsensusClusterPlus package based on angiogenesis-related genes and TCGA data. We analyzed the alteration of genes and miRNAs as well as pathways associated with angiogenesis subtypes. Next, the regulation network, the correlation with genomic characteristics, immune microenvironment, and clinical features of the angiogenesis subtypes were further investigated. Finally, the prognostic impact of the angiogenesis-related subtypes for SCC was also analyzed. RESULTS: A total of 1368 SCC samples were included in this study. Two angiogenesis subtypes were then identified based on the one hundred and sixty-three angiogenesis-related genes with subtype1 (angiogenesis subtype) of 951 SCC patients and subtype2 (non-angiogenesis subtype) of 417 SCC. GSEA revealed that angiogenesis and epithelial-mesenchymal transition, inflammatory response, and hypoxia were enriched in the angiogenesis subtype. Eight of the 15 immune checkpoints (ADORA2A, BTLA, CD276, CYBB, HAVCR2, SIGLEC7, SIGLEC9, and VTCN1) were significantly upregulated while C10orf54 were significantly downregulated in the angiogenesis subtype. The survival analysis revealed that the patients in the angiogenesis subtype have poorer survival outcomes than those in the non-angiogenesis subtype (P = 0.017 for disease-free interval and P = 0.00013 for overall survival). CONCLUSION: Our analysis revealed a novel angiogenesis subtype classification in SCC and provides new insights into a hallmark of SCC progression.

EGFR DNA Methylation Correlates With EGFR Expression, Immune Cell Infiltration, and Overall Survival in Lung Adenocarcinoma.

BACKGROUND: The epidermal growth factor receptor (EGFR) is a primary target of molecular targeted therapy for lung adenocarcinoma (LUAD). The mechanisms that lead to epigenetic abnormalities of EGFR in LUAD are still unclear. The purpose of our study was to evaluate the abnormal methylation of EGFR CpG sites as potential biomarkers for LUAD. METHODS: To assess the differentially methylation CpG sites of EGFR in LUAD, we used an integrative study of Illumina HumanMethylation450K and RNA-seq data from The Cancer Genome Atlas (TCGA). We evaluated and compared EGFR multiple-omics data to explore the role of CpG sites located in EGFR promoter regions and gene body regions and the association with transcripts, protein expression levels, mutations, and somatic copy number variation. We calculated the correlation coefficients between CpG sites of EGFR and immune infiltration fraction (by MCPcounter and ESTIMATE) and immune-related pathways in LUAD. Finally, we validated the differential methylation of clinically and prognostically relevant CpG sites using quantitative methylation-specific PCR (qMSP). RESULTS: We found that the methylation level of many EGFR CpGs in the promoter region was negatively correlated with the transcription level, protein expression, and SCNV, while the methylation at the gene body region was positively correlated with these features. The methylation level of EGFR CpGs in the promoter region was positively correlated with the level of immune infiltration and IFN-γ signature, while the opposite was found for methylation of the gene body region. The qMSP results showed that cg02316066 had a high methylation level, while cg02166842 had a low methylation level in LUAD. There was a high degree of co-methylation between cg02316066 and cg03046247. CONCLUSION: Our data indicate that EGFR is an epigenetic regulator in LUAD acting through DNA methylation. Our research provides a theoretical basis for the further detection of EGFR DNA methylation as a predictive biomarker for LUAD survival and immunotherapy.

Development of a gene signature associated with iron metabolism in lung adenocarcinoma.

There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Therefore, our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1), and patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and low-risk groups was significantly different (TCGA: P < 0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P < 0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. In addition, immune-related pathways were highlighted in the functional enrichment analysis. In conclusion, we developed and verified a 12-gene prognostic signature, which may be help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.

Comprehensive analysis of DNA damage repair in squamous cell carcinoma subtypes.

AIMS: Defective components resulting from DNA damage and repair mechanisms have been found to be underlying causes that affect the development and progression of different types of cancers, including squamous cell carcinoma (SCC). A more detailed classification of SCC is necessary for better application of DNA damage repair therapies. MATERIALS AND METHODS: We aimed to characterize the molecular profile of SCC by developing a classification system based on DNA damage repair gene expression profiles. An integrative analysis was performed using a metadata set of 1374 SCC human samples from the UCSC Genome Browser. We then analyzed genomic alterations and mutations, and genes-TF-microRNA regulatory relationships and conducted enrichment, survival, and immune infiltration analyses. KEY FINDINGS: This study was conducted on a total of 1374 SCC patients and 402 DNA damage repair genes. Two subtypes were established using consensus clustering, with 1143 patients being of the Non DDR subtype and 231 patients being of the DDR subtype. MATH, mutation burden, and heterogeneity were significantly higher in Non-DDR subtype than in DDR subtype. Next, a total of 1081 differentially expressed genes and 21 microRNAs were identified between the two subtypes and a genes-TF-microRNA regulatory network was constructed. In addition, stromal score, immune score and ESTIMATE score were significantly lower for the Non-DDR subtype, while tumor purity was significantly lower for the DDR subtype. In addition, five pathways associated with DNA damage repair were all enriched in the DDR subtype. SIGNIFICANCE: Our study established two subtypes of SCC based on DNA damage repair, which may help to predict prognosis and determine the most suitable treatment for SCC patients.

Hypoxia-associated alternative splicing signature in lung adenocarcinoma.

Aim: To establish a signature based on hypoxia-related alternative splicing (AS) events for lung adenocarcinoma. Materials & methods: The least absolute shrinkage and selection operator Cox approach was used to construct a prognostic model. A nomogram that integrates the final AS predictor and stage was created. The network of the key AS events and splicing factors was created. Results: We created a prognostic signature of 11 AS events. Moreover, a nomogram that constitutes the pathological stage and risk was exhibited to be greatly effective in estimating the survival likelihood of lung adenocarcinoma patients. Conclusion: Herein we developed the first-ever signature based on hypoxia-related AS events with both prognostic predictive power and diagnostic efficacy.

Analysis of prognostic genes in the tumor microenvironment of lung adenocarcinoma.

BACKGROUND: Prognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer. METHODS: The RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell. RESULTS: In this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.

Novel immune subtypes of lung adenocarcinoma identified through bioinformatic analysis.

The magnitude of the immune response is closely associated with clinical outcome in patients with cancer. However, finding potential therapeutic targets for lung cancer in the immune system remains challenging. Here, we constructed a vital immune-prognosis genes (VIPGs) based cluster of lung adenocarcinoma (LUAD) from IMMPORT databases and The Cancer Genome Atlas. A transcription factor regulatory network for the VIPGs was also established. The tumor microenvironment of LUAD was analyzed using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and single-sample Gene Set Enrichment Analysis. The immune checkpoints and genomic alterations were explored in the different immune clusters. We identified 15 VIPGs for patients with LUAD and clustered the patients into low-immunity and high-immunity subtypes. The immune score, stromal score and ESTIMATE score were significantly higher in the high-immunity subtype, whereas tumor purity was higher in the low-immunity subtype. In addition, the immune checkpoints cytotoxic T lymphocyte associate protein-4(CTLA4), programmed cell death protein-1 and programmed death-ligand were elevated in the low-immunity subtype. The genomic results also showed that the tumor mutation burden was higher in the high-immunity subtype. Finally, Gene Set Enrichment Analysis showed that several immune-related gene sets, including interleukin-2/STAT5 signaling, inflammatory response, interleukin-6/Janus kinase(JAK)/signal transducer and activator of transcription 3 (STAT3) signaling, interferon-gamma response and allograft rejection, were elevated in the high-immunity subtype. Finally, high-immunity patients exhibited greater overall and disease-specific survival outcome compared with low-immunity patients (log rank P = 0.013 and P = 0.0097). Altogether, here we have identified 15 immune-prognosis genes and a potential immune subtype for patients with LUAD, which may provide new insights into the prognosis and treatment of LUAD.

Circ-IGF1R inhibits cell invasion and migration in non-small cell lung cancer.

BACKGROUND: Circular RNA (circRNA) is a novel molecular marker and target candidate that is closely associated with tumor invasion and migration. The mechanism of action of hsa_circ_0005035 (circ-IGF1R) in non-small cell lung cancer remains unclear. In this study, we aimed to study the mechanism of action of circ-IGF1R in lung cancer. METHODS: We screened circ-IGF1R, one of the most notable differential expressions, from the Gene Expression Omnibus database, GSE104854, for further research. The expression level of circ-IGF1R was examined using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) in five different lung cancer cell lines and 50 pairs of lung cancer and adjacent tissues. Wound-healing and Transwell assays were used for verifying the biological function of circ-IGF1R. The effect of overexpressing circ-IGF1R on the transcriptome of whole lung cancer cells was explored in lung cancer cell lines using RNA-seq. RESULTS: The expression level of circ-IGF1R was notably lower in lung cancer tissues and lung cancer cell lines than in the adjacent normal tissues and cells (P < 0.0001). In addition, the expression level of circ-IGF1R was associated with larger tumors (T2/T3/T4) and lymph node metastasis (N1/ N2/N3) (P < 0.05). The overexpression of circ-IGF1R significantly inhibited the invasion and migration of the lung cancer cells. The potential network of circ-IGF1R-miR-1270-VANGL2 was preliminarily determined, and the expression patterns of miR-1270 and VANGL2 were verified in lung cancer cell lines. CONCLUSION: Circ-IGF1R may inhibit lung cancer invasion and migration through a potential network of circ-IGF1R-miR-1270-VANGL2.