GTSE1: A potential prognostic and diagnostic biomarker in various tumors including lung adenocarcinoma.

OBJECTIVE: This research was aimed to comprehensively investigate the expression levels, diagnostic and prognostic implications, and the relationship with immune infiltration of G2 and S phase-expressed-1 (GTSE1) across 33 tumor types, including lung adenocarcinoma (LUAD), through gene expression profiling. METHODS: GTSE1 mRNA expression data together with clinical information were acquired from Xena database of The Cancer Genome Atlas (TCGA), ArrayExpress, and Gene Expression Omnibus (GEO) database for this study. The Wilcoxon rank-sum test was used to detect differences in GTSE1 expression between groups. The ability of GTSE1 to accurately predict cancer status was evaluated by calculating the area under the curve (AUC) value for the receiver operating characteristic curve. Additionally, we investigated the predictive value of GTSE1 in individuals diagnosed with neoplasms using univariate Cox regression analysis as well as Kaplan-Meier curves. Furthermore, the correlation between GTSE1 expression and levels of immune infiltration was assessed by utilizing the Tumor Immune Estimate Resource (TIMER) database to calculate the Spearman rank correlation coefficient. Finally, the pan-cancer analysis findings were validated by examining the association between GTSE1 expression and prognosis among patients with LUAD. RESULTS: GTSE1 exhibited significantly increased expression levels in a wide range of tumor tissues in contrast with normal tissues (p < 0.05). The expression of GTSE1 in various tumors was associated with clinical features, overall survival, and disease-specific survival (p < 0.05). In immune infiltration analyses, a strong correlation of the level of immune infiltration with the expression of GTSE1 was observed. Furthermore, GTSE1 demonstrated good discriminative and diagnostic value for most tumors. Additional experiments confirmed the relationship between elevated GTSE1 expression and unfavorable prognosis in individuals diagnosed with LUAD. These findings indicated the crucial role of GTSE1 expression level in influencing the development and immune infiltration of different types of tumors. CONCLUSIONS: GTSE1 might be a potential biomarker for the prognosis of pan-cancer. Meanwhile, it represented a promising target for immunotherapy.

PM2.5 regulates the progression of lung adenocarcinoma through the axis of HCG18, miR-195 and ATG14.

Relevant studies have indicated the association of HCG18 with tumour occurrence and progression. In this study, we observed that PM2.5 can enhance the growth of lung adenocarcinoma cells by modulating the expression of HCG18. Further investigations, including overexpression and knockout experiments, elucidated that HCG18 suppresses miR-195, which in turn upregulates the expression of ATG14, resulting in the upregulation of autophagy. Consequently, exposure to PM2.5 leads to elevated HCG18 expression in lung tissues, which in turn increases Atg14 expression and activates autophagy pathways through inhibition of miR-195, thereby contributing to oncogenesis.

Abnormal methylation mediated upregulation of LINC00857 boosts malignant progression of lung adenocarcinoma by modulating the miR-486-5p/NEK2 axis.

LINC00857 is frequently dysregulated in varying cancers, which in turn exerts carcinogenic effects; however, its DNA methylation status in promoter region and molecular mechanisms underlying the progression of lung adenocarcinoma (LUAD) remain rarely understood. Through bioinformatics analysis, we examined the expression state and methylation site of LINC00857 in LUAD and further investigated the properties of LINC00857 as a competitive endogenous RNA in the cancer progression. The current study revealed that the overexpression of LINC00857 in LUAD tissue and cells was mainly caused by the hypomethylation of the promoter region. LINC00857 knockdown prominently reduced cell proliferation, impeded cell migration and invasion, and restrained lymph node metastasis, with enhancing radiosensitivity. The effects of LINC00857 on tumor growth were also investigated in nude mice models. Subsequently, the downstream factors, miR-486-5p and NEK2, were screened, and the putative regulatory axis was examined. Overall, the regulatory effect of methylation-mediated LINC00857 overexpression on miR-486-5p/NEK2 axis may be a new mechanism for LUAD progression.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma.

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

Novel genome-wide DNA methylation profiling reveals distinct epigenetic landscape, prognostic model and cellular composition of early-stage lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) has been a leading cause of cancer-related mortality worldwide. Early intervention can significantly improve prognosis. DNA methylation could occur in the early stage of tumor. Comprehensive understanding the epigenetic landscape of early-stage LUAD is crucial in understanding tumorigenesis. METHODS: Enzymatic methyl sequencing (EM-seq) was performed on 23 tumors and paired normal tissue to reveal distinct epigenetic landscape, for compared with The Cancer Genome Atlas (TCGA) 450K methylation microarray data. Then, an integrative analysis was performed combined with TCGA LUAD RNA-seq data to identify significant differential methylated and expressed genes. Subsequently, the prognostic risk model was constructed and cellular composition was analyzed. RESULTS: Methylome analysis of EM-seq comparing tumor and normal tissues identified 25 million cytosine-phosphate-guanine (CpG) sites and 30,187 differentially methylated regions (DMR) with a greater number of untraditional types. EM-seq identified a significantly higher number of CpG sites and DMRs compared to the 450K microarray. By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset. Finally, cell deconvolution based on the in-house EM-seq methylation profile was used to estimate cellular composition of early-stage LUAD. CONCLUSIONS: This study firstly delves into novel pattern of epigenomic DNA methylation and provides a multidimensional analysis of the role of DNA methylation revealed by EM-seq in early-stage LUAD, providing distinctive insights into its potential epigenetic mechanisms.

DDX18 Facilitates the Tumorigenesis of Lung Adenocarcinoma by Promoting Cell Cycle Progression through the Upregulation of CDK4.

Lung adenocarcinoma (LUAD) is the most prevalent and aggressive subtype of lung cancer, exhibiting a dismal prognosis with a five-year survival rate below 5%. DEAD-box RNA helicase 18 (DDX18, gene symbol DDX18), a crucial regulator of RNA metabolism, has been implicated in various cellular processes, including cell cycle control and tumorigenesis. However, its role in LUAD pathogenesis remains elusive. This study demonstrates the significant upregulation of DDX18 in LUAD tissues and its association with poor patient survival (from public databases). Functional in vivo and in vitro assays revealed that DDX18 knockdown potently suppresses LUAD progression. RNA sequencing and chromatin immunoprecipitation experiments identified cyclin-dependent kinase 4 (CDK4), a cell cycle regulator, as a direct transcriptional target of DDX18. Notably, DDX18 depletion induced G1 cell cycle arrest, while its overexpression promoted cell cycle progression even in normal lung cells. Interestingly, while the oncogenic protein c-Myc bound to the DDX18 promoter, it did not influence its expression. Collectively, these findings establish DDX18 as a potential oncogene in LUAD, functioning through the CDK4-mediated cell cycle pathway. DDX18 may represent a promising therapeutic target for LUAD intervention.

Single-cell sequencing of tumour infiltrating T cells efficiently identifies tumour-specific T cell receptors based on the T cell activation score.

Adoptively transferred T cell receptor-engineered T cells are a promising cancer treatment strategy, and the identification of tumour-specific TCRs is essential. Previous studies reported that tumour-reactive T cells and TCRs could be isolated based on the expression of activation markers. However, since T cells with different cell states could not respond uniformly to activation but show a heterogeneous expression profile of activation and effector molecules, isolation of tumour-reactive T cells based on single activation or effector molecules could result in the absence of tumour-reactive T cells; thus, combinations of multiple activation and effector molecules could improve the efficiency of isolating tumour-specific TCRs. We enrolled two patients with lung adenocarcinoma and obtained their tumour infiltrating lymphocytes (TILs) and autologous tumour cells (ATCs). TILs were cocultured with the corresponding ATCs for 12 h and subjected to single-cell RNA sequencing. First, we identified three TCRs with the highest expression levels of IFNG and TNFRSF9 mRNA for each patient, yet only the top one or two recognized the corresponding ATCs in each patient. Next, we defined the activation score based on normalized expression levels of IFNG, IL2, TNF, IL2RA, CD69, TNFRSF9, GZMB, GZMA, GZMK, and PRF1 mRNA for each T cell and then identified three TCRs with the highest activation score for each patient. We found that all three TCRs in each patient could specifically identify corresponding ATCs. In conclusion, we established an efficient approach to isolate tumour-reactive TCRs based on combinations of multiple activation and effector molecules through single-cell RNA sequencing.

Integration of multi-omics data for survival prediction of lung adenocarcinoma.

BACKGROUND AND OBJECTIVE: The morbidity of lung adenocarcinoma (LUAD) has been increasing year by year and the prognosis is poor. This has prompted researchers to study the survival of LUAD patients to ensure that patients can be cured in time or survive after appropriate treatment. There is still no fully valid model that can be applied to clinical practice. METHODS: We introduced struc2vec-based multi-omics data integration (SBMOI), which could integrate gene expression, somatic mutations and clinical data to construct mutation gene vectors representing LUAD patient features. Based on the patient features, the random survival forest (RSF) model was used to predict the long- and short-term survival of LUAD patients. To further demonstrate the superiority of SBMOI, we simultaneously replaced scale-free gene co-expression network (FCN) with a protein-protein interaction (PPI) network and a significant co-expression network (SCN) to compare accuracy in predicting LUAD patient survival under the same conditions. RESULTS: Our results suggested that compared with SCN and PPI network, the FCN based SBMOI combined with RSF model had better performance in long- and short-term survival prediction tasks for LUAD patients. The AUC of 1-year, 5-year, and 10-year survival in the validation dataset were 0.791, 0.825, and 0.917, respectively. CONCLUSIONS: This study provided a powerful network-based method to multi-omics data integration. SBMOI combined with RSF successfully predicted long- and short-term survival of LUAD patients, especially with high accuracy on long-term survival. Besides, SBMOI algorithm has the potential to combine with other machine learning models to complete clustering or stratificational tasks, and being applied to other diseases.

Prognostic and immunotherapeutic implications of bilirubin metabolism-associated genes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of non-small-cell lung cancer and accompanies high mortality rates. While the role of bilirubin metabolism in cancer is recognized, its specific impact on LUAD and patient response to immunotherapy needs to be elucidated. This study aimed to develop a prognostic signature of bilirubin metabolism-associated genes (BMAGs) to predict outcomes and efficacy of immunotherapy in LUAD. We analysed gene expression data from The Cancer Genome Atlas (TCGA) to identify survival-related BMAGs and construct a prognostic model in LUAD. The prognostic efficacy of our model was corroborated by employing TCGA-LUAD and five Gene Expression Omnibus datasets, effectively stratifying patients into risk-defined cohorts with marked disparities in survival. The BMAG signature was indeed an independent prognostic determinant, outperforming established clinical parameters. The low-risk group exhibited a more favourable response to immunotherapy, highlighted by increased immune checkpoint expression and immune cell infiltration. Further, somatic mutation profiling differentiated the molecular landscapes of the risk categories. Our screening further identified potential drug candidates preferentially targeting the high-risk group. Our analysis of critical BMAGs showed the tumour-suppressive role of FBP1, highlighting its suppression in LUAD and its inhibitory effects on tumour proliferation, migration and invasion, in addition to its involvement in cell cycle and apoptosis regulation. These findings introduce a potent BMAG-based prognostic indicator and offer valuable insights for prognostication and tailored immunotherapy in LUAD.

Knockdown of NADK promotes LUAD ferroptosis via NADPH/FSP1 axis.

BACKGROUND: Lung cancer is a serious threat to human health and is the first leading cause of cancer death. Ferroptosis, a newly discovered form of programmed cell death associated with redox homeostasis, is of particular interest in the lung cancer, given the high oxygen environment of lung cancer. NADPH has reducing properties and therefore holds the potential to resist ferroptosis. Resistance to ferroptosis exists in lung cancer, but the role of NADK in regulating ferroptosis in lung cancer has not been reported yet. METHODS: Immunohistochemistry (IHC) was used to analyse the expression of NADK in 86 cases of lung adenocarcinoma(LUAD) and adjacent tissues, and a IHC score was assigned to each sample. Chi-square and kaplan-meier curve was performed to analyse the differences in metastasis and five-year survival between the two groups with NADK high or low scores. Proliferation of NADK-knockdown LUAD cell lines was detected in vivo and vitro. Furthermore, leves of ROS, MDA and Fe2+ were measured to validate the effect and mechanism of NADK on ferroptosis in LUAD. RESULTS: The expression of NADK was significantly evaluated in LUAD tissues as compared to adjacent non-cancerous tissues. The proliferation of NADK-knockdown cells was inhibited both in vivo and vitro, and increasing levels of intracellular ROS, Fe2+ and lipid peroxide products (MDA) were observed. Furthermore, NADK-knockdown promoted the ferroptosis of LUAD cells induced by Erastin/RSL3 by regulating the level of NADPH and the expression of FSP1. Knockdown of NADK enhanced the sensitivities of LUAD cells to Erastin/RSL3-induced ferroptosis by regulating NADPH level and FSP1 expression. CONCLUSIONS: NADK is over-expressed in LUAD patients. Knockdown of NADK inhibited the proliferation of LUAD cells both in vitro and in vivo and promotes the Erastin/RSL3-induced ferroptosis of LUAD cells by down-regulating the NADPH/FSP1 axis.

Comprehensive molecular analyses of cuproptosis-related genes with regard to prognosis, immune landscape, and response to immune checkpoint blockers in lung adenocarcinoma.

BACKGROUND: Recent studies have emphasized the importance of the biological processes of different forms of cell death in tumor heterogeneity and anti-tumor immunity. Nonetheless, the relationship between cuproptosis and lung adenocarcinoma (LUAD) remains largely unexplored. METHODS: Data for 793 LUAD samples and 59 normal lung tissues obtained from TCGA-LUAD cohort GEO datasets were used in this study. A total of 165 LUAD tissue samples and paired normal lung tissue samples obtained from our hospital were used to verify the prognostic value of dihydrolipoamide S-acetyltransferase (DLAT) and dihydrolipoamide branched chain transacylase E2 (DBT) for LUAD. The cuproptosis-related molecular patterns of LUAD were identified using consensus molecular clustering. Recursive feature elimination with random forest and a tenfold cross-validation method was applied to construct the cuproptosis score (CPS) for LUAD. RESULTS: Bioinformatic and immunohistochemistry (IHC) analyses revealed that 13 core genes of cuproptosis were all significantly elevated in LUAD tissues, among which DBT and DLAT were associated with poor prognosis (DLAT, HR = 6.103; DBT, HR = 4.985). Based on the expression pattern of the 13 genes, two distinct cuproptosis-related patterns have been observed in LUAD: cluster 2 which has a relatively higher level of cuproptosis was characterized by immunological ignorance; conversely, cluster 1 which has a relatively lower level of cuproptosis is characterized by TILs infiltration and anti-tumor response. Finally, a scoring scheme termed the CPS was established to quantify the cuproptosis-related pattern and predict the prognosis and the response to immune checkpoint blockers of each individual patient with LUAD. CONCLUSION: Cuproptosis was found to influence tumor microenvironment (TME) characteristics and heterogeneity in LUAD. Patients with a lower CPS had a relatively better prognosis, more abundant immune infiltration in the TME, and an enhanced response to immune checkpoint inhibitors.

The expression characteristic and prognostic role of Siglec-15 in lung adenocarcinoma.

Sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15) has been identified as an immune suppressor and a promising candidate for immunotherapy of cancer management. However, the association between Siglec-15 expression and clinicopathological features of lung adenocarcinoma (LUAD), especially the prognostic role, is not fully elucidated. In this present study, a serial of bioinformatics analyses in both tissue and cell levels were conducted to provide an overview of Siglec-15 expression. Real-time quantitative PCR (qPCR) test, western blotting assay, and immunohistochemistry (IHC) analyses were conducted to evaluate the expression of Siglec-15 in LUAD. Survival analysis and Kaplan-Meier curve were employed to describe the prognostic parameters of LUAD. The results of bioinformatics analyses demonstrated the up-regulation of Siglec-15 expression in LUAD. The data of qPCR, western blotting, and IHC analyses further proved that the expression of Siglec-15 in LUAD tissues was significantly increased than that in noncancerous tissues. Moreover, the expression level of Siglec-15 protein in LUAD was substantially associated with TNM stage. LUAD cases with up-regulated Siglec-15 expression, positive N status, and advance TNM stage suffered a critical unfavorable prognosis. In conclusion, Siglec-15 could be identified as a novel prognostic biomarker in LUAD and targeting Siglec-15 may provide a promising strategy for LUAD immunotherapy.

Clinical relevance of somatic mutations in Chinese lung adenocarcinoma and their prognostic implications for survival.

BACKGROUND: To comprehensively elucidate the genomic and mutational features of lung cancer cases, and lung adenocarcinoma (LUAD), it is imperative to conduct ongoing investigations into the genomic landscape. In this study, we aim to analyze the somatic mutation profile and assessed the significance of these informative genes utilizing a retrospective LUAD cohort. METHODS: A total of 247 Chinese samples were analyzed to exhibit the tumor somatic genomic alterations in patients with LUAD. The Cox regression analysis was employed to identify prognosis-related genes and establish a predictive model for stratifying patients with LUAD. RESULTS: In the Dianjiang People's Hospital (DPH) cohort, the top five frequent mutated genes were (Epidermal growth factor receptor) EGFR (68%), TP53 (30%), RBM10 (13%), LRP1B (9%), and KRAS (9%). Of which, EGFR is a mostly altered driver gene, and most mutation sites are located in tyrosine kinase regions. Oncogene pathway alteration and mutation signature analysis demonstrated the RTK-RAS pathway alteration, and smoking was the main carcinogenic factor of the DPH cohort. Furthermore, we identified 34 driver genes in the DPH cohort, including EGFR (68%), TP53 (30.4%), RBM10 (12.6%), KRAS (8.5%), LRP1B (8.5%), and so on, and 45 Clinical Characteristic-Related Genes (CCRGs) were found to closely related to the clinical high-risk factors. We developed a Multiple Parameter Gene Mutation (MPGM) risk model by integrating critical genes and oncogenic pathway alterations in LUAD patients from the DPH cohort. Based on publicly available LUAD datasets, we identified five genes, including BRCA2, Anaplastic lymphoma kinase (ALK), BRAF, EGFR, and Platelet-Derived Growth Factor Receptor Alpha (PDGFRA), according to the multivariable Cox regression analysis. The MPGM-low group showed significantly better overall survival (OS) compared to the MPGM-high group (p < 0.0001, area under the curve (AUC) = 0.754). The robust performance was validated in 55 LUAD patients from the DPH cohort and another LUAD dataset. Immune characteristics analysis revealed a higher proportion of primarily DCs and mononuclear cells in the MPGM-low risk group, while the MPGM-high risk group showed lower immune cells and higher tumor cell infiltration. CONCLUSION: This study provides a comprehensive genomic landscape of Chinese LUAD patients and develops an MPGM risk model for LUAD prognosis stratification. Further follow-up will be performed for the patients in the DPH cohort consistently to explore the resistance and prognosis genetic features.

TM6SF1 suppresses the progression of lung adenocarcinoma and M2 macrophage polarization by inactivating the PI3K/AKT/mtor pathway.

Transmembrane 6 superfamily 1 (TM6SF1) is lowly expressed in lung adenocarcinoma (LUAD), but the function and mechanisms of TM6SF1 remain unclear. Thus, we attempt to explore the function of TM6SF1 and its underlying mechanisms in LUAD. qRT-PCR was used for detecting TM6SF1 mRNA expression. Immunohistochemistry staining was used for detecting the expression of MMP-2, TM6SF1, Ki67, MMP-9, and CD163 proteins. E-cadherin, p-PI3K, Vimentin, AKT, N-cadherin, PI3K, p-AKT, mTOR, p-mTOR, and marker proteins of M2 macrophages were evaluated using Western blot. CD206 protein expression was examined via immunofluorescence. The IL-10 concentration was measured via enzyme-linked immunosorbent assay (ELISA). Using CCK-8, colony formation and transwell assays, cell proliferation, migration, and invasion were assessed. A549 cells were injected into the mice's flank for establishing a mouse tumor model and into the tail vein for establishing the lung metastasis model. HE staining was performed to detect pathological changes in lung tissues. Decreased TM6SF1 expression was found in LUAD tissues and cells. TM6SF1 overexpression inhibited cell viability, proliferation, invasion, migration, EMT, and polarization of M2 macrophages in LUAD cells, along with tumor growth and metastasis in xenograft mice. Bioinformatics analysis demonstrated that TM6SF1 was correlated with the tumor microenvironment. TM6SF1 overexpression reduced expression levels of p-mTOR, p-PI3K, p-AKT, mTOR, and AKT. TM6SF1-caused inhibition of proliferation, migration, invasion and EMT, as M2 macrophage polarization was reversed by the PI3K activator in LUAD cells. TM6SF1 inactivated the PI3K/AKT/mTOR pathway to suppress LUAD malignancy and polarization of M2 macrophages, providing insight for developing new LUAD treatments.

NGEF is a potential prognostic biomarker and could serve as an indicator for immunotherapy and chemotherapy in lung adenocarcinoma.

BACKGROUND: Neuronal guanine nucleotide exchange factor (NGEF) plays a key role in several cancers; however, its role in lung adenocarcinoma (LUAD) remains unclear. The aim of this study was to evaluate the efficacy of NGEF as a prognostic biomarker and potential therapeutic target for LUAD. METHODS: NGEF expression data for multiple cancers and LUAD were downloaded from multiple databases. The high- and low-NGEF expression groups were constructed based on median NGEF expression in LUAD samples, and then performed Kaplan-Meier survival analysis. Differentially expressed genes (DEGs) from the two NGEF expression groups were screened and applied to construct a protein-protein interaction network. The primary pathways were obtained using gene set enrichment analysis. The associations between NGEF expression and clinical characteristics, immune infiltration, immune checkpoint inhibitors (ICIs), sensitivity to chemotherapy, and tumor mutation burden (TMB) were investigated using R. Levels of NGEF expression in the lung tissue was validated using single-cell RNA sequencing, quantitative polymerase chain reaction (qPCR), immunohistochemical staining, and western blot analysis. RESULTS: The expression of NGEF mRNA was upregulated in multiple cancers. mRNA and protein expression levels of NGEF were higher in patients with LUAD than in controls, as validated using qPCR and western blot. High NGEF expression was an independent prognostic factor for LUAD and was associated with advanced tumor stage, large tumor size, more lymph node metastasis, and worse overall survival (OS). A total of 182 overlapping DEGs were screened between The Cancer Genome Atlas and GSE31210, among which the top 20 hub genes were identified. NGEF expression was mainly enriched in the pathways of apoptosis, cell cycle, and DNA replication. Moreover, elevated NGEF expression were associated with a high fraction of activated memory CD4+ T cells and M0 macrophages; elevated expression levels of the ICIs: programmed cell death 1 and programmed cell death 1 ligand 1 expression; higher TMB; and better sensitivity to bortezomib, docetaxel, paclitaxel, and parthenolide, but less sensitivity to axitinib and metformin. CONCLUSION: NGEF expression is upregulated in LUAD and is significantly associated with tumor stages, OS probability, immune infiltration, immunotherapy response, and chemotherapy response. NGEF may be a potential diagnostic and prognostic biomarker and therapeutic target in LUAD.

An exosomes-related lncRNA prognostic model correlates with the immune microenvironment and therapy response in lung adenocarcinoma.

Recent research highlights the significance of exosomes and long noncoding RNAs (lncRNAs) in cancer progression and drug resistance, but their role in lung adenocarcinoma (LUAD) is not fully understood. We analyzed 121 exosome-related (ER) mRNAs from the ExoBCD database, along with mRNA and lncRNA expression profiles of TCGA-LUAD using "DESeq2", "survival," "ConsensusClusterPlus," "GSVA," "estimate," "glmnet," "clusterProfiler," "rms," and "pRRophetic" R packages. This comprehensive approach included univariate cox regression, unsupervised consensus clustering, GSEA, functional enrichment analysis, and prognostic model construction. Our study identified 134 differentially expressed ER-lncRNAs, with 19 linked to LUAD prognosis. These ER-lncRNAs delineated two patient subtypes, one with poorer outcomes. Additionally, 286 differentially expressed genes were related to these ER-lncRNAs, 261 of which also correlated with LUAD prognosis. We constructed an ER-lncRNA-related prognostic model and calculated an ER-lncRNA-related risk score (ERS), revealing that a higher ERS correlates with poor overall survival in both the Meta cohort and two validation cohorts. The ERS potentially serves as an independent prognostic factor, and the prognostic model demonstrates superior predictive power. Notably, significant differences in the immune landscape were observed between the high- and low-ERS groups. Drug sensitivity analysis indicated varying responses to common chemotherapy drugs based on ERS stratification, with the high-ERS group showing greater sensitivity, except to rapamycin and erlotinib. Experimental validation confirmed that thymidine kinase 1 enhances lung cancer invasion, metastasis, and cell cycle progression. Our study pioneers an ER-lncRNA-related prognostic model for LUAD, proposing that ERS-based risk stratification could inform personalized treatment strategies to improve patient outcomes.

[Establishment of Dual Fluorescent Labeled Human High Bone Metastasis 
Lung Adenocarcinoma Cell Line and Transcriptomic Characterization Analysis].

BACKGROUND: Bone is a common site for metastasis in lung adenocarcinoma, but the mechanism behind lung adenocarcinoma bone metastasis is still unclear. And currently, there is a lack of easily traceable and stable lung adenocarcinoma bone metastasis cell models, which limits the research on the mechanism of lung adenocarcinoma bone metastasis. The establishment of human lung adenocarcinoma cell line that are highly metastatic to bone, labeled with green fluorescent proteins (GFP) and fireflies luciferase (LUC), along with transcriptomic characterization, would be beneficial for research on lung adenocarcinoma bone metastasis and provide new experimental methods. METHODS: The human lung adenocarcinoma cell line A549-GFP-LUC was injected into nude mice via the left ventricle to construct a bone metastasis model, and was domesticated in vivo for three consecutive times to obtain the human high bone metastasis lung adenocarcinoma cell line A549-GFP-LUC-BM3; cell counting kit-8 (CCK-8), colony formation assay, scratch wound assays, Transwell assay and Western blot were used to compare the proliferation and invasion abilities of A549-GFP-LUC-BM3 with the parental cells. A549-GFP-LUC-BM3 cells and parental cells were further analyzed by transcriptomic sequencing. RESULTS: Human high-bone metastatic lung adenocarcinoma cells A549-GFP-LUC-BM3 was successfully established. Compared to parental cells, this cells exhibited a significantly higher incidence of bone metastasis and enhanced in vitro proliferation, migration, and invasion abilities. Transcriptomic sequencing results revealed that the A549-GFP-LUC-BM3 cell line had 2954 differentially expressed genes compared to the parental cells, with 1021 genes up-regulated and 1933 genes down-regulated. Gene Ontology (GO) functional enrichment analysis indicated that the differentially expressed genes were primarily localized in cellular components such as the cell periphery. The molecular functions identified as significantly enriched included signaling receptor activity, calcium ion binding, and extracellular matrix structural constituent. Additionally, the biological processes found to be enriched were cell adhesion and biological adhesion. The enrichment analysis conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that the differentially expressed genes were primarily involved in the metabolism of xenobiotics by cytochrome P450, retinol metabolism, drug metabolism-cytochrome P450, cell adhesion molecules, steroid hormone biosynthesis, and the nuclear factor kappa B (NF-κB) signaling pathway. CONCLUSIONS: The highly bone-metastatic human lung adenocarcinoma cell line with GFP and luciferase double labeling was successfully established. The biological behavior and transcriptome sequencing of the cell line suggest that it has a high bone-metastatic potential.

Associations between immune cell phenotypes and lung cancer subtypes: insights from mendelian randomization analysis.

INTRODUCTION: Lung cancer is a common malignant tumor, and different types of immune cells may have different effects on the occurrence and development of lung cancer subtypes, including lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). However, the causal relationship between immune phenotype and lung cancer is still unclear. METHODS: This study utilized a comprehensive dataset containing 731 immune phenotypes from the European Bioinformatics Institute (EBI) to evaluate the potential causal relationship between immune phenotypes and LUSC and LUAD using the inverse variance weighted (IVW) method in Mendelian randomization (MR). Sensitivity analyses, including MR-Egger intercept, Cochran Q test, and others, were conducted for the robustness of the results. The study results were further validated through meta-analysis using data from the Transdisciplinary Research Into Cancer of the Lung (TRICL) data. Additionally, confounding factors were excluded to ensure the robustness of the findings. RESULTS: Among the final selection of 729 immune cell phenotypes, three immune phenotypes exhibited statistically significant effects with LUSC. CD28 expression on resting CD4 regulatory T cells (OR 1.0980, 95% CI: 1.0627-1.1344, p < 0.0001) and CD45RA + CD28- CD8 + T cell %T cell (OR 1.0011, 95% CI: 1.0007; 1.0015, p < 0.0001) were associated with increased susceptibility to LUSC. Conversely, CCR2 expression on monocytes (OR 0.9399, 95% CI: 0.9177-0.9625, p < 0.0001) was correlated with a decreased risk of LUSC. However, no significant causal relationships were established between any immune cell phenotypes and LUAD. CONCLUSION: This study demonstrates that specific immune cell types are associated with the risk of LUSC but not with LUAD. While these findings are derived solely from European populations, they still provide clues for a deeper understanding of the immunological mechanisms underlying lung cancer and may offer new directions for future therapeutic strategies and preventive measures.

Long non­coding RNA lung cancer­associated transcript 1 regulates ferroptosis via microRNA­34a­5p­mediated GTP cyclohydrolase 1 downregulation in lung cancer cells.

Ferroptosis, a recently discovered type of programmed cell death triggered by excessive accumulation of iron­dependent lipid peroxidation, is linked to several malignancies, including non­small cell lung cancer. Long non­coding RNAs (lncRNAs) are involved in ferroptosis; however, data on their role and mechanism in cancer therapy remains limited. Therefore, the aim of the present study was to identify ferroptosis­associated mRNAs and lncRNAs in A549 lung cancer cells treated with RAS­selective lethal 3 (RSL3) and ferrostatin­1 (Fer­1) using RNA sequencing. The results demonstrated that lncRNA lung cancer­associated transcript 1 (LUCAT1) was significantly upregulated in lung adenocarcinoma and lung squamous cell carcinoma tissues. Co­expression analysis of differentially expressed mRNAs and lncRNAs suggested that LUCAT1 has a crucial role in ferroptosis. LUCAT1 expression was markedly elevated in A549 cells treated with RSL3, which was prevented by co­incubation with Fer­1. Functionally, overexpression of LUCAT1 facilitated cell proliferation and reduced the occurrence of ferroptosis induced by RSL3 and Erastin, while inhibition of LUCAT1 expression reduced cell proliferation and increased ferroptosis. Mechanistically, downregulation of LUCAT1 resulted in the downregulation of both GTP cyclohydrolase 1 (GCH1) and ferroptosis suppressor protein 1 (FSP1). Furthermore, inhibition of LUCAT1 expression upregulated microRNA (miR)­34a­5p and then downregulated GCH1. These results indicated that inhibition of LUCAT1 expression promoted ferroptosis by modulating the downregulation of GCH1, mediated by miR­34a­5p. Therefore, the combination of knocking down LUCAT1 expression with ferroptosis inducers may be a promising strategy for lung cancer treatment.

Identification and analysis of prognostic immune cell homeostasis characteristics in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most invasive malignant tumor of the respiratory system. It is also the common pathological type leading to the death of LUAD. Maintaining the homeostasis of immune cells is an important way for anti-tumor immunotherapy. However, the biological significance of maintaining immune homeostasis and immune therapeutic effect has not been well studied. METHODS: We constructed a diagnostic and prognostic model for LUAD based on B and T cells homeostasis-related genes. Minimum absolute contraction and selection operator (LASSO) analysis and multivariate Cox regression are used to identify the prognostic gene signatures. Based on the overall survival time and survival status of LUAD patients, a 10-gene prognostic model composed of ABL1, BAK1, IKBKB, PPP2R3C, CCNB2, CORO1A, FADD, P2RX7, TNFSF14, and ZC3H8 was subsequently identified as prognostic markers from The Cancer Genome Atlas (TCGA)-LUAD to develop a prognostic signature. This study constructed a gene prognosis model based on gene expression profiles and corresponding survival information through survival analysis, as well as 1-year, 3-year, and 5-year ROC curve analysis. Enrichment analysis attempted to reveal the potential mechanism of action and molecular pathway of prognostic genes. The CIBERSORT algorithm calculated the infiltration degree of 22 immune cells in each sample and compared the difference of immune cell infiltration between high-risk group and low-risk group. At the cellular level, PCR and CKK8 experiments were used to verify the differences in the expression of the constructed 10-gene model and its effects on cell viability, respectively. The experimental results supported the significant biological significance and potential application value of the molecular model in the prognosis of lung cancer. Enrichment analyses showed that these genes were mainly related to lymphocyte homeostasis. CONCLUSION: We identified a novel immune cell homeostasis prognostic signature. Targeting these immune cell homeostasis prognostic genes may be an alternative for LUAD treatment. The reliability of the prediction model was confirmed at bioinformatics level, cellular level, and gene level.