In recent years, as more and more lung-cancer patients have been treated with immunotherapeutic agents, their survival has been prolonged compared to before. It is well known that BTK (Bruton\'s tyrosine kinase) is predominantly found in cells of the hematopoietic system. However, there is a distinct lack of literature on BTK expression in lung adenocarcinoma (LUAD) patients and its effect on the immune microenvironment. Consequently, the main goal of this investigation was to analyze how BTK expression in lung adenocarcinoma affects its progression, along with its prognostic significance, through the utilization of bioinformatics online resources and publicly available databases. Data on the sequencing results and clinical records of lung adenocarcinoma patients were gathered from The Cancer Genome Atlas (TCGA) database. Based on the expression level of BKT, TCGA categorized lung adenocarcinoma patients into BTK high-expression and low-expression groups. We investigated the effects of BKT on clinicopathologic, genomic, and immunologic characteristics of lung adenocarcinoma patients. We analyzed BTK mRNA expression in tumors and normal tissues using two key resources: Tumor Immuno Estimation Resource 2.0 (TIMER 2.0) and Gene Expression Profiling Interactive Analysis 2 (GEPIA 2). We analyzed the prognosis of the patients using GEPIA2 and validated the results using univariate and multivariate analyses. In addition, we assessed BTK protein expression by Human Protein Atlas (HPA). We sought to elucidate the clinical prognostic significance of BTK in The TCGA using the online tool GEPIA 2. Furthermore, to clarify the biologic roles and pathways linked to BTK, we conducted a genomic enrichment analysis of the information. To predict the proportion of various immune cell infiltrations in the immune microenvironment of lung adenocarcinoma patients diagnosed in the TCGA database, we performed an analysis using the TIMER online tool. Using TIMER and CIBERSORT, the correlation between genes co-expressed with BTK and the corresponding tumor-infiltrating immune cells was explored; finally, the relationship between BTK expression and immune infiltration and immune checkpoints in the TMB group and the high and low groups was analyzed by R language analysis using the TCGA database. The expression of BTK provides some hints about the prognosis of the patients. The high expression of BTK is involved in immune response regulation signaling pathways, leukocyte-mediated immunity, leukocyte intercellular adhesion, graft rejection, and complement. Analysis of the GEPIA 2 database showed that BTK was co-expressed with the genes FGD2, SASH3, NCKAP1L, CD53, ARHGAP30 and LPXN. Increased expression of the above-mentioned genes resulted in increased proportions of CD8 + T cells, memory CD4 + T cells, B cells, macrophages, and dendritic cells, and decreased proportions of Treg cells and TH2 cells. In addition, our study revealed a strong positive correlation between various key immune checkpoints (e.g., PDCD1, CD274, PDCD1LG2, CTLA4, HAVCR2, LAG3, TIGIT, and SIGLEC15) and BTK expression. In conclusion, increased BTK expression in lung adenocarcinoma is closely associated with prolonged survival of lung-cancer patients. Moreover, the genes classified under the BTK high-expression group exhibit significant enrichment in immune-related pathways, suggesting a potential impact on the tumor microenvironment. We investigated the potential of BTK as a tumor suppressor gene in predicting prolonged patient survival. In addition, we further investigated the possibility that BTK further affects the immunotherapeutic response of patients by influencing the microenvironment of tumor immune infiltration, but the relevant mechanisms remain to be further studied.

Molecular targeted therapy resistance remains a major challenge in treating lung adenocarcinoma (LUAD). The resistance of Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs, epidermal growth factor receptor-tyrosine kinase inhibitor) plays a dominant role in molecular targeted therapy. Our previous research demonstrated the role of MALAT-1 (Metastasis-associated lung adenocarcinoma transcript 1) in the formation of Erlotinib-resistant LUAD cells. This study aims to uncover the mechanism of MALAT-1 overexpression in Erlotinib-resistant LUAD cells. The RT2 LncRNA PCR array system was used to explore MALAT-1 regulation in Erlotinib-resistant LUAD cells through patient serum analysis. Dual luciferase reporter experiments confirmed the binding between MALAT-1 and miR-125, leading to regulation of miR-125 expression. Functional assays were performed to elucidate the impact of MALAT1 on modulating drug resistance, growth, and Epithelial-mesenchymal transition (EMT, Epithelial-mesenchymal transition) in both parental and Erlotinib-resistant LUAD cells. The investigation unveiled the mechanism underlying the competing endogenous RNA (ceRNA, competing endogenouse RNA) pathway. MALAT1 exerted its regulatory effect on miR-125 as a competing endogenous RNA (ceRNA). Moreover, MALAT1 played a role in modulating the sensitivity of LUAD cells to Erlotinib. Rab25 was identified as the direct target of miR-125 and mediated the functional effects of MALAT1 in Erlotinib-resistant LUAD cells. In conclusion, our study reveals overexpress MALAT-1 cause the drug resistance of EGFR-TKIs in non-small cell lung cancer (NSCLC) through the MALAT-1/miR-125/Rab25 axis. These findings present a potential novel therapeutic target and perspective for the treatment of LUAD.

UNASSIGNED: Lung cancer is one of the malignant tumors with the highest rates of morbidity and mortality worldwide. One of the most common histological types of lung cancer is lung adenocarcinoma (LUAD). Despite the fact that development in medicine has significantly improved some patients\' prognoses, the overall survival (OS) rate is still very low. In glucose-deficient SLC7A11-overexpressed cancer cells, the accumulation of disulfide molecules leads to abnormal disulfide bonding between actin cytoskeletal proteins, interferes with their tissues, and eventually leads to actin network collapse and cell death. This mode of cell death is called disulfidptosis. Studies have shown that disulfidptosis may be a new target for cancer treatment. However, the role of disulfidptosis in LUAD is still unknown.
UNASSIGNED: LUAD transcriptome and clinical information from The Cancer Genome Atlas (TCGA) was downloaded. The co-expression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Cox regression analysis was performed to screen the disulfidptosis-related lncRNAs (DRLs) and build the prognostic model. Kaplan-Meier curve, Cox regression analysis, and receiver operating characteristic (ROC) curve was used to validate the model. Then a nomogram is made to predict the prognosis of LUAD patients. Finally, fresh-collected clinical samples were used to verify the expression of DRLs in LUAD.
UNASSIGNED: The prognostic model with six DRLs was developed to predict the prognosis of LUAD, with superior prognosis value compared to other clinical variables. The Cox regression analysis revealed that T stage, N stage and the risk score were identified as independent variables that affected LUAD prognosis. ROC curve revealed that the model has a moderate diagnostic value, with an AUC of 1-year 0.684, 3-year 0.664, and 5-year 0.588. Moreover, nine medications connected to LUAD treatment were acquired through drug sensitivity analysis. LUAD tissue validation showed that AC012073.1, AC012615.1, EMSLR, and SNHG12 were highly expressed, while AL606834.1 and AL365181.2 with low expression.
UNASSIGNED: Six DRLs were screened and verified to construct the prognostic model, which can accurately predict the LUAD prognosis. It establishes a basis for further exploration into the molecular mechanisms underlying LUAD and identification of potential biomarkers for diagnosis, prognosis, and therapeutic targets.

UNASSIGNED: Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response.
UNASSIGNED: By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts.
UNASSIGNED: RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups.
UNASSIGNED: In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.

UNASSIGNED: Introduction: Circular RNAs (circRNAs) have been identified as significant contributors to the development and advancement of cancer. The objective of this study was to examine the expression and clinical implications of circRNA circ_BBS9 in lung adenocarcinoma (LUAD), as well as its potential modes of action.
UNASSIGNED: The expression of Circ_BBS9 was examined in tissues and cell lines of LUAD through the utilization of microarray profiling, quantitative real-time polymerase chain reaction (qRT-PCR), and western blot analysis. In this study, we assessed the impact of circ_BBS9 on the proliferation of LUAD cells, as well as its influence on ferroptosis and tumor formation. To analyze these effects, we employed CCK-8 assays and ferroptosis assays. The identification of proteins that interact with Circ_BBS9 was achieved through the utilization of RNA pull-down and mass spectrometry techniques. A putative regulatory network comprising circ_BBS9, miR-7150, and IFIT3 was established using bioinformatics study. The investigation also encompassed the examination of the correlation between the expression of IFIT3 and the invasion of immune cells.
UNASSIGNED: Circ_BBS9 was significantly downregulated in LUAD tissues and cell lines. Low circ_BBS9 expression correlated with poor prognosis. Functional experiments showed that circ_BBS9 overexpression inhibited LUAD cell proliferation and promoted ferroptosis in vitro and suppressed tumor growth in vivo. Mechanistically, circ_BBS9 was found to directly interact with IFIT3 and regulate its expression by acting as a sponge for miR-7150. Additionally, IFIT3 expression correlated positively with immune infiltration in LUAD.
UNASSIGNED: Circ_BBS9 has been identified as a tumor suppressor in lung adenocarcinoma (LUAD) and holds promise as a diagnostic biomarker. The potential mechanism of action involves the modulation of ferroptosis and the immunological microenvironment through direct interaction with IFIT3 and competitive binding to miR-7150. The aforementioned findings offer new perspectives on the pathophysiology of LUAD and highlight circ_BBS9 as a potentially valuable target for therapeutic interventions.

UNASSIGNED: Natural Killer (NK) cells are vital components of the innate immune system, crucial for combating infections and tumor growth, making them pivotal in cancer prognosis and immunotherapy. We sought to understand the diverse characteristics of NK cells within lung adenocarcinoma (LUAD) by conducting single-cell RNA sequencing analyses.
UNASSIGNED: Using the scRNA-seq dataset for multiple primary lung cancers (MPLCs), we examined two major NK cell groups, NK1 and NK2, comparing the expression profiles of 422 differentially expressed NK signature genes. We identified eight genes (SPON2, PLEKHG3, CAMK2N1, RAB27B, CTBP2, EFHD2, GOLM1, and PLOD1) that distinguish NK1 from NK2 cells. A prognostic signature, the NK gene signature (NKGS) score, was established through LASSO Cox regression. High NKGS scores were linked to poorer overall survival in TCGA-LUAD patients and consistently validated in other datasets (GSE31210 and GSE14814).
UNASSIGNED: Functional analysis revealed an enrichment of genes related to the TGF-β signaling pathway in the high NKGS score group. Moreover, a high NKGS score correlated with an immunosuppressive tumor microenvironment (TME) driven by immune evasion mechanisms. We also observed reduced T-cell receptor (TCR) repertoire diversity in the high-risk NKGS group, indicating a negative association between inflammation and risk score.
UNASSIGNED: This study introduced the innovative NKGS score, differentiating NK1 from NK2 cells. High NKGS scores were associated with the TGF-β pathway and provided insights into LUAD prognosis and immune activities.

UNASSIGNED: Lung adenocarcinoma is a malignancy characterized by high mortality rates and unfavorable prognosis. However, the role of Leukotriene C4 Synthase (LTC4S) in lung cancer remains uninvestigated.
UNASSIGNED: The expression and prognostic value of LTC4S in LUAD were analyzed using the GEPIA online database. Subsequently, the function of LTC4S in lung cancer cells was examined through gain-of function experiments, using assays to evaluate tumor malignant behavior. Subcutaneous xenograft experiments in vivo was used for investigating the functions of LTC4S. Then, tumor hallmark pathways were analyzed by GSEA. Western blot assay was used to validate the impact of LTC4S on mTORC1 pathway. Finally, the correlation of mRNA and methylation of LTC4S were analyzed by cBioPortal. qRT-PCR, ChIP-qPCR and ChIP-Atlas were used to verify the regulation factors of LTC4S low expression in LUAD cells.
UNASSIGNED: LTC4S presented significant decreased expression and favorable prognostic significance in LUAD. LTC4S was correlated with clinical stages in LUAD, which showed decreased expression gradually and significantly along with TNM stages. LTC4S-co-expressed genes were closely related to Ras signaling pathway, and MAPK signaling pathway. Overexpression of LTC4S inhibited cancer malignant phenotype and tumor growth in vitro and vivo. GSEA analysis and Western blot assay suggested low expression of LTC4S activated mTORC1 signaling pathway in LUAD. Moreover, the DNA methylation level of LTC4S in LUAD tissue was markedly elevated compared to normal tissue. The hypermethylation of the LTC4S promoter by DNMT3A leads to the decreased expression of LTC4S in LUAD.
UNASSIGNED: In conclusion, low expression of LTC4S serves as an unfavorable prognostic marker and the critical function of LTC4S in controlling the progression of LUAD. This highlights the promise for exploring the clinical benefits of manipulating LTC4S in LUAD targeted therapies.

UNASSIGNED: Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted.
UNASSIGNED: RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219.
UNASSIGNED: Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue.
UNASSIGNED: We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

UNASSIGNED: Lung cancer is the most common primary malignant tumor of the lung, and as one of the malignant tumors that pose the greatest threat to the health of the population, the incidence rate has remained high in recent years. Previous studies have shown that KLRB1 is transcriptionally repressed in lung adenocarcinoma and correlates with lung adenocarcinoma prognosis. The objective of this study is to investigate the intrinsic mechanisms by which KLRB1 affects the malignant phenotypes of lung adenocarcinoma such as immune infiltration, proliferation, growth and metastasis.
UNASSIGNED: We assessed the expression levels of KLRB1 in publicly available databases and investigated its associations with clinical and pathological variables. Enrichment analysis was subsequently conducted to investigate possible signaling pathways and their associated biological functions. Statistical analysis, including Spearman correlation and the application of multigene prediction models, was utilized to assess the relationship between the expression of KLRB1 and the infiltration of immune cells. The diagnostic and prognostic value of KLRB1 was evaluated using Kaplan-Meier survival curves, diagnostic receptor operating characteristic (ROC) curves, histogram models, and Cox regression analysis. Specimens from lung adenocarcinoma (LUAD) patients were collected, the expression level of KLRB1 was detected by protein blotting analysis, and the expression level of KLRB1 was detected at the mRNA level by real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR). Small interfering RNA (siRNA) was used to silence gene expression, and Transwell, Cell Counting Kit-8 (CCK-8) and colony formation assays were subsequently performed to analyze the effects of KLRB1 on LUAD cell migration, invasion and proliferation.
UNASSIGNED: KLRB1 expression was lower in lung cancer tissue than in surrounding healthy tissue. Genes differentially expressed in the low and high KLRB1 expression groups were found to be significantly enriched in pathways related to immunity. KLRB1 exerted an impact on the MAPK/ERK signaling pathway, thereby modulating the growth and proliferation of LUAD cells. KLRB1 expression is linked to prognosis, immune infiltration, and cell migration and proliferation in LUAD.
UNASSIGNED: The evidence revealed a correlation between KLRB1 and both prognosis and immune infiltration in LUAD patients.

UNASSIGNED: Driver genes are essential predictors of targeted therapeutic efficacy. Detecting driver gene mutations in lung adenocarcinoma (LUAD) patients can help to screen for targeted drugs and improve patient survival benefits. This study aims to investigate the mutation characterization of driver genes and their correlation with clinicopathological features in LUAD.
UNASSIGNED: A total of 440 LUAD patients were selected from Sir Run Run Shaw Hospital between July 2019 and September 2022. Postoperative tissue specimens were analyzed for gene mutations using next-generation sequencing technology, focusing, including epidermal growth factor receptor EGFR, ALK, ROS1, RET, KRAS, MET, BRAF, HER2, PIK3CA and NRAS. At the same time, clinicopathological data were collected and organized for multidimensional correlation analysis.
UNASSIGNED: Of 440 LUAD patients, driver gene mutations were not detected in 48 patients. The proportion of patients with driver gene mutations was as high as 89.09%. The top three driver genetic mutations were EGFR, KRAS, and MET. Sixty-nine types of EGFR mutations were detected and distributed in the protein tyrosine kinase catalytic domain (56, 81.16%), Furin-like cysteine-rich region (9, 13.04%), receptor binding domain (3, 4.35%), and EGFR transmembrane domain (1, 1.45%). Single gene locus mutation occurred in 343 LUAD patients, but the mutation gene types covered all tested genes. Our findings showed that EGFR mutations were more commonly observed in non-smoking and female patients (P<0.01), KRAS mutations were more prevalent in male patients and smokers (P<0.01), ROS1 mutations had larger tumor diameters (P<0.01) and RET mutations were more prevalent in smokers (P<0.05).
UNASSIGNED: LUAD patients exhibit diverse genetic mutations, which may co-occur simultaneously. Integrated analysis of multiple mutations is essential for accurate diagnosis and effective treatment of the disease. The use of NGS can significantly expand our understanding of gene mutations and facilitate integrated analysis of multiple gene mutations, providing critical evidence for targeted treatment methods.