An Epithelial-Mesenchymal Transition-Based Prognostic Model for Survival Prediction in Lung Adenocarcinoma: COL5A2 and ZEB2.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

The coagulation process and infiltration of macrophages affect the progression and prognosis of lung adenocarcinoma (LUAD) patients. This study was designed to explore novel classification methods that better guide the precise treatment of LUAD patients on the basis of coagulation and macrophages. Weighted gene coexpression network analysis (WGCNA) was applied to identify M2 macrophage-related genes, and TAM marker genes were acquired through the analysis of scRNA-seq data. The MSigDB and KEGG databases were used to obtain coagulation-associated genes. The intersecting genes were defined as coagulation and macrophage-related (COMAR) genes. Unsupervised clustering analysis was used to evaluate distinct COMAR patterns for LUAD patients on the basis of the COMAR genes. The R package "limma" was used to identify differentially expressed genes (DEGs) between COMAR patterns. A prognostic risk score model, which was validated through external data cohorts and clinical samples, was constructed on the basis of the COMAR DEGs. In total, 33 COMAR genes were obtained, and three COMAR LUAD subtypes were identified on the basis of the 33 COMAR genes. There were 341 DEGs identified between the three COMAR subtypes, and 60 prognostic genes were selected for constructing the COMAR risk score model. Finally, 15 prognosis-associated genes (CORO1A, EPHA4, FOXM1, HLF, IFIH1, KYNU, LY6D, MUC16, PPARG, S100A8, SPINK1, SPINK5, SPP1, VSIG4, and XIST) were included in the model, which was efficient and robust in predicting LUAD patient prognosis and clinical outcomes in patients receiving anti-PD-1/PD-L1 immunotherapy. LUAD can be classified into three subtypes according to COMAR genes, which may provide guidance for precise treatment.

Although the use of osimertinib can significantly improve the survival time of lung adenocarcinoma (LUAD) patients with epithelial growth factor receptor mutation, eventually drug resistance will limit the survival benefit of most patients. This study aimed to develop a novel prognostic predictive signature based on genes associated with osimertinib resistance. The differentially expressed genes (DEGs) associated with osimertinib resistance in LUAD were screened from Gene Expression Omnibus datasets and The Cancer Genome Atlas datasets. Multivariate cox regression was used to establish a prognostic signature, and then a nomogram was developed to predict the survival probability of LUAD patients. We used ROC curve and DCA curve to evaluate its clinical prediction accuracy and net benefit.In addition, the differentially expressed genes significantly associated with prognosis were selected for immune infiltration analysis and drug sensitivity analysis, and their roles in the progression of lung adenocarcinoma were verified by in vitro experiments. Our evaluation results indicated that the new nomogram had higher clinical prediction accuracy and net benefit value than the TN nomogram. Further analysis showed that patients with low STRIP2 expression had a higher level of immune response, and may be more likely to benefit from immune checkpoint inhibitors and conventional antitumor drugs. This may help to select more precise and appropriate therapy for LUAD patients with osimertinib resistance. Furthermore, in vitro experiments showed that STRIP2 promoted the LUAD cells proliferation, migration and invasion. This further demonstrates the importance of this gene signature for prognostic prediction. We developed a reliable prognostic model based on DEGs associated with osimertinib resistance and screened for biomarker that can predict the immune response in LUAD patients, which may help in the selection of treatment regimens after osimertinib resistance.

Tumor migration in lung adenocarcinoma (LUAD) contributes to a poor prognosis by allowing malignant cells to escape the localized effects of radiotherapy, diminishing its overall efficacy. This study investigated the role of SET8, a methyltransferase, in LUAD migration and radiotherapy. In vitro experiments, including CRISPR/Cas9-mediated SET8 knockout, wound healing assays, and transwell migration assays, were used to assess the impact of SET8 on radiation-induced migration in LUAD cells. Bioinformatics analyses, such as differential expression analysis, clustering, functional enrichment, and CpG island methylation analysis, were performed using LUAD patient data from TCGA to examine the broader relationship between SET8, LUAD migration, and prognosis. Statistical methods, including Cox regression and LASSO regression, were employed to establish a prognostic model for radiotherapy outcomes, and drug sensitivity analysis was used to identify potential therapeutic agents. Ionizing radiation induced migration in LUAD cells, coupled with altered SET8 expression. SET8 was found to engage in IR-induced migration through the PTTG1-PI3K-AKT signaling axis. Furthermore, elevated SET8 expression was more prevalent in LUAD patients with metastasis and correlated with adverse prognosis. Under equivalent X-ray irradiation doses, SET8 depletion significantly inhibited the migratory capability of LUAD cells. Finally, SET8-associated migration genes could predict the survival rate, radiation responsiveness, and drug sensitivity of radiotherapy patients. SET8 facilitates radiation-induced migration in LUAD through the PTTG1-PI3K-AKT pathway, and SET8-associated genes may act as valuable markers for predicting radiotherapeutic efficacy in LUAD patients.

BackgroundDepression plays a crucial role in lung adenocarcinoma (LUAD) occurrence, progression, and prognosis. However, the impact of depression-related genes (DRGs) on the prognosis of LUAD patients is unclear. Thus, a prognosis prediction model was constructed to assess the potential impact of depression on LUAD prognosis.MethodsThe gene expression profiles from The Cancer Genome Atlas (TCGA)-LUAD and GSE68465 were collected for model training and validation. By identifying the intersection of DRGs and differentially expressed genes (DEGs) in LUAD, a risk score model was constructed to stratify patient risk based on univariate and multivariate analyses. The immune infiltration status and therapeutic potential of different risk groups were further explored. The correlation between key genes and clinical outcomes was evaluated in Kaplan-Meier (KM) analysis. Finally, the expression and mechanism of key genes were verified by in vitro experiments.ResultsWe identified 2,222 DEGs and 385 DRGs-DEGs, and DRGs-DEGs were closely related to nervous system function and cell signaling. Nine DRGs-DEGs were identified to construct the risk score model for risk stratification. The model’s predictive accuracy for patient survival was confirmed by receiver operating characteristic (ROC) curve analysis. LUAD patients with high-risk had significantly higher levels of CD8 T cells, B cells memory, and macrophages M1, which may affect the prognosis of LUAD patients. Furthermore, low-risk patients responded better to immunotherapy. KM analysis revealed that ACSS3 was significantly associated with poor prognosis in LUAD patients. oe-ACSS3 inhibits LUAD cell proliferation, migration, and invasion, and also promotes apoptosis.ConclusionsThe nine-gene risk score model proposed in our study demonstrated promising prognostic performance, highlighting the significant role of depression in LUAD prognosis. ACSS3 was demonstrated to play a critical role in regulating LUAD progression and may be a potential therapeutic target for LUAD treatment.

Objective: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor. Tumor mutations and the immune microenvironment play important roles in LUAD development and progression. This study was aimed at elucidating the characteristics of patients with different tumor immune microenvironment and establishing a prediction model of prognoses and immunotherapy benefits for patients with LUAD.Materials and Methods: We conducted a bioinformatics analysis on data from The Cancer Genome Atlas and Gene Expression Omnibus (training and test sets, respectively). Patients in the training set were clustered into different immunophenotypes based on tumor-infiltrating immune cells (TIICs). The immunophenotypic differentially expressed genes (IDEGs) were used to develop a prognostic risk score (PRS) model. Then, the model was validated in the test set and applied to evaluate 42 surgery patients with early LUAD.Results: Patients in the training set were clustered into high (Immunity_H), medium (Immunity_M), and low (Immunity_L) immunophenotype groups. Immunity_H patients had the best survival and more TIICs than Immunity_L patients. Immunity_M patients had the worst survival, characterized by most CD8+ T and Treg cells and highest expression of PD-1 and PD-L1. The PRS model, which consisted of 14 IDEGs, showed good potential for predicting the prognoses of patients in both training and test sets. In the training set, the low-risk patients had more TIICs, higher immunophenoscores (IPSs) and lower mutation rates of driver genes. The high-risk patients had more mutations of DNA mismatch repair deficiency and APOBEC (apolipoprotein B mRNA editing enzyme catalytic polypeptide-like). The model was also a good indicator of the curative effect for immunotherapy-treated patients. Furthermore, the low-risk group out of 42 patients, which was evaluated by the PRS model, had more TIICs, higher IPSs and better progression-free survival. Additionally, IPSs and PRSs of these patients were correlated with EGFR mutations.Conclusion: The PRS model has good potential for predicting the prognoses and immunotherapy benefits of LUAD patients. It may facilitate the diagnosis, risk stratification, and treatment decision-making for LUAD patients.

BackgroundLymph node metastasis is one of the important factors affecting the prognosis of lung adenocarcinoma (LUAD) patients. The key molecules in lymph node metastasis have not yet been fully revealed. Therefore, we aimed to construct a prognostic model based on lymph node metastasis-related genes to evaluate the prognosis of LUAD patients.MethodsThe differentially expressed genes (DEGs) in the process of LUAD metastasis were identified in The Cancer Genome Atlas (TCGA) database, and the biological roles of the DEGs were depicted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and a protein-protein interaction (PPI) network. Survival analysis and Cox regression analysis were used to identify the genes related to the prognosis of patients with LUAD, and a nomogram and a prognostic model were constructed. The potential prognostic value, immune escape, and regulatory mechanisms of the prognostic model in LUAD progression were explored through survival analysis and gene set enrichment analysis (GSEA).ResultsA total of 75 genes were upregulated, and 138 genes were downregulated in tissues of lymph node metastasis. The expression levels of STC1, CYP17A1, RHOV, GUCA2B, TM4SF20, DEFB1, CRHR2, ABCC2, CYP4B1, KRT16, and NTS were revealed as risk factors for a poor prognosis in LUAD patients. High-risk LUAD patients had a poor prognosis in the prognostic model based on RHOV, ABCC2, and CYP4B1. The clinical stage and the risk score were found to be independent risk factors for a poor prognosis in LUAD patients, and the risk score was associated with the tumor purity, T cell, natural killer (NK) cell, and other immune cells. The prognostic model might affect the progression of LUAD using DNA replication, the cell cycle, P53, and other signaling pathways.ConclusionsLymph node metastasis-related genes RHOV, ABCC2, and CYP4B1 are associated with a poor prognosis in LUAD. A prognostic model based on RHOV, ABCC2, and CYP4B1 might predict the prognosis of LUAD patients and be associated with immune infiltration.

Background Oxidative stress plays a role in carcinogenesis. This study explores the roles of oxidative stress-related genes (OSRGs) in lung adenocarcinoma (LAC). Besides, we construct a risk score model of OSRGs that evaluates the prognosis of LAC patients. Methods OSRGs were downloaded from the Gene Set Enrichment Analysis (GSEA) website. The expression levels of OSRGs were confirmed in LAC tissues of the TCGA database. GO and KEGG analyses were used to evaluate the roles and mechanisms of oxidative stress-related differentially expressed genes (DEGs). Survival, ROC, Cox analysis, and AIC method were used to screen the prognostic DEGs in LAC patients. Subsequently, we constructed a risk score model of OSRGs and a nomogram. Further, this work investigated the values of the risk score model in LAC progression and the relationship between the risk score model and immune infiltration. Results We discovered 163 oxidative stress-related DEGs in LAC, involving cellular response to oxidative stress and reactive oxygen species. Besides, the areas under the curve of CCNA2, CDC25C, ERO1A, CDK1, PLK1, ITGB4, and GJB2 were 0.970, 0.984, 0.984, 0.945, 0.984, 0.771, and 0.959, respectively. This indicates that these OSRGs have diagnosis values of LAC and are significantly related to the overall survival of LAC patients. ERO1A, CDC25C, and ITGB4 overexpressions were independent risk factors for the poor prognosis of LAC patients and were associated with risk scores in the risk model. High-risk score levels affected the poor prognosis of LAC patients. Notably, a high-risk score may be implicated in LAC progression via cell cycle, DNA replication, mismatch repair, and other mechanisms. Further, ERO1A, CDC25C, and ITGB4 expression levels were related to the immune infiltrating cells of LAC, including mast cells, NK cells, and CD8 T cells. Conclusion In summary, ERO1A, CDC25C, and ITGB4 of OSRGs are associated with poor prognosis of LAC patients. We confirmed that the risk model based on the ERO1A, CDC25C, and ITGB4 is expected to assess the prognosis of LAC patients.

Single-cell sequencing analysis and transcriptome analysis constructed the macrophage related gene-related signature in lung adenocarcinoma and verified by an independent cohort

lncRNA CERS6-AS1 upregulates the expression of ANLN by sponging miR-424-5p to promote the progression and drug resistance of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common subtype of non-small cell lung cancer (NSCLC) with a poor prognosis. To identify novel biomarkers and understand the underlying mechanisms in LUAD, we conducted a comprehensive analysis using single-cell and bulk RNA sequencing data. Our study focused on LAD1, a basement membrane filament protein that has been implicated in tumorigenesis in various cancers. We analyzed scRNA-seq data from 10 LUAD patients and identified nine cell subgroups, with LAD1 specifically expressed in cancer cells. Further analysis revealed significant correlations between LAD1 and genes associated with LUAD progression, including SFTPB, S100A6, CEACAM6, KRT19, S100A10, ANXA2, S100A11, and CAPN2. We investigated the effects of altered LAD1 expression on differentially expressed genes (DEGs), biological processes, and signaling pathways. Furthermore, we collected cancer tissue and corresponding adjacent tissue samples from 36 LUAD patients, used immunohistochemical staining to detect LAD1 expression, and analyzed its correlation with clinicopathological characteristics. We knocked down the expression of LAD1 in A549 cells using siRNA, and detected changes in LUAD cell migration and invasion ability through scratch healing assay and Transwell assay. Our results indicated diverse effects of LAD1 at both the single-cell and whole tumor levels, with a convergence on processes associated with tumor progression. In lung adenocarcinoma tissues, LAD1 was significantly upregulated, particularly in the cancer cell subgroup within tumors. Immunohistochemical results showed that LAD1 was highly expressed in LUAD, and knocking down LAD1 could inhibit LUAD cell migration and invasion. Pan-cancer analyses demonstrated LAD1 as an independent prognostic factor for overall survival in LUAD. Moreover, we developed a nomogram model incorporating LAD1 expression and clinical parameters, which demonstrated good predictive performance. The high expression of LAD1 in cancer cells, its associations with LUAD-related genes, and its links to biological processes and pathways suggest its potential biological relevance and that it merits further investigation. Overall, this study provides insights into LUAD and supports LAD1 as a gene worthy of further investigation.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-33277-z.

PurposeTP53 mutation, one of the most frequent mutations in early‐stage lung adenocarcinoma (LUAD), triggers a series of alterations in the immune landscape, progression, and clinical outcome of early‐stage LUAD. Our study was designed to unravel the effects of TP53 mutation on the immunophenotype of early‐stage LUAD and formulate a TP53‐associated immune prognostic model (IPM) that can estimate prognosis in early‐stage LUAD patients.Materials and methodsImmune‐associated differentially expressed genes (DEGs) between TP53 mutated (TP53MUT) and TP53 wild‐type (TP53WT) early‐stage LUAD were comprehensively analyzed. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis identified the prognostic immune‐associated DEGs. We constructed and validated an IPM based on the TCGA and a meta‐GEO composed of GSE72094, GSE42127, and GSE31210, respectively. The CIBERSORT algorithm was analyzed for assessing the percentage of immune cell types. A nomogram model was established for clinical application.ResultsTP53 mutation occurred in approximately 50.00% of LUAD patients, stimulating a weakened immune response in early‐stage LUAD. Sixty‐seven immune‐associated DEGs were determined between TP53WT and TP53MUT cohort. An IPM consisting of two prognostic immune‐associated DEGs (risk score = 0.098 * ENTPD2 expression + 0.168 * MIF expression) was developed through 397 cases in the TCGA and further validated based on 623 patients in a meta‐GEO. The IPM stratified patients into low or high risk of undesirable survival and was identified as an independent prognostic indicator in multivariate analysis (HR = 2.09, 95% CI: 1.43–3.06, p < 0.001). Increased expressions of PD‐L1, CTLA‐4, and TIGIT were revealed in the high‐risk group. Prognostic nomogram incorporating the IPM and other clinicopathological parameters (TNM stage and age) achieved optimal predictive accuracy and clinical utility.ConclusionThe IPM based on TP53 status is a reliable and robust immune signature to identify early‐stage LUAD patients with high risk of unfavorable survival.

Identification of different lung adenocarcinoma subtypes in combination with antidiuretic hormone-related genes and creation of an associated index to predict prognosis and guide immunotherapy.

Combination of tumor mutation burden and immune infiltrates for the prognosis of lung adenocarcinoma

Chronic polystyrene microplastics exposure promotes lung adenocarcinoma metastasis through EREG-regulated phosphorylation-dependent NF-κB activation