A Complement and Coagulation Cascade-Related Prognostic Signature Predicts Survival and Immune Landscape in Lung Adenocarcinoma.

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.

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.

Lung adenocarcinoma (LUAD) is a common malignant tumor with no obvious clinical symptoms in its early stages. Patients can be divided into radiotherapysensitive groups (RS) and radiotherapy-resistant groups (RR) due to their varying conditions. The therapeutic effect of radiotherapy is quite different between the two groups. Therefore, this paper explores the role of radiation-related lung function genes in LUAD and its immune landscape. Firstly, we divided LUAD samples from the TCGA cohort into RS and RR groups and analyzed differential expression to obtain differentially expressed genes (DEGs). Then, DEGs and patients' grouping information were input into the weighted co-expression network, and the genes in the radiotherapy-related modules were identified. Furthermore, after the intersection of DEGs and lung function-related genes, the prognosis-related genes were obtained through univariate Cox and Lasso-Cox analyses, respectively, and the risk model was constructed. Finally, the differences in prognosis and immunity of the samples in the risk model were explored. Additionally, we also performed a qPCR experiment on lung function-related genes. In this paper, radiation-related genes of LUAD were identified through a series of bioinformatics analyses. By conducting enrichment analysis on these genes, several pathways related to LUAD radiation were identified, and DEGs associated with significant prognosis were determined. Furthermore, a radiation-related risk model of LUAD was developed. All samples were divided into high-risk and low-risk groups based on the risk score, and the differences in immune cell infiltration abundance and immune function between these groups were evaluated. The qPCR experimental results demonstrated a significant difference in the expression of genes related to lung function. The prognosis-related genes identified in this paper and the risk model created can serve as a reference for diagnosing and treating LUAD.

Mitophagy and aging (MiAg) are very important pathophysiological mechanisms contributing to tumorigenesis. MiAg-related genes have prognostic value in lung adenocarcinoma (LUAD). However, prognostic, and immune correlation studies of MiAg-related genes in LUAD are lacking. MiAg differentially expressed genes (DEGs) in LUAD were obtained from public sequencing datasets. A prognostic model including MiAg DEGs was constructed according to patients divided into low- and high-risk groups. Gene Ontology, gene set enrichment analysis, gene set variation analysis, CIBERSORT immune infiltration analysis, and clinical characteristic correlation analyses were performed for functional annotation and correlation of MiAgs with prognosis in patients with LUAD. Seven MiAg DEGsof LUAD were identified: CAV1, DSG2, DSP, MYH11, NME1, PAICS, PLOD2, and the expression levels of these genes were significantly correlated (P < 0.05). TheRiskScoreof theMiAgDEGprognostic modeldemonstrated high predictive ability of overall survival of patients diagnosed with LUAD. Patients with high and low MiAg phenotypic scores exhibitedsignificant differences in the infiltration levels of eight types of immune cells (P < 0.05). The multi-factor DEG regression model showed higher efficacy in predicting 5-year survival than 3- and 1-year survival of patients with LUAD. Seven MiAg-related genes were identified to be significantly associated with the prognosis of patients diagnosed with LUAD. Moreover, the identified MiAg DEGs might affect the immunotherapy strategy of patients with LUAD.

Immune cells in the immune microenvironment of lung adenocarcinoma (LUAD) are involved in tumour progression. The aim of this study was to investigate the molecular mechanisms of immune infiltration-related genes in LUAD. The GEO, GeneCards, BioGPS and Genehopper databases were utilized to screen for immune infiltration-related differentially expressed genes (DEGs) in LUAD. Protein-protein interaction (PPI) network construction and survival analysis were performed in the Kaplan-Meier database to identify hub genes. The TIMER 2.0 database was used to analyse the correlations between hub gene expression and immune infiltration level. Co-culture of LUAD cells with macrophages and plasmid transfection to overexpress ANGPT1 were performed to investigate the function of the hub genes in LUAD using RT-qPCR, Western blot, CCK-8 assays, cell wound healing assays and transwell assays. A total of 88 immune infiltration-related DEGs were screened. The hub genes ANGPT1, CDH5 and CLDN5 were reduced in LUAD, while COL3A1 was overexpressed. ANGPT1 was significantly correlated with OS, FP and PPS, and ANGPT1 promoted the polarization of M1 macrophages. Further experiments revealed that ANGPT1 inhibited the proliferation, migration and invasion of LUAD cells by inhibiting the TGF-β signalling pathway. ANGPT1 promotes polarization of M1 macrophages and reduces the progression of LUAD by inhibiting the TGF-β signalling pathway. Thus, ANGPT1 could be employed as a predictive biomarker and immunotherapy target for lung cancer.

BACKGROUNDCurrently, there are many therapeutic methods for lung adenocarcinoma (LUAD), but the 5-year survival rate is still only 15% at later stages. Epithelial– mesenchymal transition (EMT) has been shown to be closely associated with local dissemination and subsequent metastasis of solid tumors. However, the role of EMT in the occurrence and development of LUAD remains unclear.AIMTo further elucidate the value of EMT-related genes in LUAD prognosis.METHODSUnivariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were applied to establish and validate a new EMT-related gene signature for predicting LUAD prognosis. The risk model was evaluated by Kaplan–Meier survival analysis, principal component analysis, and functional enrichment analysis and was used for nomogram construction. The potential structures of drugs to which LUAD is sensitive were discussed with respect to EMT-related genes in this model.RESULTSThirty-three differentially expressed genes related to EMT were found to be highly associated with overall survival (OS) by using univariate Cox regression analysis (log2FC ≥ 1, false discovery rate < 0.001). A prognostic signature of 7 EMT-associated genes was developed to divide patients into two risk groups by high or low risk scores. Kaplan–Meier survival analysis showed that the OS of patients in the high-risk group was significantly poorer than that of patients in the low-risk group (P < 0.05). Multivariate Cox regression analysis showed that the risk score was an independent risk factor for OS (HR > 1, P < 0.05). The results of receiver operator characteristic curve analysis suggested that the 7-gene signature had a perfect ability to predict prognosis (all area under the curves > 0.5).CONCLUSIONThe EMT-associated gene signature classifier could be used as a feasible indicator for predicting OS.

Identifying M1-like macrophage related genes for prognosis prediction in lung adenocarcinoma based on a gene co-expression network

Introduction: Lung adenocarcinoma (LUAD) is the deadliest cancer worldwide. Many efforts are made to search for new biomarkers to screen for immunotherapy candidates and predict the response. Neural Precursor Cell Expressed, developmentally down-Regulated 9 (NEDD9), is predominantly expressed in many cancer types but has not been studied yet in lung adenocarcinoma. Herein, we analyzed the association of NEDD9 with immune inhibitory components to understand its immune landscape in lung adenocarcinoma. Methods: Gene expression profiles were retrieved from The Cancer Genomic Atlas Lung Adenocarcinoma cohort (n = 567). We used TIMER 2.0 for immune inhibitory cell infiltrates analysis. Tumor mutational burden (TMB) was measured using the cBioPortal tool. The enrichment analysis of function and signaling pathways of DEGs in LUAD was done by gene ontology (GO) using Enrichr. Results: NEDD9 is differentially expressed in LUAD tissues (P &amp;lt; 0.00). High NEDD9 expression had better overall survival prognostic value in LUAD patients (HR: 0.66, 95% CI: 0.49 - 0.89, P = 0.0221) compared to low expression. NEDD9 expression was positively correlated with the infiltration of cancer-associated fibroblasts and macrophage M2 cells (spearman’s ρ = 0.257, P &amp;lt; 0.001, ρ = 0.266, P &amp;lt; 0.001, respectively), but negatively with myeloid-derived suppressor cells (MDSC) (ρ = -0.344, P &amp;lt; 0.001). Moreover, survival analysis based on NEDD9 expression and MDSC levels revealed a better OS group identified by low MDSC levels and low NEDD9 expression after adjustment for age, stage, and purity. In terms of immune checkpoint genes’ expression, NEDD9 expression was directly correlated with CTLA-4, PD-1, PD-L1, VSIR, and LAG-3 (ρ &amp;gt; 0.2, Q value &amp;lt; 0.05). TMB was inversely associated with NEDD9 expression (ρ = -0.1, P &amp;lt; 0.001). GO analysis showed that genes co-expressed with NEDD9 were mainly in the phagocytic vesicles and participated in biological processes of cellular response to interferon-gamma by molecular function such as cysteine-type endopeptidase activity involved in apoptotic signaling pathways. Conclusion: We found that NEDD9 expression is a useful biomarker of immunosuppression in LUAD patients and can predict the response to immunotherapy. Our results suggest that NEDD9 is a favorable prognostic biomarker, which could be explained by low MDSC. Further studies are needed for a better understanding of the NEDD9 value in LAUD. Citation Format: Leen M. Al-Kraimeen, Yaqeen M. Al-Kraimeen. NEDD9 as a prognostic biomarker and its immune landscape in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2214.

We attempted to screen out the feature genes associated with the prognosis of hepatocellular carcinoma (HCC) patients through bioinformatics methods, to generate a risk model to predict the survival rate of patients. Gene expression information of HCC was accessed from GEO database, and differentially expressed genes (DEGs) were obtained through the joint analysis of multi‐chip. Functional and pathway enrichment analyses of DEGs indicated that the enrichment was mainly displayed in biological processes such as nuclear division. Based on TCGA‐LIHC data set, univariate, LASSO, and multivariate Cox regression analyses were conducted on the DEGs. Then, 13 feature genes were screened for the risk model. Also, the hub genes were examined in our collected clinical samples and GEPIA database. The performance of the risk model was validated by Kaplan–Meier survival analysis and receiver operation characteristic (ROC) curves. While its universality was verified in GSE76427 and ICGC (LIRI‐JP) validation cohorts. Besides, through combining patients’ clinical features (age, gender, T staging, and stage) and risk scores, univariate and multivariate Cox regression analyses revealed that the risk score was an effective independent prognostic factor. Finally, a nomogram was implemented for 3‐year and 5‐year overall survival prediction of patients. Our findings aid precision prediction for prognosis of HCC patients.

The role of N7-methylguanosine(m7G)-related miRNAs in lung adenocarcinoma (LUAD) remains unclear. We used LUAD data from The Cancer Genome Atlas (TCGA) to establish a risk model based on the m7G-related miRNAs, and divided patients into high-risk or low-risk subgroups. A nomogram for predicting overall survival (OS) was then constructed based on the independent risk factors. In addition, we performed a functional enrichment analysis and defined the oxidative stress-related genes, immune landscape as well as a drug response profile in the high-risk and low-risk subgroups. This study incorporated 28 m7G-related miRNAs into the risk model. The data showed a significant difference in the OS between the high-risk and low-risk subgroups. The receiver operating characteristic curve (ROC) predicted that the area under the curve (AUC) of one-year, three-year and five-year OS was 0.781, 0.804 and 0.853, respectively. The C-index of the prognostic nomogram for predicting OS was 0.739. We then analyzed the oxidative stress-related genes and immune landscape in the high-risk and low-risk subgroups. The data demonstrated significant differences in the expression of albumin (ALB), estimated score, immune score, stromal score, immune cell infiltration and functions between the high-risk and low-risk subgroups. In addition, the drug response analysis showed that low-risk subgroups may be more sensitive to tyrosine kinase inhibitor (TKI) and histone deacetylase (HDAC) inhibitors. We successfully developed a novel risk model based on m7G-related miRNAs in this study. The model can predict clinical prognosis and guide therapeutic regimens in patients with LUAD. Our data also provided new insights into the molecular mechanisms of m7G in LUAD.

背景与目的肺腺癌（lung adenocarcinoma, LUAD）靶向治疗中酪氨酸激酶抑制剂（tyrosine kinase inhibitors, TKIs）耐药问题突出，亟需筛选与耐药及预后相关的关键分子标志物以指导精准治疗。本研究旨在探究LUAD TKIs耐药的分子机制，筛选核心差异表达基因（differentially expressed genes, DEGs），明确不同基因聚类与患者生存、药物反应的关联，构建并验证LUAD预后预测的风险模型，为LUAD精准治疗与预后评估提供依据。方法整合GSE162045、GSE114647等多个LUAD相关数据集，通过韦恩图筛选核心重叠DEGs并构建基因相关性网络。采用共识聚类法对样本进行分组，结合t-SNE降维可视化验证聚类稳定性与区分度。运用京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）与基因集富集分析（Gene Set Enrichment Analysis, GSEA）探究DEGs功能。比较不同聚类中12种药物的半抑制浓度（50% maximal inhibitory concentration, IC50）值，评估药物敏感性差异。通过LASSO回归筛选预后相关核心基因构建风险模型，并在GSE31210队列中通过桑基图、Kaplan-Meier生存曲线、受试者工作特征（reciever operating characteristic, ROC）曲线验证模型效能。分析关键基因在不同聚类及风险组间的表达差异，绘制单基因表达与生存关联的Kaplan-Meier曲线。基于多个数据集（GSE19804、GSE19188、GSE44077、GSE30219）分析PLEK2在LUAD组织中的表达，并通过Western blot检测其在表皮生长因子受体（epidermal growth factor receptor, EGFR）-TKIs耐药细胞系中的蛋白水平。结果筛选出12个核心DEGs（如HMGA1、PLEK2等）；当聚类数（K值）为2时样本稳定分为Cluster A和Cluster B，10个核心基因在两组中表达差异显著（P<0.0001），且Cluster A患者总生存期（overall survival, OS）、无病生存期（disease-free survival, DFS）、无进展生存期（progression-free survival, PFS）均显著优于Cluster B。两组在TP53、KRAS、EGFR等高频基因突变类型上存在明显差异，KEGG富集分析显示差异基因主要富集于“细胞周期”“神经活性配体-受体相互作用”等通路。GSEA提示Cluster B与肿瘤恶性进展相关基因集显著关联。药物敏感性分析显示两聚类对10种药物的IC50值存在显著差异。成功构建基于9个基因的风险模型，高风险组患者死亡比例更高、生存率更低（P<0.0001），模型在1、3、5年的曲线下面积（area under the area, AUC）分别为0.700、0.647、0.675，GSE31210队列验证显示模型具有良好稳定性与通用性。关键基因在风险组间表达差异显著（P<0.0001），其中HMGA1、PLEK2高表达提示预后不良，而ID3、DAPK2与预后无关。将临床变量与LASSO风险评分纳入分析，单因素Cox分析显示风险评分与OS显著关联（HR=0.49, P=3.80×10-6）；多因素校正后，风险评分仍为独立预后因素（HR=0.57, P=6.40×10-4），具有稳定独立预测价值。公共数据集分析及Western blot实验均证实，PLEK2在LUAD组织中表达上调，且在EGFR-TKIs耐药细胞系中表达进一步升高。结论本研究构建的风险模型可有效预测LUAD患者的预后，其中PLEK2在LUAD中高表达且与EGFR-TKIs耐药有关，可能成为潜在的预后标志物和治疗靶点。

BackgroundAs the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD).MethodsFirst, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan–Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation.ResultsLUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into high- and low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan–Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells.ConclusionsWe developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens.

Unveiling shared PANoptosis mechanisms in LUAD and osteoarthritis via bioinformatics and machine learning

Despite the recent advancements in the treatment of cancer, the 5-year survival of patients with non-small cell lung cancer (NSCLC) remains unsatisfactory. Lung adenocarcinoma (LUAD) is NSCLC's most common subtype, and metastasis is the major cause of death in patients with cancer. Therefore, identifying novel targets associated with metastasis in NSCLC is crucial to improving treatment. This study aimed to characterize the expression of GNGT1 in LUAD and to clarify the mechanism underlying the association between the higher expression level of GNGT1 and worse prognosis in patients. The transcriptome datasets and clinical information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Bioinformatics analyses were performed in 515 patients who were stratified into two groups (high- and low-GNGT1 expression group) according to the GNGT1 level. Overall survival, DNA promotor methylation, immune cell infiltration, gene set enrichment analysis (GSEA), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to elucidate the functions of GNGT1 and to identify the related hub genes in LUAD. Their expression and functions in LUAD were verified using tissues from patients and transgenic mice overexpressing GNGT1 under the control of a lung-specific promoter (Scgb1a1-Cre). GNGT1 was overexpressed in patients with LUAD and was associated with poor prognosis. GNGT1 expression was significantly correlated with gene alteration and hypomethylated promoter status. High GNGT1 expression in patients with LUAD was associated with advanced lymph node metastasis and the degree of immune cell infiltration. Functional enrichment analyses indicated that differentially expressed genes (DEGs) in the high-GNGT1 group participated in DNA replication, DNA replication preinitiation, and M phase, while cell adhesion molecules, apoptosis, and natural killer cell-mediated cytotoxicity were all downregulated. Messenger RNA and protein levels were correspondingly regulated in human LUAD tissues and the Scgb1a1-Cre; LSL-GNGT1 mouse model (GNGT1fl/+ mice). GNGT1 was associated with tumor cell proliferation via the enhancement of tumor cell stemness and interaction with driver genes. Elevated GNGT1 expression promoted epithelial-mesenchymal transformation, remodeled the tumor microenvironment, and led to tumor metastasis, ultimately worsening the survival-related prognosis of patients with LUAD.

Therapeutic and prognostic effect of disulfidptosis-related genes in lung adenocarcinoma