Prognosis Of Lung Adenocarcinoma Research Articles

Construction of a novel lipid drop-mitochondria-associated genetic profile for predicting the survival and prognosis of lung adenocarcinoma

Construction of a novel lipid drop-mitochondria-associated genetic profile for predicting the survival and prognosis of lung adenocarcinoma

Identification of Molecular Subtypes and Prognostic Traits Based on Chromosomal Instability Phenotype-Related Genes in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) exhibits significant molecular heterogeneity; however, previous studies have not fully explored its classification into distinct molecular subtypes. Here, we identified LUAD-significant chromosomal instability (CIN) phenotype genes (n = 24) using a TCGA-LUAD cohort (n = 592) and evaluated their ability to predict pathologic grade. Unsupervised clustering and principal component analysis revealed that LUAD patients could be classified into CIN phenotype-related subtypes (GroupLow, GroupModerate, and GroupHigh), each exhibiting distinct transcriptomic patterns. Notably, the GroupHigh showed significantly poor overall survival [OS; hazard ratio (HR) = 1.43, p-value < 10−3] and disease-free survival (DFS; HR = 1.27, p-value < 10−3). Univariate and multivariate analysis confirmed that its expression status was an independent prognostic predictor (p-value < 10−3, HR = 2.18, 95% C.I = 1.26–3.76) of the clinical outcomes, outperforming pathologic grade (p-value < 10−3, HR = 1.2, 95% C.I = 1.08–1.33). Moreover, analysis of surfactant metabolism-related genes revealed higher expression in the GroupLow, which was associated with a favorable prognosis. By integrating multiple independent cohorts (n = 779), we validated these findings and confirmed that CIN phenotype gene status serves as a critical prognostic marker in LUAD. Furthermore, genomic profiling showed that the GroupHigh exhibited frequent mutations in key genes such as KEAP1, LYST, SETD2, and TP53, with oncogenes in this group preferentially showing copy number gains. Our study highlights the significance of CIN phenotype gene status as a predictor of LUAD prognosis and its association with transcriptomic and genomic alterations, paving the way for further clinical validation and potential therapeutic interventions.

NLRP12/C1qA positive feedback in tumor-associated macrophages regulates immunosuppression through LILRB4/NF-κB pathway in lung adenocarcinoma.

The anti-tumor immune response is greatly hindered by the protumor polarization of tumor-associated macrophages (TAMs). Cancer-related inflammation plays a central role in TAMs protumor polarization. Our study explored the unique positive feedback loop between inflammasome and complement in TAMs. The present study identified NOD-like receptors family pyrin domain containing 12 (NLRP12) formed positive feedback with C1qA and drove TAMs protumor polarization via the LILRB4/NF-κB pathway. In addition, NLRP12 was predominantly expressed in TAMs and was associated with poorer prognosis in lung adenocarcinoma (LUAD) patients. Knocking down LILRB4 inhibited TAMs protumor polarization. NLRP12-overexpressing TAMs promoted tumor cells' malignant progression and inhibited T cells' proliferation and cytotoxic function. Lastly, NLRP12 knockout (NLRP12-/-) reversed macrophage polarization, enhanced T-cell anti-tumor immunity, and suppressed tumor growth. Our findings highlighted the essential role of NLRP12/C1qA positive feedback loop and the LILRB4/NF-κB pathway in promoting TAMs protumor polarization. Inhibition of NLRP12 suppressed tumor development and promoted immune response. NLRP12 may be a promising target for LUAD immunotherapy.

Establishment of potential lncRNA-related hub genes involved competitive endogenous RNA in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) have a notable role in the diagnosis and prognosis of cancer. However, the associations between lncRNA-related hub genes (LRHGs) expression and the corresponding outcomes have not been fully understood in lung adenocarcinoma (LUAD). Here, a total of 71 patients diagnosed with LUAD and 60 healthy volunteers at The First Affiliated Hospital of Huzhou University from April, 2023 to December, 2023 were enrolled in the present study. A LRHGs model was established using least absolute shrinkage and selection operator analyses of The Cancer Genome Atlas-LUAD datasets. The underlying mechanisms of the LRHGs were investigated via Gene Set Enrichment Analysis and Gene Set Variation Analysis. Additionally, the diagnostic role of serum HOXD cluster antisense RNA 2 (HOXD-AS2) was assessed by receiver operating characteristic (ROC) curve analysis. Lastly, TCGA-LUAD samples were divided into high- and low-HOXD-AS2 expression groups based on the median expression. The associations between HOXD-AS2 expression and miR-4538 as well as Calmodulin-Dependent Protein Kinase Type II subunit Beta (CAMK2B) levels were conducted through Pearson correlation analysis. A comprehensive analysis identified 141 differentially expressed lncRNAs between 539 LUAD tissues and 59 normal samples. A prognostic marker for overall survival was established by constructing a predictive signature consisting of 9 LRHGs. Subsequently, 474 LUAD samples were categorized into a high or low-risk group based on the median of the risk score. An independent prognostic model was constructed to confirm the validity of this categorization. Further comparisons of the clinicopathological features and LRHG-related pathways were performed between the two groups. Examinations of LRHG expression in two LUAD clusters and of the association between LRHG expression and immune infiltration were also conducted. HOXD-AS2 expression was shown to be elevated in LUAD tissues compared with matched normal tissues, and the serum HOXD-AS2 level was also notably increased in LUAD samples compared with healthy controls. The results of the ROC analysis indicated that the sensitivity and specificity of HOXD-AS2 were higher than that of cytokeratin-19 fragment (CYFRA21-1), which is a serum marker for LUAD. Pearson analyses indicated that miR-4538 level was negatively associated with HOXD-AS2 expression, but CAMK2B level showed positive correlation in LUAD. The results of the present study therefore indicated that the constructed LRHG model, particularly HOXD-AS2, could independently diagnose and predict the prognosis of LUAD, which suggested the underlying mechanism of the HOXD-AS2/miR-4538/CAMK2B, and might offer efficient strategies for LUAD treatment.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a metastasis-related gene signature for predicting prognosis in lung adenocarcinoma.

Metastasis has been documented as an independent and significant prognostic feature of lung adenocarcinoma (LUAD) patients. However, the underlying genetic and molecular mechanisms responsible for LUAD metastasis and their prognostic significance are not exactly defined. The single-cell transcriptomic profiles of primary and metastatic LUAD samples were integrated as a whole dataset. Enrichment analysis and pseudotime trajectory analysis were performed to illustrate the cellular origins and changes during the metastatic process. The LUAD metastasis-related genes (LMRGs) molecular cluster and signature was constructed through unsupervised consensus clustering and ten machine-learning algorithms in The Cancer Genome Atlas (TCGA) LUAD cohort using ten machine-learning algorithms. Validation of the signature was conducted using four independent cohorts from the Gene Expression Omnibus (GEO) database. Kaplan-Meier, ROC, univariate and multivariate Cox-regression analyses were performed to test the stability of the signature. The gene CCT6A was subjected to knockdown, followed by validation through western blot analysis, flow cytometry, wound healing and transwell-migration assays to determine its potential significance. First, the signaling pathway networks remodeling and metabolic reprogramming were demonstrated to be involved in the metastasis of malignant LUAD cells, which facilitate their extravasation and adaptation to other organs. Furthermore, distinct subtypes of malignant LUAD cells exhibit tissue-specific patterns. Then, two distinct molecular patterns of LMRGs were established, which showed diverse prognoses. A LUAD metastasis-related gene signature (LMRGS) was constructed via a multiple machine-learning-based integrative procedure, which possesses distinctly superior accuracy than most common clinical features and 69 published prognostic signatures. The patients stratified by the signature into high-risk group had a significantly poorer prognosis compared to those in the low-risk group, and this was well validated across different clinical subgroups. In addition, the risk score calculated by LMRGS remained an independent prognostic parameter in both univariate and multivariate Cox regression. Notably, knockdown of CCT6A gene promoted cell apoptosis and decelerated the cell migration obviously. LMRGS could serve as a novel and promising tool to improve clinical outcomes for individual LUAD patients.

Overexpression of CDCA8 predicts poor prognosis and drug insensitivity in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) accounts for the highest proportion of lung cancers; however, specific biomarkers are lacking for diagnosis, treatment, and prognostic assessment. Cell division cycle-associated 8 (CDCA8) is a cell cycle regulator with elevated expression in various cancers. However, the association between CDCA8 expression and LUAD prognosis remains unclear.MethodsThe association between CDCA8 and LUAD prognosis was evaluated based on the The Cancer Genome Atlas (TCGA) dataset, and CDCA8 related functions were determined using gene enrichment and gene ontology analyses. We also analyzed the association between CDCA8 expression and immune cell infiltration. Immunohistochemistry was used to determine the differential expression of CDCA8 in tumors and controls. Finally, we evaluated the differences in the sensitivity of different levels of CDCA8 to different anticancer drugs in LUAD.ResultsCDCA8 expression was significantly higher in primary LUAD tumors than in normal tissues (P < 0.001). Moreover, Kaplan–Meier survival analysis demonstrated that high CDCA8 expression predicted poor survival in patients with LUAD (P = 0.006). The receiver operating characteristic (ROC) curves indicated that CDCA8 was an effective guide for the diagnosis of LUAD. Functional annotation indicated that CDCA8 might be involved in functions such as p53 stabilization, nucleotide metabolism, RNA-mediated gene silencing, and the G2/M phase checkpoint. Immune infiltration results suggested that CDCA8 was positively correlated with Th2 cells and Tgd and negatively correlated with Eosinophils and Mast cells (P < 0.01). In addition, elevated expression of CDCA8 may increase the sensitivity of patients to certain anticancer drugs.ConclusionsCDCA8 upregulation is significantly associated with poor survival and immune infiltration in patients with LUAD. Our study suggests that CDCA8 can be used as a biomarker for LUAD prognosis and a reference for personalized medication.Graphical

Unveiling the distinctive variations in multi-omics triggered by TP53 mutation in lung cancer subtypes: An insight from interaction among intratumoral microbiota, tumor microenvironment, and pathology

The TP53 mutation is one of the most common gene mutations in non-small cell lung cancer (NSCLC) and plays a significant role in the occurrence, development, and prognosis of both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Recent studies have also suggested the predictive value of TP53 mutations in the response to immunotherapy for NSCLC. It is known that intratumoral microbiota, tumor immune microenvironment (TIME) and histology are associated with the roles of TP53 mutation in NSCLC. However, the intrinsic associations among these three factors and their underlying interaction with TP53 mutation are not well understood. Additionally, the potential of predicting TP53 mutations using deep learning methods has not yet been fully evaluated. In this paper, we comprehensively evaluated the tissue microbiome, host gene expression characteristics, and histopathological slides of 992 NSCLC patients obtained from the cancer genome atlas (TCGA) and validated the findings using multi-omics data of 332 NSCLC patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Compared to LUSC, LUAD exhibited more substantial differences between patients with and without TP53 mutation in all three aspects. In LUAD, our results imply underlying links between the tissue microbiome and immune cell components in the TIME, and show that the abundance of immune cells is reflected in histology slides. Furthermore, we propose a novel multimodal deep learning model that focuses on histopathology images, which achieves an area under the curve (AUC) of 0.84 in LUAD. In summary, TP53 mutation of LUAD resulted more significant changes in intratumoral microbiota, TIME and histology than that of LUSC. And histopathology images can be used to predict TP53 mutation in LUAD with reasonable accuracy.

Retraction Note: Lactate dehydrogenase D serves as a novel biomarker for prognosis and immune infiltration in lung adenocarcinoma

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s12885-023-11221-6.

Liquid-Liquid Phase Separation in the Prognosis of Lung Adenocarcinoma: An Integrated Analysis.

Lung adenocarcinoma (LUAD) is a highly lethal malignancy. Liquid-Liquid Phase Separation (LLPS) plays a crucial role in targeted therapies for lung cancer and in the progression of lung squamous cell carcinoma. However, the role of LLPS in the progression and prognosis of LUAD remains insufficiently explored. This study employed a multi-step approach to identify LLPS prognosis-related genes in LUAD. First, differential analysis, univariate Cox regression analysis, Random Survival For-est (RSF) method, and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regres-sion analysis were utilized to identify five LLPS prognosis-related genes. Subsequently, LASSO Cox regression was performed to establish a prognostic score termed the LLPS-related progno-sis score (LPRS). Comprehensive analyses were then conducted based on the LPRS, including survival analysis, clinical feature analysis, functional enrichment analysis, and tumor microenvi-ronment assessment. The LPRS was integrated with additional clinicopathological factors to de-velop a prognostic nomogram for LUAD patients. Immunohistochemical validation was per-formed on clinical tissue samples to further validate the findings. Finally, the relationship be-tween KRT6A, one of the identified genes, and epidermal growth factor receptor (EGFR) muta-tions was investigated. The LPRS was established using five LLPS-related genes: IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. Higher LPRS was closely associated with poor survival outcomes, gender, progression-free survival (PFS), and advanced TNM stage. Furthermore, LPRS emerged as an independent prognostic factor for LUAD. A nomogram integrating LPRS, TNM stage, and age demonstrated remarkable predictive accuracy for prognosis among patients with LUAD. LLPS likely influences LUAD prognosis through the activity of IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. KRT6A exhibits significant upregulation in LUAD, particularly in patients with EGFR mutations. This study introduces a novel LPRS model that demonstrates high accuracy in pre-dicting the clinical prognosis of LUAD. Moreover, the findings suggest that KRT6A may play a critical role in the LLPS-mediated malignant progression of LUAD.

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD), with a preliminary investigation into their clinical significance and underlying molecular mechanisms. Transcriptome data for SARS-CoV-2 infection and LUAD were sourced from public databases. Differentially expressed genes (DEGs) associated with SARS-CoV-2 infection were identified and subsequently overlapped with TCGA-LUAD DEGs to discern the crosstalk genes (CGs). In addition, CGs pertaining to both diseases were further refined using LUAD TCGA and GEO datasets. Univariate Cox regression was conducted to identify genes associated with LUAD prognosis, and these genes were subsequently incorporated into the construction of a prognosis signature using 10 different machine learning algorithms. Additional investigations, including tumor mutation burden assessment, TME landscape, immunotherapy response assessment, as well as analysis of sensitivity to antitumor drugs, were also undertaken. We discovered the risk stratification based on the prognostic signature revealed that the low-risk group demonstrated superior clinical outcomes (p < 0.001). Gene set enrichment analysis results predominantly exhibited enrichment in pathways related to cell cycle. Our analyses also indicated that the low-risk group displayed elevated levels of infiltration by immunocytes (p < 0.001) and superior immunotherapy response (p < 0.001). In our study, we reveal a close association between CGs and the immune microenvironment of LUAD. This provides preliminary insight for further exploring the mechanism and interaction between the two diseases.

Five-gene prognostic model based on autophagy-dependent cell death for predicting prognosis in lung adenocarcinoma

Non-small cell lung adenocarcinoma (LUAD) is the predominant form of lung cancer originating from lung epithelial cells, making it the most prevalent pathological type. Currently, reliable indicators for predicting treatment efficacy and disease prognosis are lacking. Despite extensive validation of autophagy-dependent cell death (ADCD) in solid tumor studies and its correlation with immunotherapy effectiveness and cancer prognosis, systematic research on ADCD-related genes in LUAD is limited. We utilized AddModuleScore, ssGSEA, and WGCNA to identify genes associated with ADCD across single-cell and bulk transcriptome datasets. The TCGA dataset, comprising 598 cases, was randomly divided into training and validation sets to develop an ADCD-related LUAD prediction model. Internal validation was performed using the TCGA validation set. For external validation, datasets GSE13213 (119 LUAD samples), GSE26939 (115 LUAD samples), GSE29016 (39 LUAD samples), and GSE30219 (86 LUAD samples) were employed. We evaluated the model’s accuracy and effectiveness in predicting prognostic risk. Additionally, CIBERSORT, ESTIMATE, and ssGSEA techniques were used to explore immunological characteristics, drug response, and gene expression in LUAD. Real-time RT-PCR was conducted to assess variations in mRNA expression levels of the gene XCR1 between cancerous and normal tissues in 10 lung cancer patients. We identified 249 genes associated with autophagy-dependent cell death (ADCD) at both single-cell and bulk transcriptome levels. Univariate COX regression analysis revealed that 18 genes were significantly associated with overall survival (OS). Using LASSO-Cox analysis, we developed an ADCD signature based on five genes (BIRC3, TAP1, SLAMF1, XCR1, and HLA-DMB) and created the ADCD-related risk scoring system (ADCDRS). Validation of this model demonstrated its ability to predict disease prognosis and its correlation with clinical characteristics, immune cell infiltration, and the tumor microenvironment. To enhance clinical applicability, we integrated an ADCDRS nomogram. Furthermore, we identified potential drugs targeting specific risk subgroups. We successfully identified a model based on five ADCD genes to predict disease prognosis and treatment efficacy in LUAD, as well as to assess the tumor immune microenvironment. An efficient and practical ADCDRS nomogram was designed.

Nicotine promotes M2 macrophage polarization through α5-nAChR/SOX2/CSF-1 axis in lung adenocarcinoma

α5-nicotinic acetylcholine receptor (α5-nAChR) plays a vital part in lung adenocarcinoma (LUAD). However, it is not comprehensively understood that how the α5-nAChR affects LUAD. Through diverse bioinformatics analyses and immunohistochemistry, the expressions of α5-nAChR and SOX2 as well as their relations were dissected. α5-nAChR regulated the differentiation of monocytes into M2 macrophages by targeting the STAT3/SOX2/CSF-1 signaling in the coculture system by western blotting and ChIP. α5-nAChR-mediated macrophage-mediated LUAD cell migration via SOX2/CSF-1 signaling in the cocultured medium. Correlations of α5-nAChR, SOX2 and M2 phenotype tumor-associated macrophages (TAMs) were validated in mouse LUAD models and clinical samples. α5-nAChR expression was connected to SOX2 expression, smoking and bad prognosis of LUAD among clinical samples. Nicotine-induced SOX2 expression was mediated by α5-nAChR via STAT3. Additionally, SOX2-mediated macrophage colony-stimulating factor (CSF-1) expression contributed to LUAD progression in vitro. Furthermore, α5-nAChR expression was strongly linked to pSTAT3, SOX2 and M2 macrophage marker CD206 expression and negatively correlated with M1 macrophage marker CD86 expression in vivo. It is indicated that M2 macrophages are mediated by the new α5-nAChR /SOX2/CSF-1 axis in nicotine-related LUAD, which is a potential therapeutic strategy for cancer.

The effect of median household income on the prognosis of lung adenocarcinoma: A SEER analysis.

Lung cancer is the main cause of death related to malignant tumors. Since cause-specific mortality can guide clinical decision-making, this study employed the Fine-Gray model based on the Surveillance, Epidemiology, and End Results (SEER) database to identify significantly how socio-economic status influences initial treatment decisions and survival outcomes in patients with lung adenocarcinoma. The aim of this study was to identify the predictors of lung adenocarcinoma. The U.S. Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients diagnosed with lung adenocarcinoma between 2000 and 2018. Seventeen thousand four hundred forty-one patients with lung adenocarcinoma were subdivided in four socio-economic tertiles, based on median household income. Cox regression modeling explored the relationship between race, surgery, grading, and median household income on survival outcomes. The study also assessed patient demographic characteristics, age at diagnosis, and surgical interventions. Among 17441 patients with primary lung adenocarcinoma, the age distribution was as follows: less than 45 years (n=202, 1.16%), between 45 and 54 years (n=1121, 6.43%), 55 and 64 years (n=4252, 24.38%), 65 and 74 years (n=6357, 36.45%), 75 and 84 years (n=4426, 25.38%), and more than 84 years (n=1083, 6.2%). The adjusted hazard ratio (aHR) with 95% CI for ages 65-74 years, 75-84 years, and older than or equal to 85 years were 0.25 (0.11,1.29), 0.40 (0.11,1.50), and 0.72 (0.11,2.05), respectively. Multifactorial Cox regression indicated that the aHR for tumor metastasis was 0.93 (0.03, 2.54), and for patients who did not undergo surgery, it was 1.46 (0.03, 4.31). Grade IV patients exhibited the lowest survival rate [0.66(0.11, 1.93)]. A notable correlation existed between median household income and survival, with distinctly lower survival rates observed in low-income groups. Older patients, especially those who did not undergo surgery and had a higher tumor grade, had significantly reduced survival. Moreover, survival rates for black patients in lower-income brackets were worse than those for white patients in the same financial category. The quality of life of lung cancer patients is affected by low-income family.

Radiologists vs. AI-based software: Predicting Lymph Node Metastasis and Prognosis in Lung Adenocarcinoma from CT under Various Image Display Conditions

PurposeTo compare the variability of quantitative values from lung adenocarcinoma CT images independently assessed by two radiologists and AI-based software under different display conditions, and to identify predictors of pathological lymph node metastasis (LNM), disease-free survival (DFS), and overall survival (OS). MethodsPreoperative CT images of 307 patients were displayed under four conditions: lung-1, lung-2, mediastinum-1, and mediastinum-2. Two radiologists (R1, R2) measured total diameter (tD) and the longest solid diameter (sD) under each condition. The AI-based software automatically detected lung nodules, providing tD, sD, total volume (tV), and solid volume (sV). ResultsAll measurements by R1 and R2 with AI-based software were identical. Four out of the eight measurements showed significant variation between R1 and R2. For LNM, multivariate logistic regression identified significant indicators including sD at mediastinum-2 of R1, sD at mediastinum-1 and mediastinum-2 of R2, tV, and the proportion of sV to tV (sV/tV) of AI-based software. For DFS, multivariate Cox regression identified sD at lung-1 of R1, the proportions of sD to tD at lung-2 of R1, sD at lung-2 and mediastinum-1 of R2, tV, and sV/tV of AI-based software as significant. For OS, multivariate Cox regression identified sD at lung-1 and mediastinum-2 of R1, tD at lung-2 of R2, sD at mediastinum-1 of R2, sV, and sV/tV of AI-based software as significant. ConclusionRadiologists’ CT measurements were significant predictors of LNM and prognosis, but variability existed among radiologists and display conditions. AI-based software can provide accurate and reproducible indicators for predicting LNM and prognosis.Micro-abstract: Accurate measurement of lung adenocarcinoma is crucial for determining treatment plan and predicting prognosis. However, inter-observer variability and display conditions can affect these measurements. We compared tumor size measurements between radiologists and commercially available AI-based software using preoperative CT images from 307 cases and evaluated their predictive value for lymph node metastasis, disease-free survival, and overall survival. We concluded that while radiologist measurements showed inter-observer variability, AI-based software provided accurate and reproducible prognostic indicators.

TMEM173 is a biomarker of predicting prognosis, immune responses and therapeutic effect in human lung adenocarcinoma

The concerns on the function of Transmembrane protein 173 (TMEM173)-dependent innate immunity in prevention and management of cancers has recently been increased. The role of TMEM173 in predicting the prognosis and response to treatment in lung adenocarcinoma (LUAD) remain unclear. Our study revealed that TMEM173 expression was significantly differential in various tumors and the related prognosis was heterogeneous. Further investigation discovered that the expression level of TMEM173 in LUAD tissues was significantly decreased and high TMEM173 expression is associated with better overall survival in LUAD patients. TMEM173 was mainly enriched in immune response-regulating signaling pathway, T cell activation and cell cycle G2/M phase. Furthermore, it was found that TMEM173 expression was positively related to markers and infiltration levels of tumor-infiltrating immune cells. TMEM173 could predict response to targeted therapy, chemotherapy and immunotherapy in LUAD patients. In vitro TMEM173 knockdown decreased the percentage of G2 phase cells, contributing to the increased growth of lung cancer cells. These results implied that TMEM173 might be a prognostic biomarker and a potential target of precision therapy for LUAD patients.

Oxidative phosphorylation related gene COA6 is a novel indicator for the prognosis and immune response in lung adenocarcinoma

Although the initial research focused on glycolysis, mitochondrial oxidative phosphorylation has become a major target of cancer cells. Cytochrome C oxidase assembly factor 6 (COA6) is a conserved assembly factor necessary for complex IV biogenesis. Nevertheless, the clinical predictive value of COA6, especially its correlation with immune cell infiltration in lung adenocarcinoma (LUAD), has not yet been elucidated. COA6 exhibited higher expression levels in LUAD cells and tumor tissues compared to normal tissues. Additionally, heightened COA6 expression was associated with reduced overall survival (OS) and advanced tumor stage. Apart from its role in mitochondrial respiratory processes, COA6 may be involved in the process of antigen binding, immunoglobulin receptor binding. Interestingly, we observed a positive correlation between COA6 expression and tumor mutational burden (TMB), as well as a significant association with decreased immune cell infiltration. COA6 was linked to resistance against gemcitabine and etoposide. We verified that COA6 was highly expressed in LUAD experimentally and cell proliferation was inhibited after COA6 knockdown. Thus, we conclude that the expression of COA6 was correlated with reduced immune cell infiltration. Additionally, COA6 functioned as a biomarker for drug sensitivity and the prognosis of lung adenocarcinoma.

Construction of machine learning models of lipid metabolism-related long non-coding RNA in lung adenocarcinoma is associated with microenvironmental heterogeneity and immunotherapy

Using various bioinformatics tools, we constructed a prognostic model integrating the expression profiles of lipid metabolization-related lncRNAs and clinical features. Our study discovered that various lipid metabolism-related lncRNAs were linked to the prognosis of lung adenocarcinoma. The link between immune cell infiltration in the tumour microenvironment and the expression level of lncRNAs involved with lipid metabolism was also investigated. Our findings suggest that there is a complex interplay between lipid metabolism, microenvironmental heterogeneity, and immunotherapy in lung adenocarcinoma. Furthermore, the study has significant clinical implications for the development of effective therapies for patients with lung adenocarcinoma by investigating the potential of these lncRNAs as biomarkers for anticipating the response to immunotherapy. Finally, our study emphasises the significance of continued analysis of lncRNAs associated with lipid metabolism in tumours to better understand the mechanisms behind the incidence and progression of lung adenocarcinoma. Several of the strengths of our work are the extensive analysis of the relationship between lipid metabolism and lncRNAs in lung adenocarcinoma and the utilization of a sizable sample size from the TCGA-LUAD cohort. However, there are also some limitations. Firstly, the mechanisms of how these lncRNAs interact with lipid metabolism pathways and immune response require further investigation. Secondly, our study was based on bioinformatics analysis and lacked experimental verification. Finally, our study was limited to the TCGA-LUAD cohort and further validation using other independent cohorts is required. In conclusion, our study provides a comprehensive and systematic analysis of lncRNAs associated with lipid metabolism in lung adenocarcinoma. Lung cancer patients may benefit from using identified lncRNAs as therapeutic targets and prognostic biomarkers. Validating these findings and confirming the potential therapeutic applications of these lncRNAs will require more mechanistic research.

Identification of a Novel Signature Based on Ferritinophagy-Related Genes to Predict Prognosis in Lung Adenocarcinoma: Focus on AHNAK2

Background: Lung adenocarcinoma (LUAD) accounts for over 40% of all non-small cell lung cancer (NSCLC) cases and continues to be difficult to treat despite advancements in diagnostics and therapies. Ferritinophagy, a newly recognized autophagy process linked to ferroptosis, has been associated with LUAD development. Recent studies have shown a dysregulation of genes related to ferritinophagy in LUAD, indicating its potential as a therapeutic target. Methods: We constructed a predictive model using seven genes associated with ferritinophagy. The model’s accuracy was evaluated across three independent gene expression datasets. We analyzed the biological functions, immune environment, mutations, and drug sensitivities in groups with high and low risk. Utilizing a single-cell sequencing (scRNA-seq) dataset, we confirmed the expression of the model genes and identified a subtype of epithelial cells expressing AHNAK2. We further investigated the impact of the ferritinophagy-related gene AHNAK2 on LUAD cell proliferation, invasion, migration, and ferroptosis in vitro. Results: Our prediction model, comprising seven genes (AHNAK2, ARNTL2, CD27, LTB, SLC15A1, SLC2A1, and SYT1), has shown potential in predicting the prognosis of individuals diagnosed with LUAD. Notably, AHNAK2 impedes ferroptosis, promoting LUAD progression in vitro. Conclusions: Our research suggests that ferritinophagy-associated genes are promising prognostic markers for LUAD and lay the groundwork for further exploration of ferritinophagy’s role in LUAD. Furthermore, we present AHNAK2 as a novel regulator of ferroptosis, which requires further investigation to understand its mechanism.

Non-classical action of Ku70 promotes Treg suppressive function through a FOXP3-dependent mechanism in lung adenocarcinoma.

Ku70, a DNA repair protein, binds to the damaged DNA ends and orchestrates the recruitment of other proteins to facilitate repair of DNA double-strand breaks. Besides its essential role in DNA repair, several studies have highlighted non-classical functions of Ku70 in cellular processes. However, its function in immune homeostasis and anti-tumor immunity remains unknown. Here, we discovered a marked association between elevated Ku70 expression and unfavorable prognosis in lung adenocarcinoma, focusing specifically on increased Ku70 levels in tumor-infiltrated Treg cells. Using a lung-colonizing tumor model of in mice with Treg-specific Ku70 deficiency, we demonstrated that deletion of Ku70 in Treg cells led to a stronger anti-tumor response and slower tumor growth due to impaired immune-suppressive capacity of Treg cells. Furthermore, we confirmed that Ku70 played a critical role in sustaining the suppressive function of human Treg cells. We found that Ku70 bound to FOXP3 and occupied FOXP3-bound genomic sites to support its transcriptional activities. These findings not only unveil a non-homologous end joining (NHEJ)-independent role of Ku70 crucial for Treg suppressive function, but also underscore the potential of targeting Ku70 as an effective strategy in cancer therapy, aiming to both restrain cancer cells and enhance pulmonary anti-tumor immunity.

Immune regulation and prognostic prediction model establishment and validation of PSMB6 in lung adenocarcinoma.

Lung cancer is one of the most common malignant tumors, and patients are often diagnosed at an advanced stage, posing a substantial risk to human health, so it is crucial to establish a model to forecast the prognosis of patients with lung cancer. Recent research has indicated that proteasome 20S subunit 6 (PSMB6) may be closely associated with anti-apoptotic pathways, and proliferation transduction signals in tumor cells of different tumors. However, the precise role of PSMB6 in the immunoregulatory processes within lung adenocarcinoma (LUAD) is yet to be elucidated. By analyzing the TCGA database, we discovered a positive correlation between the expression of PSMB6 and tumor growth trends, and lung adenocarcinoma patients with elevated PSMB6 expression levels had a worse prognosis. Our findings suggest a close correlation between PSMB6 expression levels, immune cell infiltration and immune checkpoint gene expression, which suggests that PSMB6 may become a new independent prognostic indicator. In addition, we developed a prognostic model of PSMB6-regulated immune infiltration-associated genes by analyzing the link between PSMB6 and the immune microenvironment. This model can not only predict the prognosis of lung adenocarcinoma but also forecasts the patient's reaction to immunotherapy. The validity of this research outcome has been confirmed by the GSE31210 and IMvigor210 cohorts. Analysis of the Kaplan-Meier Plotter database indicates that individuals with elevated levels of PSMB6 expression exhibit a poorer prognosis. Additionally, in vitro experiments demonstrated that knockdown of PSMB6 inhibits the proliferation, migration, and invasion of lung adenocarcinoma cells while promoting their apoptosis. Overall, our findings suggest that PSMB6 could remarkably influence the management and treatment of lung adenocarcinoma, opening new avenues for targeted immunotherapeutic strategies.