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.

BackgroundIndividual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients.MethodsMultivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model.ResultsThere were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients.ConclusionThe current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

Lung adenocarcinoma (LUAD) is one of the most malignant tumors with significant implications for population health and life. Nicotinamide metabolism may play a pivotal part in influencing the prognosis of LUAD. This study aimed to figure out the potential value of nicotinamide metabolism-related genes (NMRGs) in LUAD prognosis. Forty-two NMRGs were obtained from the Molecular Signatures Database and intersected with differentially expressed genes (DEGs) in LUAD from The Cancer Genome Atlas. Unsupervised consensus clustering was conducted based on the intersecting genes, followed by identification of DEGs between clusters. Enrichment analyses and LASSO-Cox regression analyses were conducted. Immune cell infiltration was calculated using the ssGSEA algorithm. Wilcoxon test was conducted on the TIDE scores of different risk groups. Targeted small molecule drug prediction was performed on the DEGs in groups. A total of 10 nicotinamide metabolism-related differentially expressed genes (NMRDEGs) were identified for LUAD clustering. LUAD patients were divided into two clusters. Cluster 1 had considerably higher overall survival rates compared to cluster 2. Differential genes between clusters were mainly enriched in hormone metabolic processes and the neuroactive ligand-receptor interaction pathway. An 11-gene prognostic model was established to predict the overall survival of LUAD patients. The validation set showed that the model had a good prognostic effect. Analysis of immune cell infiltration demonstrated that in the high-risk (HR) group, infiltration levels of mast cells and neutrophils were considerably lower than low-risk (LR) group, while infiltration levels of NK cells and Th2 cells were considerably higher than LR group. TIDE scores indicated a lower likelihood of immune evasion and a potentially better response to immunotherapy in LR patients. Drug prediction showed that FGIN-1-43, deferiprone, and griseofulvin were potential drugs. We successfully developed a predictive model for the survival and immunotherapy response of LUAD patients.

Background: Lung adenocarcinoma (LUAD) remains heterogeneous in the prognosis of patients; oxidative stress (OS) has been widely linked to cancer progression. Therefore, it is necessary to explore the prognostic value of the OS-associated genes in LUAD. Methods: An OS-associated prognostic signature was developed using the Cox regression and random forest model in The Cancer Genome Atlas-LUAD dataset. Kaplan-Meier (K-M) survival curve and time-dependent receiver operating characteristic (tROC) curves were applied to evaluate and validate the predictive accuracy of this signature among the training and testing cohorts. A nomogram was constructed and also verified by the concordance index (C-index), calibration curves, and tROC curves, respectively. ESTIMATE algorithm and CIBERSORT algorithms were conducted to explore the signature's immune characteristics. Core target genes of the prognostic signature were identified in the protein-protein interaction network. Results: A six OS-associated prognostic gene signature (CDC25C, ERO1A, GRIA1, TERT, CAV1, BDNF) was developed. The tROC and K-M survival curves in the training and testing cohorts revealed that the signature had good and robust predictive capability to predict the overall survival of LUAD patients. Meanwhile, the risk score was an independent prognostic factor influencing patients' overall survival. The results of the C-index (0.714), calibration curves, and the 1-, 2-, and 3-year tROC curves (area under the curve = 0.703, 0.737, and 0.723, respectively) suggested that the nomogram had good predictive efficacy and prognostic value for LUAD. Then, the authors found that the high-risk group may be depletion or loss of antitumor function of immune cells. Finally, 10 core genes of the signature were predicted. Conclusion: Their study may provide a novel understanding for the identification of prognostic stratification in LUAD patients, as well as the regulation of OS-associated genes in LUAD progression.

We aimed to identify the immune checkpoint Programmed cell death 1 (PD-1)-related gene signatures to predict the overall survival of lung adenocarcinoma (LUAD). RNA-seq datasets associated with LUAD as well as clinical information were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Based on the expression level of PD-1, Kaplan-Meier (K-M) survival analysis was performed to divide samples into PD-1 high- and low- expression groups. Then, differentially expressed genes (DEGs) between high- and low- expression groups were identified. Meanwhile, samples were divided into the high and low immune infiltration groups according to score of immune cell, followed by screening of DEGs between these two groups. Subsequently, DEGs related to both PD-1 expression and immune infiltration was integrated to obtain the overlapping genes. Lasso COX regressions were implemented to construct prognostic signatures. The prognostic model was validated using an independent GEO dataset and TCGA cohorts. In addition, the predictive ability of the seven-gene prognostic model with other molecular biomarkers was compared. A seven-gene signature (DPT, ITGAD, CLECL1, SYT13, DUSP26, AMPD1, and NELL2) related to PD-1 was developed through Lasso Cox regression. Univariate and multivariate regression analyses indicated that the constructed risk model was an independent prognostic factor. K-M survival analysis indicated that patients in the high risk group had significantly worse prognosis than those in the low risk group. Further, the results of validation analysis showed that this model was reliable and effective. The constructed prognostic model can predict overall survival in LUAD patients with great predictive performance, and it may be applied for diagnosis and adjuvant treatment of LUAD in clinical trials.

BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

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.

Identification and validation of lung adenocarcinoma (LUAD)-associated targets for monascin from the extracts of Monascus purpureus-fermented rice: Compound preparation, high-throughput genome sequencing and bioinformatics analysis

More than 40% of lung cancers are lung adenocarcinoma (LUAD) worldwide. However, the prognosis of LUAD is poor for the lack of effective treatment methods. Our study identified PLK1 as a novel prognosis biomarker and treatment target for LUAD. Based on the Cancer Genome Atlas (TCGA) database, differentially expressed genes (DEGs) from 551 LUAD cases were analyzed for the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. To explore the biological pathways and the tumor-infiltrating immune cells (TICs) using gene set variation analysis (GSVA) and the CIBERSORT, as well as to analyze DEGs, a protein-protein interaction (PPI) network and Cox regression analysis were performed. Validation of DEGs was achieved through quantitative real-time PCR (qPCR) and immunoblotting. DEGs associated with the cell cycle were sorted out. Cell cycle scores were positively correlated with age, clinical stages, and metastasis and negatively correlated with overall survival of LUAD patients. PPI and Cox analyses showed that PLK1 could be a prognostic factor for LUAD patients. CIBERSORT analysis revealed a positive correlation between the transcription level of PLK1 and the function of CD8+ and activated memory CD4+ T cells, as well as a negative correlation with activated natural killer cells. Furthermore, PLK1 overexpression increased immune cytotoxicity, as measured by the cytolytic activity score, IFN- score, and IFN- level. There is a strong correlation between PLK1 and key features of TICs, indicating its potential as a promising prognostic biomarker for LUAD.

The tumor microenvironment plays an important role in tumor development and progression, but the role of immune and stromal cells in this environment has not been sufficiently studied. In this study, we aimed to identify key genes associated with the microenvironment of lung adenocarcinoma (LUAD). Raw data for stromal and immune cells in malignant tumors were downloaded from The Cancer Genome Atlas (TCGA). These expression data were used to identify the differentially expressed genes (DEGs) in tissue samples of LUAD with high and low immune scores. A protein-protein interaction (PPI) network based on genes with significant differential expression was constructed. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to functionally annotate putative hub genes. These genes were assessed via Kaplan Meier analysis to determine their correlation with overall survival. In total, we identified 216 DEGs which were correlated with immune and stromal scores, including 30 hub genes which were identified based on the PPI network. Further analysis suggested that the expression levels of 10 of these genes were significantly correlated with overall survival of LUAD patients. These key hub genes included CCR2, CCR5, CD53, CYBB, HCK, IRF8, LCP2, PLEK, PTPRC, and TLR7. Moreover, the expression level of CCR2 was found to have strong prognostic value for LUAD patients. Additionally, high expression of CYBB was also correlated with better survival of patients with LUAD. The results of this study open several new avenues to explore in the treatment of LUAD.

So far, there is a lack of reliable prognostic biomarkers for lung adenocarcinoma (ADC). Initially, we found that EF-hand and coiled-coil domain containing 1 (EFCC1) was a novel gene which was downregulated consistently with the progression of lung ADC in The Cancer Genome Atlas (TCGA) data through bioinformatics analysis. In this study, we aimed to evaluate the prognostic significance of EFCC1 in lung ADC in both TCGA data and clinical samples. Firstly, the expression level and prognostic significance of EFCC1 in lung ADC were investigated in TCGA data. Then, the expression level of EFCC1 was validated by qPCR, Western blot, and immunohistochemistry (IHC) in five clinical lung ADC and matched adjacent non-tumor tissues. Finally, the association of EFCC1 expression with clinicopathological characteristics and overall survival (OS) in lung ADC patients was further evaluated in 130 clinical lung ADC samples with tissue microarray (TMA). In TCGA data, we found that decreased mRNA expression (P<0.001), elevated DNA methylation (P<0.001) of EFCC1 in lung ADC samples compared with normal lung samples, and low EFCC1 mRNA expression was associated with poor OS in lung ADC patients (HR =0.856, 95% CI: 0.754-0.970, P=0.015). In five clinical lung ADC and matched adjacent non-tumor tissues, both mRNA and protein levels of EFCC1 were lower in all lung ADC tissues than in their adjacent non-tumor counterparts. In 130 clinical lung ADC samples with TMA, EFCC1 expression was correlated with tumor-node-metastasis (TNM) stages (P=0.040) and lymph node metastasis status (P=0.001). The Kaplan-Meier survival curve revealed that low EFCC1 expression was significantly associated with poor OS in lung ADC patients (P=0.001) and multivariate Cox regression hazard model demonstrated that EFCC1 expression level was an independent prognostic factor for lung ADC patients (HR =0.557, 95% CI: 0.351-0.883, P=0.013). Our findings suggested that decreased expression of EFCC1 was significantly associated with progression of lung ADC and could serve as a novel prognostic biomarker for lung ADC patients.

BackgroundBrain metastasis was one of the factors leading to the poor long-term prognosis of patients with lung adenocarcinoma (LUAD).MethodsThe expression levels of immune genes in LUAD and LUAD brain metastases tissues were analyzed in GSE161116 dataset using the GEO2R, and the levels of differential immune genes in normal lung and LUAD tissues were verified. The biological functions and signaling mechanisms of the differential immune genes were explored via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Cox regression analysis was used to screen the prognostic factors of LUAD patients, and a risk model was constructed. The role of the model was checked in the development of LUAD via receiver operating characteristic analysis, gene set enrichment analysis, and Cox regression analysis.ResultsDifferentially expressed genes (DEGs) in brain metastasis were involved in the adaptive immune response, B cell differentiation, leukocyte migration, NF-kB signaling pathway, among others. The expression levels of TNFRSF11A, MS4A2, IL11, CAMP, MS4A1, and F2RL1 were independent factors affecting the poor prognosis of LUAD patients via Cox regression analysis and Akaike information criterion. In the constructed risk model, the overall survival of LUAD patients in the high-risk group was poor. The risk model was significantly related to the gender, clinical stage, T stage, lymph node metastasis, and survival status of LUAD patients. In addition, the risk model score was an independent risk factor that affected the poor prognosis of LUAD patients. TNFRSF11A, CAMP, F2RL1, IL11, MS4A1, and MS4A2 of the risk factors had diagnostic significance in LUAD brain metastasis and LUAD. The risk model participated in cytokinetic process, cell cycle, citrate cycle TCA cycle, etc. The risk model score was correlated with the levels of B cells memory, mast cells resting, macrophages M0, mast cells activated, neutrophils, eosinophils, T cells gamma delta, and immune cell markers.ConclusionsThe risk model based on the LUAD brain metastasis immune factors TNFRSF11A, MS4A2, IL11, CAMP, MS4A1, and F2RL1 was related to the diagnosis, poor prognosis, and immune infiltrating cells of LUAD patients, and is expected to provide a reference for the development of treatment strategies for LUAD patients.

Background: Lung adenocarcinoma (LUAD) is the most commonly histological subtype of lung cancer (LC) and the prognoses of the majority of LUAD patients are still very poor. The present study aimed at integrating long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) expression data to construct lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network and identify importantly potential lncRNA signature in ceRNA network as a candidate prognostic biomarker for LUAD patients. Methods: lncRNA, miRNA and mRNA expression data as well as clinical characteristics of LUAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DElncRNAs), differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNA (DEmiRNA) between LUAD and normal lung tissues samples were analyzed. A lncRNA-miRNA-mRNA ceRNA network was constructed and the biological functions of DEmRNAs in ceRNA network were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Univariate and multivariate Cox regression analyses of DElncRNAs in ceRNA network were implemented to predict the overall survival (OS) in LUAD patients. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Results: A total of 1,664 DElncRNAs, 120 DEmiRNAs and 2,503 DEmRNAs was identified between LUAD and normal lung tissues samples. A lncRNA-miRNA-mRNA ceRNA network including 140 DElncRNAs, 33 DEmiRNAs and 57 DEmRNAs was established. Kaplan-Meier (KM) [Log-rank (LR) test] and univariate regression analysis of those 140 DElncRNAs revealed that 7 DElncRNAs (LINC00518, UCA1, NAV2-AS2, MED4-AS1, SYNPR-AS1, AC011483.1, AP002478.1) were simultaneously identified to be associated with OS of LUAD patients. A multivariate Cox regression analysis of those 7 DElncRNAs showed that a group of 4 DElncRNAs including AP002478.1 (Cox P=4.66E-03), LINC00518 (Cox P=2.34E-04), MED4-AS1 (Cox P=6.42E-03) and NAV2-AS2 (Cox P=6.66E-02) had significantly prognostic value in OS of LUAD patients. The cumulative risk score indicated that the 4-lncRNA signature was significantly associated with OS of LUAD patients (P=0). The area under the curve (AUC) of the 4-lncRNA signature related with 3-year survival was 0.669. Conclusions: The present study provides novel insights into the lncRNA-related regulatory mechanisms in LUAD, and identifying 4-lncRNA signature may serve as a candidate prognostic biomarker in predicting the OS of LUAD patients.

Lung Adenocarcinoma (LUAD), a common and aggressive form of lung cancer, poses significant treatment challenges due to its low survival rates. To better understand the role of ferroptosis driver genes in LUAD, this study aimed to explore their diagnostic and prognostic significance, as well as their impact on treatment approaches and tumor immune function in LUAD. To accomplish the defined goals, a comprehensive methodology incorporating both in silico and wet lab experiments was employed. A comprehensive analysis was conducted on a total of 233 ferroptosis driver genes obtained from the FerrDB database. Utilizing various TCGA databases and the RT-qPCR technique, the expression profiles of 233 genes were examined. Among them, TP53, KRAS, PTEN, and HRAS were identified as hub genes with significant differential expression. Notably, TP53, KRAS, and HRAS exhibited substantial up-regulation, while PTEN demonstrated significant down-regulation at both the mRNA and protein levels in LUAD samples. The dysregulation of hub genes was further associated with poor overall survival in LUAD patients. Additionally, targeted bisulfite-sequencing (bisulfite-seq) analysis revealed aberrant promoter methylation patterns linked to the dysregulation of hub genes. Furthermore, hub genes were found to participate in diverse oncogenic pathways, highlighting their involvement in LUAD tumorigenesis. By leveraging the diagnostic and prognostic potential of ferroptosis driver hub genes (TP53, KRAS, PTEN, and HRAS), significant advancements can be made in the understanding and management of LUAD pathogenesis. Therapeutic targeting of these genes using specific drugs holds great promise for revolutionizing drug discovery and improving the overall survival of LUAD patients.

The function of HOXC antisense RNA 1 (HOXC-AS1) in lung adenocarcinoma (LUAD) remains largely unexplored. The objective of this research was to examine the relationship between HOXC-AS1 levels and LUAD through both bioinformatics analysis and experimental validation. We employed statistical methods and bioinformatics to evaluate the correlation between HOXC-AS1 expression and various clinical features, survival predictors, regulatory mechanisms, and immune cell infiltration in LUAD. The levels of HOXC-AS1 in LUAD cell lines were ascertained through quantitative reverse transcription PCR. HOXC-AS1 displayed significantly increased expression in individuals with LUAD. There was a significant correlation between high HOXC-AS1 levels and diminished overall survival in LUAD patients, characterized by a hazard ratio of 0.66, a 95% confidence interval of 0.49 to 0.88, and a statistically significant P-value (0.005). An elevated expression of HOXCAS1 was found to be a standalone predictor of poor overall survival in LUAD patients, with a Pvalue of 0.002. HOXC-AS1 was found to be implicated in various pathways, such as neuroactive ligand-receptor interaction and asthma, among others. The study revealed a substantial link between high HOXC-AS1 expression and unfavorable outcomes in LUAD, including poor survival and altered immune cell infiltration. LUAD cell lines exhibited a marked increase in HOXC-AS1 expression compared to the Beas-2B normal lung cell line. The research indicated a strong association between higher levels of HOXC-AS1 and negative outcomes in LUAD, such as reduced survival rates and the presence of immune cell infiltration. HOXC-AS1 could potentially be utilized as a biomarker to anticipate patient prognosis and their likelihood of responding to immunotherapies in LUAD.