Prognostic Factor In Lung Adenocarcinoma Research Articles

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607-0.7720) and an area under the precision-recall curve of 0.8391 (0.8326-0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.

Bioinformatics analysis reveals SOD1 is a prognostic factor in lung adenocarcinoma.

Lung cancer is a major cause of cancer-related deaths worldwide. Unfortunately, non-small cell lung cancer (NSCLC) often lacks clear clinical symptoms and molecular markers for early diagnosis, which can hinder the initiation of timely treatments. In this study, we conducted an extensive bioinformatics analysis of copper-zinc superoxide dismutase (SOD1), a molecule linked to lung adenocarcinoma (LUAD) to enhance early detection and treatment approaches for this condition. A bioinformatics analysis was conducted using a dataset from The Cancer Genome Atlas (TCGA) database. Several analytical methods, such as a differential expression analysis, a Kaplan-Meier survival analysis, a clinicopathological analysis, an enrichment analysis, protein-protein interaction (PPI) network construction using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and an immunoreactivity analysis of SOD1 expression in LUAD using TIMER were employed. We further validated the expression of SOD1 in LUAD through in vitro experiments using quantitative polymerase chain reaction (qPCR) and Western blot. Our findings indicate that LUAD tissues exhibited significantly higher expression levels of SOD1 than healthy tissues. The univariate Cox analysis showed that the elevated level was linked to unfavorable overall survival (OS) rates. Further, the Cox regression analysis of multiple variables suggested that elevated SOD1 expression levels acted as an autonomous prognosticator for unfavorable OS. We also conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and a gene set enrichment analysis (GSEA) and observed differential pathway enrichment among patients with high SOD1 expression. In addition, a correlation between SOD1 and immune cell infiltration was found. The in vitro experiments confirmed that SOD1 expression was upregulated in LUAD. SOD1 could serve as a reliable prognostic indicator in individuals diagnosed with LUAD. Our findings may prove valuable in the development of therapeutic and prognostic markers for LUAD. The potential clinical utility of SOD1 in LUAD requires further investigation.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1.

Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations. Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff etal. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort. Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells. Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

DLX1 acts as a novel prognostic biomarker involved in immune cell infiltration and tumor progression in lung adenocarcinoma.

The biological function of distal-less homeobox 1 (DLX1) in lung adenocarcinoma (LUAD) remains unclear, despite a growing body of evidence that DLX1 is involved in the initiation and progression of various tumors. This study explored and confirmed the prognostic and immunologic roles of DLX1 in LUAD via bioinformatic analysis and cellular functional validation. MethSurv was used to analyze the DNA methylation levels of DLX1 and the prognostic value of CpG islands. DLX1 mutation rates and prognoses between patients with and without the mutated DLX1 gene were analyzed by cBioPortal. Finally, cellular functional assays were used to investigate the effect of DLX1 on LUAD cells. Our results showed that DLX1 mRNA expression was significantly upregulated in LUAD. High DLX1 expression or promoter methylation was associated with worse prognosis, which confirmed DLX1 as an independent prognostic factor in LUAD. The level of multiple immune cell infiltration was significantly associated with DLX1 expression. Genes in the high DLX1 expression group were mainly enriched in cell cycle checkpoint, DNA replication, DNA repair, Fceri-mediated MAPK activation, TP53 activity regulation, and MET activation of PTK2-regulated signaling pathways. Cellular functional assays showed that the knockdown of DLX1 inhibited the proliferation, migration, and invasion of LUAD cells. Our study identified DLX1 as a potential diagnostic and prognostic biomarker, and a promising therapeutic target in LUAD.

High expression of VTA1 is an adverse prognostic factor in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common pathological type of non-small cell lung cancer; identifying preferable biomarkers has become one of the current challenges. Given that VTA1 has been reported associated with tumor progression in various human solid cancers but rarely reported in LUAD, herein, RNA sequencing data from TCGA and GTEx were obtained for analysis of VTA1 expression and differentially expressed gene (DEG). Furthermore, functional enrichment analysis of VTA1-related DEGs was performed by GO/KEGG, GSEA, immune cell infiltration analysis, and protein-protein interaction (PPI) network. In addition, the clinical significance of VTA1 in LUAD was figured out by Kaplan-Meier Cox regression and prognostic nomogram model. R package was used to analyze incorporated studies. As a result, VTA1 was highly expressed in various malignancies, including LUAD, compared with normal samples. Moreover, high expression of VTA1 was associated with poor prognosis in 533 LUAD samples, as well as T stage T2&T3&T4, N stage N1&N2&N3, M stage M1, pathologic stage II&III&IV, and residual tumor R1&R2, et al. (P < 0.05). High VTA1 was an independent prognostic factor in Cox regression analysis; Age and cytogenetics risk were included in the nomogram prognostic model. Furthermore, a total of 4232 DEGs were identified between the high- and the low-expression group, of which 736 genes were up-regulated and 3496 genes were down-regulated. Collectively, high expression of VTA1 is a potential biomarker for adverse outcomes in LUAD. The DEGs and pathways recognized in the study provide a preliminary grasp of the underlying molecular mechanisms of LUAD carcinogenesis and progression.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD. In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD. We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma. Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Analysis of Nucleoporin 107 Overexpression and Its Association with Prognosis and Immune Infiltration in Lung Adenocarcinoma by Bioinformatics Methods.

Lung adenocarcinoma (LUAD) has high morbidity and mortality. Current studies indicate nucleoporin 107 (NUP107) is involved in the construction of nuclear pore complex, and NUP107 overexpression contributes to the growth and development in most types of cancers, but its effect in LUAD has not been elucidated. Differences in NUP107 expression were investigated using the Cancer Genome Atlas (TCGA) and multiple Gene Expression Omnibus (GEO) data sets. Enrichment analysis were implemented to probe the NUP107 function. The association of NUP107 with the degree of immune cell infiltration was investigated by the TIMER database, single-sample gene set enrichment analysis (ssGSEA), and ESTIMATE. The association of NUP107 expression with tumor mutation burden (TMB), TP53, and immune checkpoint was analyzed. Single-cell RNA sequencing data were used to detect NUP107 expression in different cell clusters. Finally, we performed real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) to prove the difference of NUP107 expression. NUP107 was overexpressed in LUAD and mainly expressed in cancer stem cell (CSC). Overexpression of NUP107 in LUAD suggested a poorer prognosis. Functional enrichment analysis pointed out that NUP107 was mainly linked to the regulation of cell cycle. Both immune cell infiltration and TMB were found to be in connection with NUP107. Cases in the group with high NUP107 expression had poorer immune infiltration, but had higher expression of immune checkpoints, TMB, and proportion of TP53 mutations. NUP107 is a sensitive diagnostic and prognostic factor for LUAD and may be involved in tumor progression through its effects on cell cycle and immune infiltration.

Methylation of hypoxia-inducible factor 3 subunit alpha contributes to poor prognosis in lung adenocarcinoma.

Hypoxia-inducible factor 3 subunit alpha (HIF3A) has been implicated in various types of cancers, while its precise role in the lung adenocarcinoma remains unclear. Our study aimed to investigate the roles of HIF3A in lung adenocarcinoma and its regulation by DNA methylation. We utilized bioinformatic tools, including UALCAN and KMPlot, to analyze the relationship between HIF3A expression, DNA methylation, and patient survival rate in lung adenocarcinoma. We also used siRNA-mediated knockdown of HIF3A and DNA-methyltransferase 1 (DNMT1), as well as the treatment of DNA methylation inhibitor 5-Azacytidine, in A549 and H1299 lung adenocarcinoma cell lines. qPCR, MTT, and cell counting assays were performed to evaluate the mRNA expression and cell viability. The bioinformatic analysis revealed that HIF3A expression was downregulated and its methylation was upregulated in lung tumor tissues. Additionally, Kaplan-Meier analysis indicated a correlation between low HIF3A expression and patient poor survival rate. We found that DNMT1 regulated HIF3A methylation. Knockdown of HIF3A promoted cancer cell proliferation. These data suggest that downregulation of HIF3A promotes tumor cell proliferation, and support that HIF3A methylation may serve as a prognostic factor for lung adenocarcinoma.

High glucose enhances the aggressiveness of lung adenocarcinoma via activating epidermal growth factor receptor/signal transducer and activator of transcription 3 pathways

Epidemiological studies revealed hyperglycemia as a poor prognostic factor for lung adenocarcinoma with unclear molecular mechanisms. The present study thus aimed to investigate the effects of high glucose on the progression of lung adenocarcinoma and its underlying mechanisms. Lung adenocarcinoma cell lines, A549 and RERF-LC-KJ, were cultured in 5.6 mM glucose (normal glucose; NG) or 25 mM glucose (high glucose; HG) resembling euglycemia and hyperglycemia. Cells were examined for proliferation by the MTT assay, and migration-invasion using Transwell. The expressions of signaling proteins in epidermal growth factor receptor (EGFR) pathways and their downstream targets were investigated using Western blots. The effects of diabetes mellitus (DM) and hyperglycemia on lung adenocarcinoma growth in vivo were studied in streptozotocin-induced diabetic BALB/cAJcl-Nu/Nu mice and their nondiabetic counterparts. High glucose significantly promoted proliferation, migration, and invasion of lung adenocarcinoma cells compared with those in normal glucose (P<.05). Western blot analyses showed the increased ratio of pEGFR/EGFR in cells cultured in high glucose and subsequently activated the signal transducer and activator of transcription 3 (STAT3). Epithelial-mesenchymal (EMT) markers were also altered in lung adenocarcinoma cells in high glucose conditions, corresponding with increased migration and invasion abilities. Erlotinib, an EGFR inhibitor, significantly reversed high glucose-induced aggressive phenotypes confirming high glucose-enhancing lung adenocarcinoma progression via the activation of EGFR. DM and hyperglycemia also promoted the growth of lung adenocarcinoma xenografts in vivo in which erlotinib significantly suppressed the growth of tumors (P<.05) suggesting EGFR inhibitor as an effective therapeutic agent for lung adenocarcinoma with DM.

Impact of microvessel patterns and immune status in NSCLC: a non-angiogenic vasculature is an independent negative prognostic factor in lung adenocarcinoma.

Non-small cell lung carcinomas (NSCLC) exhibit different microvessel patterns (MVPs). Basal (BA), diffuse (DA) and papillary (PA) patterns show signs of angiogenesis (new blood vessels), while an alveolar pattern indicates that tumors are co-opting existing normal vessels (non-angiogenic alveolar, NAA). NAA tumor growth is known to exist in NSCLC, but little is known about its prognostic impact in different histological subgroups, and about associations between MVPs and immune cell infiltration. Detailed patterns of angiogenic and non-angiogenic tumor growth were evaluated by CD34 immunohistochemistry in whole tissue slides from 553 surgically treated patients with NSCLC stage I-IIIB disease. Associations with clinicopathological variables and markers related to tumor immunology-, angiogenesis- and hypoxia/metabolism were explored, and disease-specific survival (DSS) was analyzed according to histological subtypes. The predominant MVP was angiogenic in 82% of tumors: BA 40%, DA 34%, PA 8%, while a NAA pattern dominated in 18%. A contribution of the NAA pattern >5% (NAA+), i.e., either dominant or minority, was observed in 40.1% of tumors and was associated with poor disease-specific survival (DSS) (p=0.015). When stratified by histology, a significantly decreased DSS for NAA+ was found for adenocarcinomas (LUAD) only (p< 0.003). In multivariate analyses, LUAD NAA+ pattern was a significant independent prognostic factor; HR 2.37 (CI 95%, 1.50-3.73, p< 0.001). The immune cell density (CD3, CD4, CD8, CD45RO, CD204, PD1) added prognostic value in squamous cell carcinoma (LUSC) and LUAD with 0-5% NAA (NAA-), but not in LUAD NAA+. In correlation analyses, there were several significant associations between markers related to tumor metabolism (MCT1, MCT4, GLUT1) and different MVPs. The NAA+ pattern is an independent poor prognostic factor in LUAD. In NAA+ tumors, several immunological markers add prognostic impact in LUSC but not in LUAD.

Anoikis-related genes combined with single cell sequencing: Insights into model specification of lung adenocarcinoma and applicability for prognosis and therapy.

Background: Anoikis has therapeutic potential against different malignancies including lung adenocarcinoma. This study used anoikis and bioinformatics to construct a prognostic model for lung adenocarcinoma and explore new therapeutic strategies. Methods: Several bioinformatic algorithms (co-expression analysis, univariate Cox analysis, multivariate Cox analysis, and cross-validation) were used to screen anoikis-related genes (ARGs) to construct a risk model. Lung adenocarcinoma patients were divided into training and testing groups at a ratio of 1:1. The prognostic model was validated by risk score comparison between high- and low-risk groups using receiver operating characteristic curve (ROC), nomograms, independent prognostic analysis and principal component analysis. In addition, two anoikis-related genes patterns were classified utilizing consensus clustering method and were compared with each other in survival time, immune microenvironment, and regulation in pathway. Single cell sequencing was applied to analyze anoikis-related genes constructed the model. Results: This study demonstrated the feasibility of the model based on seven anoikis-related genes, as well as identifying axitinib, nibtinib and sorafenib as potential therapeutic strategies for LUAD. Risk score based on this model had could be used as an independent prognostic factor for lung adenocarcinoma (HR > 1; p < 0.001) and had the highest accuracy to predict survival compared with the clinical characteristics. Single cell sequencing analysis discovered Keratin 14 (KRT14, one of the seven anoikis-related genes) was mainly expressed in malignant cells in various cancers. Conclusion: We identified seven anoikis-related genes and constructed an accurate risk model based on bioinformatics analysis that can be used for prognostic prediction and for the design of therapeutic strategies in clinical practice.

High BRCA1 expression is an independent prognostic biomarker in LUAD and correlates with immune infiltration

High BRCA1 expression is an independent prognostic biomarker in LUAD and correlates with immune infiltration

TEDC2 correlated with prognosis and immune microenvironment in lung adenocarcinoma

Tubulin epsilon and delta complex 2 (TEDC2) is a protein coding gene whose functions are poorly identified yet. This study aimed to identify the role of TEDC2 in prognosis and immune microenvironment of lung adenocarcinoma (LUAD). Through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, the mRNA expression of TEDC2 was upregulated in LUAD tissues compared to normal tissues. The protein level of TEDC2 was also higher in LUAD in the Human Protein Atlas. The receiver operating characteristic (ROC) curve showed that high TEDC2 level could distinguish LUAD patients from normal subjects. In addition, the impact of TEDC2 expression on prognosis was evaluated by Kaplan–Meier and Cox regression analyses, and the results suggested that high TEDC2 expression was significantly associated with poor prognosis and was the independent prognostic factor in LUAD. GO and KEGG pathway analyses indicated the co-expressed genes of TEDC2 were mainly related to mitotic cell cycle processes. Importantly, high expression of TEDC2 indicated low infiltration of immune cells, especially dendritic cells and B cells. TEDC2 was also positively correlated with immune checkpoints such as PDCD1, LAG3 and CD276. Taken together, this study preliminarily revealed the clinical significance of TEDC2 in LUAD and provided novel insights into the role of TEDC2 in immune microenvironment.

Identification of novel prognostic model based on homologous recombination deficiency associated lncRNAs in lung adenocarcinoma

BackgroundNon-small cell lung cancer with homologous recombination deficiency (HRD) was revealed to have better response to immunotherapy. Long non-coding RNAs (lncRNAs) modulate multiple processes including HRD acting as potential biomarkers in tumors. The function of HRD-associated lncRNAs in lung cancer arouses our interests. MethodsTwo independent cohorts were enrolled containing 838 lung adenocarcinoma (LUAD) patients. HRD-associated lncRNAs were defined as the lncRNAs that were differential expressed in high-HRD group and low-HRD group which were classified in accordance with the HRD score. The least absolute shrinkage and selection operator cox regression was employed to construct a signature according to prognostic HRD-associated lncRNAs. The signature robustness was evaluated by using the prognosis analysis, multivariate-cox analysis, ROC curve, and nomogram. The participating pathways were estimated by gene set enrichment analysis and KEGG pathway analysis. The infiltration of immune cells was estimated by using xCell. The tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) were both utilized for the prediction of immunotherapy response. ResultsSeventeen HRD-associated lncRNAs were screened to classify the LUAD patients into two groups with variant survival that inferior overall survival was found in high-risk patients comparing to those with low-risk. Our model not only was the independent prognostic factor in LUAD but also had better performance on the prognosis prediction when making a comparison with other clinical and molecular signatures. Additionally, the high-risk group was suggested to have increased genomic instability and less response to immunotherapy. ConclusionA great predicative efficient prognostic signature was established based on 17 HRD-associated lncRNAs in LUAD. The signature might be the predictor for genomic instability and immunotherapy response in LUAD. Our findings provided new insight for the improvement of clinical stratification in LUAD.

Integrated analyzes identify CCT3 as a modulator to shape immunosuppressive tumor microenvironment in lung adenocarcinoma

BackgroundChaperonin-containing tailless complex polypeptide 1 (TCP1) subunit 3 (CCT3) has tumor-promoting effects in lung adenocarcinoma (LUAD). This study aims to investigate the molecular mechanisms of CCT3 in LUAD oncogenesis.MethodsThe UALCAN databases, Human Protein Atlas (HPA) and The Cancer Genome Atlas (TCGA) data were used to analyze CCT3 expression in LUAD. Both the Wilcoxon rank-sum test and the regression model were used to investigate the connection between clinicopathologic characteristics of LUAD patients and CCT3 expression. The prognostic value of CCT3 was determined by Cox regression models, the Kaplan-Meier method and Nomogram prediction. Next, we identified the most related genes with CCT3 via GeneMANIA and String databases, and the association between CCT3 and infiltrated immune cells using single-sample Gene Set Enrichment Analysis (ssGSEA). CCT3-related pathway enrichment analysis was investigated by GSEA. Finally, CCT3 roles in cell proliferation and apoptosis of LUAD A549 cells was verified by siRNA (small interfering RNA) mediated CCT3 knockdown.ResultsCCT3 was upregulated in LUAD both in mRNA and protein levels. CCT3 overexpression was associated with clinicopathological characteristics including sex, smoking, T- and N-categories, pathological staging, and a poor prognosis of LUAD patients. GeneMANIA and String databases found a set of CCT3-related genes that are connected to the assembly and stability of proteins involved in proteostasis of cytoskeletal filaments, DNA repair and protein methylation. Furthermore, CCT3 was found to be positively correlated with the infiltrating Th2 cells (r = 0.442, p < 0.01) while negatively correlated with mast cells (r = -0.49, p < 0.01) and immature dendritic cells (iDCs, r = -0.401, p < 0.001) according to ssGSEA analyzes. The pathway analysis based on GSEA method showed that the cell cycle pathway, the protein export pathway, the proteasome pathway and the ribosome pathway are enriched in CCT3 high group, whereas the JAK/STAT pathway, B cell receptor pathway, T cell receptor pathway and toll like receptor pathway were enriched in CCT3 low group. Finally, CCT3 knockdown substantially inhibited proliferation while promoted apoptosis of A549 cells.ConclusionIntegrated analyzes identify CCT3 as a modulator to shape immunosuppressive tumor microenvironment in LUAD and therefore, a prognostic factor for LUAD.

Identification of IRAK1BP1 as a candidate prognostic factor in lung adenocarcinoma.

Lung cancer is one of the major causes of cancer-related mortality worldwide. High-throughput RNA sequencing (RNA-seq) of surgically removed tumors has been used to identify new biomarkers of lung cancer; however, contamination by non-tumor cells in the tumor microenvironment significantly interferes with the search for novel biomarkers. Tumor organoids, as a pre-clinical cancer model, exhibit similar molecular characteristics with tumor samples while minimizing the interference from other cells. Here we analyzed six RNA-seq datasets collected from different organoid models, in which cells with oncogenic mutations were reprogrammed to mimic lung adenocarcinoma (LUAD) tumorigenesis. We uncovered 9 LUAD-specific biomarker genes by integrating transcriptomic data from multiple sources, and identified IRAK1BP1 as a novel predictor of LUAD disease outcome. Validation with RNA-seq and microarray data collected from multiple patient cohorts, as well as patient-derived xenograft (PDX) and lung cancer cell line models confirmed that IRAK1BP1 expression was significantly lower in tumor cells, and had no correlation with known markers oflung cancer prognosis. In addition, loss of IRAK1BP1 correlated with the group of LUAD patients with worse survival; and gene-set enrichment analysis using tumor and cell line data implicated that high IRAK1BP1 expression was associated with suppression of oncogenic pathways. In conclusion, we demonstrate that IRAK1BP1 is a promising biomarker of LUAD prognosis.

Budding uninhibited by benzimidazoles 1 overexpression is associated with poor prognosis and malignant phenotype: A promising therapeutic target for lung adenocarcinoma.

The budding uninhibited by benzimidazoles (BUB) family is involved in the cell cycle process as mitotic checkpoint components. Abnormal proliferation is a vital process in the development of lung adenocarcinoma (LUAD). Nevertheless, the roles of BUB1 in LUAD remain unclear. In this study, we evaluated the prognostic value and biological functions of BUB1 in LUAD using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), clinical LUAD samples, and in vitro experiments. The expression, prognostic significance, functions, immune infiltration, and methylation of BUB1 in LUAD were comprehensively analyzed using TCGA, GEO, Gene Expression Profiling Interactive Analysis, Metascape, cBioPortal, MethSurv, and cancerSEA databases. Furthermore, we performed a battery of in vitro experiments and immunohistochemistry (IHC) to verify the bioinformatics results. Multivariate analysis revealed that BUB1 overexpression was an independent prognostic factor (hazard ratio=1.499, p=0.013). Functional enrichment analysis showed that BUB1 was correlated with cell cycle, proliferation, DNA repair, DNA damage, and invasion (p < 0.05). Finally, in vitro experiments showed that downregulation of BUB1 inhibited the proliferation, migration, and invasion of LUAD cells and promoted LUAD cell apoptosis. IHC also showed that BUB1 was overexpressed in LUAD (p < 0.001) and was significantly associated with poor prognosis (p < 0.001). Our bioinformatics and IHC analyses revealed that BUB1 overexpression was an adverse prognostic factor in LUAD. In vitro experiments demonstrated that BUB1 promoted tumor cell proliferation, migration, and invasion in LUAD. These results indicated that BUB1 was a promising biomarker and potential therapeutic target in LUAD.

Clinicopathological Significance of DUB3 Expression in Non-small Cell Lung Cancer and Relationship Between DUB3 Expression and LATS1 Expression.

Deubiquitinating enzyme 3 (DUB3) is a member of the ubiquitin-specific proteases family involved in regulating cell proliferation, invasion, and apoptosis. However, the biological role and clinicopathological significance of DUB3 expression have not been elucidated in non-small cell lung cancer (NSCLC). We evaluated the expression of DUB3 by immunohistochemistry using tissue microarrays and assessed the clinicopathologic significance of DUB3 expression levels in 187 patients with NSCLC, including its two major subtypes (93 cases of adenocarcinoma and 72 cases of squamous cell carcinoma). In adenocarcinoma, we observed that DUB3 expression had an effect on tumor size (p=0.030), vessel invasion (p=0.038), T stage (p=0.014), and tumor recurrence (p=0.002). Kaplan-Meier curves with log-rank test showed that high DUB3 expression was correlated with significantly more favorable clinical outcomes compared to those of the low expression group in adenocarcinoma (p=0.013). Multivariate analysis of disease-free survival also demonstrated that DUB3 expression is an independent prognostic factor in lung adenocarcinoma (p=0.017). Additionally, we identified the correlation between DUB3 and the expression of large tumor suppressor kinase 1 expression, a core protein of the Hippo pathway. DUB3 could function as a tumor suppressor by regulating the Hippo pathway in lung adenocarcinoma and can be considered a powerful predictive factor and therapeutic target.

Cyclin B1 expression as an independent prognostic factor for lung adenocarcinoma and its potential pathways.

Although great progress has been made in the early diagnosis and targeted therapy of lung adenocarcinoma (LUAD), the survival of patients with LUAD remains unsatisfactory. There is an urgent requirement for new biomarkers to guide the diagnosis, prognosis and treatment of LUAD. Following an initial bioinformatics screen, the present study focused on cyclin B1 (CCNB1) in LUAD. A total of 94 patients with LUAD from a single hospital were included in the study. CCNB1 protein expression was detected and scored in 94 LUAD samples and 30 normal tissue samples by immunohistochemistry. The associations between CCNB1 expression and the clinicopathological features of the patients with LUAD were analyzed. Furthermore, the relationship between prognosis and the CCNB1 expression level was analyzed using Cox regression and survival analyses. Weighted gene co-expression network analysis and RNA-sequencing were also applied to identify the potential molecular mechanisms of CCNB1 in LUAD. CCNB1 was highly expressed in patients with LUAD and was associated with poor prognosis. It may affect the expression of CPLX1, PPIF, SRPK2, KRT8, SLC20A1 and CBX2 genes and function via different pathways. CCNB1 has the potential to become a novel prognostic target for LUAD.

The role of radiotherapy-related autophagy genes in the prognosis and immune infiltration in lung adenocarcinoma.

BackgroundThere is a close relationship between radiotherapy and autophagy in tumors, but the prognostic role of radiotherapy-related autophagy genes (RRAGs) in lung adenocarcinoma (LUAD) remains unclear.MethodsData used in the current study were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted gene co-expression network analysis (WGCNA) was executed to recognize module genes associated with radiotherapy. The differentially expressed genes (DEGs) between different radiotherapy response groups were filtered via edgeR package. The differentially expressed radiotherapy-related autophagy genes (DERRAGs) were obtained by overlapping the module genes, DEGs, and autophagy genes (ATGs). Then, prognostic autophagy genes were selected by Cox analyses, and a risk model and nomogram were subsequently built. Gene Set Enrichment Analysis (GSEA) and single-sample Gene Set Enrichment Analysis (ssGSEA) were performed to investigate potential mechanisms through which prognostic autophagy signatures regulate LUAD. Radiotherapy-resistant cell lines (A549IR and PC9IR) were established after exposure to hypo-fractionated irradiation. Ultimately, mRNA expression was validated by quantitative real-time PCR (qRT-PCR), and relative protein levels were measured in different cell lines by western blot.ResultsA total of 11 DERRAGs were identified in LUAD. After Cox analyses, SHC1, NAPSA, and AURKA were filtered as prognostic signatures in LUAD. Then, the risk score model was constructed using the prognostic signatures, which had a good performance in predicting the prognosis, as evidenced by receiver operating characteristics curves. Furthermore, Cox regression analyses demonstrated that risk score was deemed as an independent prognostic factor in LUAD. Moreover, GSEA and ssGSEA results revealed that prognostic RRAGs may regulate LUAD by modulating the immune microenvironment and affecting cell proliferation. The colony formation assay showed that the radiosensitivity of radiation-resistant cell lines was lower than that of primary cells. The western blot assay found that the levels of autophagy were elevated in the radiotherapy-resistant cell lines. Moreover, the expression of DERRAGs (SHC1, AURKA) was higher in the radiotherapy-resistant cells than in primary cells.ConclusionOur study explored the role of RRAGs in the prognosis of LUAD and identified three biomarkers. The findings enhanced the understanding of the relationship between radiotherapy, autophagy, and prognosis in LUAD and provided potential therapeutic targets for LUAD patients.