Nicotinamide in Combination with EGFR-TKIs for the Treatment of Stage IV Lung Adenocarcinoma with EGFR Mutations: A Randomized Double-Blind (Phase IIb) Trial.

PURPOSE: RUNX3 is a tumor suppressor gene, which is inactivated in approximately 70% of lung adenocarcinomas. Nicotinamide, a sirtuin inhibitor, has demonstrated potential in re-activating epigenetically silenced RUNX3 in cancer cells. This study assessed the therapeutic benefits of combining nicotinamide with first-generation EGFR-tyrosine kinase inhibitors (TKI) for patients with stage IV lung cancer carrying EGFR mutations. PATIENTS AND METHODS: We assessed the impact of nicotinamide on carcinogen-induced lung adenocarcinomas in mice and observed that nicotinamide increased RUNX3 levels and inhibited lung cancer growth. Subsequently, 110 consecutive patients with stage IV lung cancer who had EGFR mutations were recruited: 70 females (63.6%) and 84 never-smokers (76.4%). The patients were randomly assigned to receive either nicotinamide (1 g/day, n = 55) or placebo (n = 55). The primary and secondary endpoints were progression-free survival (PFS) and overall survival (OS), respectively. RESULTS: After a median follow-up of 54.3 months, the nicotinamide group exhibited a median PFS of 12.7 months [95% confidence interval (CI), 10.4-18.3], while the placebo group had a PFS of 10.9 months (9.0-13.2; P = 0.2). The median OS was similar in the two groups (31.0 months with nicotinamide vs. 29.4 months with placebo; P = 0.2). Notably, subgroup analyses revealed a significant reduction in mortality risk for females (P = 0.01) and never-smokers (P = 0.03) treated with nicotinamide. CONCLUSIONS: The addition of nicotinamide with EGFR-TKIs demonstrated potential improvements in PFS and OS, with notable survival benefits for female patients and those who had never smoked (ClinicalTrials.gov Identifier: NCT02416739).

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.

Background: Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined. Methods: We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD. Results: Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion. Conclusion: Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

Exploring the Mechanism of Zilongjin in Treating Lung Adenocarcinoma Based on Network Pharmacology Combined with Experimental Verification.

Zilongjin (ZLJ) is a common traditional Chinese medicine for lung adenocarcinoma (LUAD) treatment. However, its mechanisms of action remain to be elucidated. Network pharmacology was used to explore the underlying mechanisms of ZLJ on LUAD treatment. The disease-related targets were determined from the Gene-Cards and DisGeNET databases. Active compounds and targets of ZLJ were obtained from the HIT, TCMSP, and TCMID databases. Then the protein-protein interaction (PPI) network was built by the STRING database to identify core-hub targets of ZLJ in LUAD. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were employed to analyze the enriched regulatory pathways of targets. Molecular docking analysis was used to evaluate interactions between potential targets and active compounds. Finally, qRT-PCR was used to further verify the results of network pharmacology. A total of 124 LUAD-related targets of ZLJ and 5 active compounds of ZLJ from the relevant databases were screened out. Among these target proteins, JUN, CDH1, PPARG, and FOS were core hub-genes in the PPI network. GO and KEGG pathway enrichment analysis indicated that these targets might regulate the PPAR signaling pathway in LUAD. JUN, PPARG, and FOS levels were upregulated, while CDH1 level was downregulated in LUAD cells. This study discerned that ZLJ may target genes such as JUN, FOS, PPARG, and CDH1 via the PPAR signaling pathway in LUAD, offering foundational insights for further exploration of ZLJ in clinical applications.

Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.

Comprehensive Analysis of Lung Adenocarcinoma and Brain Metastasis through Integrated Single-Cell Transcriptomics.

Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer, comprising approximately half of all cases. It is often diagnosed at advanced stages with brain metastasis (BM), resulting in high mortality rates. Current BM management involves complex interventions and conventional therapies that offer limited survival benefits with neurotoxic side effects. The tumor microenvironment (TME) is a complex system where cancer cells interact with various elements, significantly influencing tumor behavior. Immunotherapies, particularly immune checkpoint inhibitors, target the TME for cancer treatment. Despite their effectiveness, it is crucial to understand metastatic lung cancer and the specific characteristics of the TME, including cell-cell communication mechanisms, to refine treatments. Herein, we investigated the tumor microenvironment of brain metastasis from lung adenocarcinoma (LUAD-BM) and primary tumors across various stages (I, II, III, and IV) using single-cell RNA sequencing (scRNA-seq) from publicly available datasets. Our analysis included exploring the immune and non-immune cell composition and the expression profiles and functions of cell type-specific genes, and investigating the interactions between different cells within the TME. Our results showed that T cells constitute the majority of immune cells present in primary tumors, whereas microglia represent the most dominant immune cell type in BM. Interestingly, microglia exhibit a significant increase in the COX pathway. Moreover, we have shown that microglia primarily interact with oligodendrocytes and endothelial cells. One significant interaction was identified between DLL4 and NOTCH4, which demonstrated a relevant association between endothelial cells and microglia and between microglia and oligodendrocytes. Finally, we observed that several genes within the HLA complex are suppressed in BM tissue. Our study reveals the complex molecular and cellular dynamics of BM-LUAD, providing a path for improved patient outcomes with personalized treatments and immunotherapies.

Dichotomous Effects of Glypican-4 on Cancer Progression and Its Crosstalk with Oncogenes.

Glypicans are linked to various aspects of neoplastic behavior, and their therapeutic value has been proposed in different cancers. Here, we have systematically assessed the impact of GPC4 on cancer progression through functional genomics and transcriptomic analyses across a broad range of cancers. Survival analysis using TCGA cancer patient data reveals divergent effects of GPC4 expression across various cancer types, revealing elevated GPC4 expression levels to be associated with both poor and favorable prognoses in a cancer-dependent manner. Detailed investigation of the role of GPC4 in glioblastoma and non-small cell lung adenocarcinoma by genetic perturbation studies displays opposing effects on these cancers, where the knockout of GPC4 with CRISPR/Cas9 attenuated proliferation of glioblastoma and augmented proliferation of lung adenocarcinoma cells and the overexpression of GPC4 exhibited a significant and opposite effect. Further, the overexpression of GPC4 in GPC4-knocked-down glioblastoma cells restored the proliferation, indicating its mitogenic effect in this cancer type. Additionally, a survival analysis of TCGA patient data substantiated these findings, revealing an association between elevated levels of GPC4 and a poor prognosis in glioblastoma, while indicating a favorable outcome in lung carcinoma patients. Finally, through transcriptomic analysis, we attempted to assign mechanisms of action to GPC4, as we find it implicated in cell cycle control and survival core pathways. The analysis revealed upregulation of oncogenes, including FGF5, TGF-ß superfamily members, and ITGA-5 in glioblastoma, which were downregulated in lung adenocarcinoma patients. Our findings illuminate the pleiotropic effect of GPC4 in cancer, underscoring its potential as a putative prognostic biomarker and indicating its therapeutic implications in a cancer type dependent manner.

Integrated analysis highlights the significance role of ITGAL in lung adenocarcinoma.

Integrin alpha L (ITGAL), a member of the integrin family, is associated with carcinogenesis and immune regulation. However, the biological functions of ITGAL in lung adenocarcinoma (LUAD) remain poorly understood. In this study, we utilized the TCGA dataset to analyse ITGAL mRNA expression in LUAD and examined its correlation with clinical prognosis. Three-dimensional (3D) Matrigel culture, 5-bromodeoxyuridine (BrdU) ELISA, wound-healing migration and cell adherence assays were used to demonstrate the potential role of ITGAL in LUAD progression. Additionally, we analysed single-cell sequencing data of LUAD to determine the expression and biological function of ITGAL. Our research revealed that the expression of ITGAL in LUAD samples is an independent predictor of prognosis. Patients with high expression of ITGAL had significantly better overall survival (OS), progression-free survival (PFS) and disease-specific survival (DSS) compared to the low-expression group. Meanwhile, the expression of ITGAL suppressed malignant progression in LUAD cells. Functional enrichment analyses showed that ITGAL was significantly correlated with cell immune response and immune checkpoint, consistent with the analysis of single-cell sequencing in paired samples of normal and tumour. Furthermore, we confirmed that ITGAL expression affect the tumour microenvironment (TME) through regulation of the expression of cytokines in NK cells of LUAD. In summary, ITGAL is a prognostic biomarker for LUAD patients, and it repressed malignant progression in LUAD cells. Moreover, ITGAL expression also enhanced the effect of immunotherapy and may be an important target in LUAD therapy.

miR-125b reverses cisplatin resistance by regulating autophagy via targeting RORA/BNIP3L axis in lung adenocarcinoma.

The platinum-based chemotherapy is one of the most frequently used treatment protocols for lung adenocarcinoma (LUAD), and chemoresistance, however, usually results in treatment failure and limits its application in the clinic. It has been shown that microRNAs (miRNAs) play a significant role in tumor chemoresistance. In this study, miR-125b was identified as a specific cisplatin (DDP)-resistant gene in LUAD, as indicated by the bioinformatics analysis and the real-time quantitative PCR assay. The decreased serum level of miR-125b in LUAD patients was correlated with the poor treatment response rate and short survival time. MiR-125b decreased the A549/DDP proliferation, and the multiple drug resistance- and autophagy-related protein expression levels, which were all reversed by the inhibition of miR-125b. In addition, xenografts of human tumors in nude mice were suppressed by miR-125b, demonstrating that through autophagy regulation, miR-125b could reverse the DDP resistance in LUAD cells, both in vitro and in vivo. Further mechanistic studies indicated that miR-125b directly repressed the expression levels of RORA and its downstream BNIP3L, which in turn inhibited autophagy and reversed chemoresistance. Based on these findings, miR-125b in combination with DDP might be an effective treatment option to overcome DDP resistance in LUAD.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy.

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

CDC45 promotes the stemness and metastasis in lung adenocarcinoma by affecting the cell cycle.

OBJECTIVE: This study aimed to assess the functions of cell division cycle protein 45 (CDC45) in Non-small cell lung cancer (NSCLC) cancer and its effects on stemness and metastasis. METHODS: Firstly, differentially expressed genes related to lung cancer metastasis and stemness were screened by differential analysis and lasso regression. Then, in vitro, experiments such as colony formation assay, scratch assay, and transwell assay were conducted to evaluate the impact of CDC45 knockdown on the proliferation and migration abilities of lung cancer cells. Western blotting was used to measure the expression levels of related proteins and investigate the regulation of CDC45 on the cell cycle. Finally, in vivo model with subcutaneous injection of lung cancer cells was performed to verify the effect of CDC45 on tumor growth. RESULTS: This study identified CDC45 as a key gene potentially influencing tumor stemness and lymph node metastasis. Knockdown of CDC45 not only suppressed the proliferation and migration abilities of lung cancer cells but also caused cell cycle arrest at the G2/M phase. Further analysis revealed a negative correlation between CDC45 and cell cycle-related proteins, stemness-related markers, and tumor mutations. Mouse experiments confirmed that CDC45 knockdown inhibited tumor growth. CONCLUSION: As a novel regulator of stemness, CDC45 plays a role in regulating lung cancer cell proliferation, migration, and cell cycle. Therefore, CDC45 may serve as a potential target for lung cancer treatment and provide a reference for further mechanistic research and therapeutic development.

[First-line Treatment with Furmonertinib Mesylate in Lung Adenocarcinoma Patient 
with EGFR Exon 20 Insertion Mutantion].

With the continuous innovation of genomics, proteomics and molecular biological detection technology, the treatment of non-small cell lung cancer (NSCLC) has changed from traditional chemotherapy to immunotherapy and targeted therapy. Among them, molecular tumor markers targeting tyrosine kinase pathways play more important roles in clinical practice. For advanced NSCLC patients with positive epidermal growth factor receptor (EGFR) mutations, there are many first-line drugs on the market and they could bring significant efficacy, thus completely subverting the treatment pattern of advanced NSCLC. Common mutations of EGFR in Chinese patients are located on exons 19, 20 and 21, of which exons 19 and 21 mutations are the more common types. Besides, there is also a subtype of EGFR mutations, known as EGFR 20 exon insertion (EGFR 20ins) mutation. The authors summarized the treatment of a lung adenocarcinoma patient with EGFR 20ins mutation accepting Furmonertinib mesylate, in order to provide effective references for clinical diagnosis and treatment.
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Overexpression of TNFSF11 reduces GPX4 levels and increases sensitivity to ferroptosis inducers in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD), the most common and lethal subtype of lung cancer, continues to be a major health concern worldwide. Despite advances in targeted and immune therapies, only a minority of patients derive substantial benefits. As a result, the urgent need for novel therapeutic strategies to improve lung cancer treatment outcomes remains undiminished. METHODS: In our study, we employed the TIMER database to scrutinize TNFSF11 expression across various cancer types. We further examined the differential expression of TNFSF11 in normal and tumor tissues utilizing the TCGA-LUAD dataset and tissue microarray, and probed the associations between TNFSF11 expression and clinicopathological parameters within the TCGA-LUAD dataset. We used the GSE31210 dataset for external validation. To identify genes strongly linked to TNFSF11, we engaged LinkedOmics and conducted a KEGG pathway enrichment analysis using the WEB-based Gene SeT AnaLysis Toolkit. Moreover, we investigated the function of TNFSF11 through gene knockdown or overexpression approaches and explore its function in tumor cells. The therapeutic impact of ferroptosis inducers in tumors overexpressing TNFSF11 were also investigated through in vivo and in vitro experiments. Through these extensive analyses, we shed light on the potential role of TNFSF11 in lung adenocarcinoma, underscoring potential therapeutic targets for this malignancy. RESULTS: This research uncovers the overexpression of TNFSF11 in LUAD patients and its inverse correlation with peroxisome-related enzymes. By utilizing gene knockdown or overexpression assays, we found that TNFSF11 was negatively associated with GPX4. Furthermore, cells with TNFSF11 overexpression were relatively more sensitive to the ferroptosis inducers. CONCLUSIONS: Our research has provided valuable insights into the role of TNFSF11, revealing its negative regulation of GPX4, which could be influential in crafting therapeutic strategies. These findings set the stage for further exploration into the mechanisms underpinning the relationship between TNFSF11 and GPX4, potentially opening up new avenues for precision medicine in the treatment of LUAD.

Purine metabolism in lung adenocarcinoma: A single-cell analysis revealing prognostic and immunotherapeutic insights.

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer, yet the contribution of purine metabolism (PM) to its pathogenesis remains poorly elucidated. PM, a critical component of intracellular nucleotide synthesis and energy metabolism, is hypothesized to exert a significant influence on LUAD development. Herein, we employed single-cell analysis to investigate the role of PM within the tumour microenvironment (TME) of LUAD. PM scoring (PMS) across distinct cell types was determined using AUCell, UCell, singscore and AddModuleScore algorithms. Subsequently, we explored communication networks among cells within high- and low-PMS groups, establishing a robust PM-associated signature (PAS) utilizing a comprehensive dataset comprising LUAD samples from TCGA and five GEO datasets. Our findings revealed that the high-PMS group exhibited intensified cell interactions, while the PAS, constructed using PM-related genes, demonstrated precise prognostic predictive capability. Notably, analysis across the TCGA dataset and five GEO datasets indicated that low-PAS patients exhibited a superior prognosis. Furthermore, the low-PAS group displayed increased immune cell infiltration and elevated CD8A expression, coupled with reduced PD-L1 expression. Moreover, data from eight publicly available immunotherapy cohorts suggested enhanced immunotherapy outcomes in the low-PAS group. These results underscore a close association between PAS and tumour immunity, offering predictive insights into genomic alterations, chemotherapy drug sensitivity and immunotherapy responses in LUAD. The newly established PAS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

Trametinib sensitizes KRAS-mutant lung adenocarcinoma tumors to PD-1/PD-L1 axis blockade via Id1 downregulation.

BACKGROUND: The identification of novel therapeutic strategies to overcome resistance to the MEK inhibitor trametinib in mutant KRAS lung adenocarcinoma (LUAD) is a challenge. This study analyzes the effects of trametinib on Id1 protein, a key factor involved in the KRAS oncogenic pathway, and investigates the role of Id1 in the acquired resistance to trametinib as well as the synergistic anticancer effect of trametinib combined with immunotherapy in KRAS-mutant LUAD. METHODS: We evaluated the effects of trametinib on KRAS-mutant LUAD by Western blot, RNA-seq and different syngeneic mouse models. Genetic modulation of Id1 expression was performed in KRAS-mutant LUAD cells by lentiviral or retroviral transductions of specific vectors. Cell viability was assessed by cell proliferation and colony formation assays. PD-L1 expression and apoptosis were measured by flow cytometry. The anti-tumor efficacy of the combined treatment with trametinib and PD-1 blockade was investigated in KRAS-mutant LUAD mouse models, and the effects on the tumor immune infiltrate were analyzed by flow cytometry and immunohistochemistry. RESULTS: We found that trametinib activates the proteasome-ubiquitin system to downregulate Id1 in KRAS-mutant LUAD tumors. Moreover, we found that Id1 plays a major role in the acquired resistance to trametinib treatment in KRAS-mutant LUAD cells. Using two preclinical syngeneic KRAS-mutant LUAD mouse models, we found that trametinib synergizes with PD-1/PD-L1 blockade to hamper lung cancer progression and increase survival. This anti-tumor activity depended on trametinib-mediated Id1 reduction and was associated with a less immunosuppressive tumor microenvironment and increased PD-L1 expression on tumor cells. CONCLUSIONS: Our data demonstrate that Id1 expression is involved in the resistance to trametinib and in the synergistic effect of trametinib with anti-PD-1 therapy in KRAS-mutant LUAD tumors. These findings suggest a potential therapeutic approach for immunotherapy-refractory KRAS-mutant lung cancers.

IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show that IBPGNET outperforms other classification methods in 5-fold cross-validation. IBPGNET identified PSMC1 and PSMD11 as genes associated with LUAD recurrence, and their expression levels were significantly higher in LUAD cells than in normal cells. The knockdown of PSMC1 and PSMD11 in LUAD cells increased their sensitivity to afatinib and decreased cell migration, invasion and proliferation. In addition, the cells showed significantly lower EGFR expression, indicating that PSMC1 and PSMD11 may mediate therapeutic sensitivity through EGFR expression.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization.

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

NPAS2, transcriptionally activated by ARRB1, promotes the malignant behaviours of lung adenocarcinoma cells and regulates the reprogramming of glucose metabolism.

Lung adenocarcinoma (LUAD) is a serious threat to public health and is accompanied by increased morbidity and mortality worldwide. Neuronal PAS domain protein2 (NPAS2) has been confirmed as an oncogene in LUAD; however, little is known about its molecular mechanism. Here, the expression level of NPAS2 was detected in LUAD cell lines and 16HBE cells. Gain- and loss-of-function experiments were performed. Cell Counting Kit-8, colony formation, flow cytometry, wound-healing and Transwell assays were conducted to assess cell proliferation, apoptosis, migration and invasion, respectively. Reprogramming of glucose metabolism was evaluated via oxygen consumption rate (OCR), complexes activities, lactic production and glucose consumption. The expression of critical proteins was examined by western blot. We demonstrated aberrant upregulation of NPAS2 and ß-arrestin-1 (ARRB1) in LUAD cell lines. ARRB1 was found to be a critical transcription factor of NPAS2 with binding sites within the promoter region of NPAS2, thereby causing its transcriptional activation. Functional experiments revealed that NPAS2 depletion significantly inhibited the malignant behaviours of A549 cells by suppressing cell proliferation, migration, invasion and epithelial-mesenchymal transition and promoting cell apoptosis. Meanwhile, NPAS2 depletion increased OCR and activities of complexes (I, II, III and V), and reduced lactic acid production and glucose uptake in A549 cells, indicating that NPAS2 depletion inhibited aerobic glycolysis, accompanied by reduced expression of glycolytic enzymes. However, the changes caused by NPAS2 knockdown were partly restored by ARRB1 overexpression. In conclusion, our study suggests that ARRB1 could transcriptionally activate NPAS2, facilitating malignant activities and glycolysis, and ultimately promoting the progression of LUAD, proving a novel therapeutic strategy for the treatment of LUAD.

Redoxhigh phenotype mediated by KEAP1/STK11/SMARCA4/NRF2 mutations diminishes tissue-resident memory CD8+ T cells and attenuates the efficacy of immunotherapy in lung adenocarcinoma.

Metabolism reprogramming within the tumor microenvironment (TME) can have a profound impact on immune cells. Identifying the association between metabolic phenotypes and immune cells in lung adenocarcinoma (LUAD) may reveal mechanisms of resistance to immune checkpoint inhibitors (ICIs). Metabolic phenotypes were classified by expression of metabolic genes. Somatic mutations and transcriptomic features were compared across the different metabolic phenotypes. The metabolic phenotype of LUAD is predominantly determined by reductase-oxidative activity and is divided into two categories: redoxhigh LUAD and redoxlow LUAD. Genetically, redoxhigh LUAD is mainly driven by mutations in KEAP1, STK11, NRF2, or SMARCA4. These mutations are more prevalent in redoxhigh LUAD (72.5%) compared to redoxlow LUAD (17.4%), whereas EGFR mutations are more common in redoxlow LUAD (19.0% vs. 0.7%). Single-cell RNA profiling of pre-treatment and post-treatment samples from patients receiving neoadjuvant chemoimmunotherapy revealed that tissue-resident memory CD8+ T cells are responders to ICIs. However, these cells are significantly reduced in redoxhigh LUAD. The redoxhigh phenotype is primarily attributed to tumor cells and is positively associated with mTORC1 signaling. LUAD with the redoxhigh phenotype demonstrates a lower response rate (39.1% vs. 70.8%, p = 0.001), shorter progression-free survival (3.3 vs. 14.6 months, p = 0.004), and overall survival (12.1 vs. 31.2 months, p = 0.022) when treated with ICIs. The redoxhigh phenotype in LUAD is predominantly driven by mutations in KEAP1, STK11, NRF2, and SMARCA4. This phenotype diminishes the number of tissue-resident memory CD8+ T cells and attenuates the efficacy of ICIs.

Prognostic and onco-immunological value of immune-related eRNAs-driven genes in lung adenocarcinoma.

BACKGROUND: We aimed to comprehensively analyze the clinical value of immune-related eRNAs-driven genes in lung adenocarcinoma (LUAD) and find the potential biomarkers for prognosis and therapeutic response to improve the survival of this malignant disease. MATERIALS AND METHODS: Pearson's correlation analysis was performed to identify the immune-related eRNAs-driven genes. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were used to construct this prognostic risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to investigate the underlying molecular mechanism. The single sample gene set enrichment analysis (ssGSEA) algorithm was conducted to evaluate the immune status based on the signature. The quantitative real-time PCR (qRT-PCR) analysis was performed to evaluate the expression value of the signature genes between LUAD tissues and adjacent lung tissues. RESULTS: Five immune-related eRNAs-driven genes (SHC1, GDF10, CCL14, FYN, and NOD1) were identified to construct a prognostic risk signature with favorable predictive capacity. The patients with high-risk scores based on the signature were significantly associated with the malignant clinical features compared with those with low-risk scores. Kaplan-Meier analysis demonstrated that the sample in the low-risk group had a prolonged survival compared with those in the high-risk group. This risk signature was validated to have a promising predictive capacity and reliability in diverse clinical situations and independent cohorts. The functional enrichment analysis demonstrated that humoral immune response and intestinal immune network for IgA production pathway might be the underlying molecular mechanism related to the signature. The proportion of the vast majority of immune infiltrating cells in the high-risk group was significantly lower than that in the low-risk group, and the immunotherapy response rate in the low-risk group was significantly higher than that in the high-risk group. Moreover, BI-2536, sepantronium bromide, and ULK1 were the potential drugs for the treatment of patients with higher risk scores. Finally, the experiment in vivo and database analysis indicated that CCL14, FYN, NOD1, and GDF10 are the potential LUAD suppressor and SHC1 is a potential treatment target for LUAD. CONCLUSION: Above all, we constructed a prognostic risk signature with favorable predictive capacity in LUAD, which was significantly associated with malignant features, immunosuppressive tumor microenvironment, and immunotherapy response and may provide clinical benefit in clinical decisions.