Survival In Lung Adenocarcinoma Research Articles

Deacetylation of GLUD1 maintains the survival of lung adenocarcinoma cells under glucose starvation by inhibiting autophagic cell death

Enhanced glutamine catabolism is one of the main metabolic features of cancer, providing energy and intermediate metabolites for cancer progression. However, the functions of glutamine catabolism in cancer under nutrient deprivation need to be further clarified. Here, we discovered that deacetylation of glutamate dehydrogenase 1 (GLUD1), one of the key enzymes in glutamine catabolism, maintains the survival of lung adenocarcinoma (LUAD) cells under glucose starvation by inhibiting autophagic cell death. We found that glucose starvation increased GLUD1 activity by reducing its acetylation on Lys84 and promoted its active hexamer formation. Besides, deacetylation of GLUD1 induced its cytoplasmic localization, where GLUD1 was ubiquitinated in K63-linkage by TRIM21, leading to the binding of GLUD1 with cytoplasmic glutaminase KGA. These two effects enhanced glutamine metabolism both in mitochondria and cytoplasm, increased the production of alpha-ketoglutarate (α-KG). Meanwhile, cytoplasmic GLUD1 also interacted with p62 and prevented its acetylation, leading to the inhibition of p62 body formation. All these effects blocked autophagic cell death of LUAD cells under glucose starvation. Taken together, our results reveal a novel function of GLUD1 under glucose deprivation in LUAD cells and provide new insights into the functions of glutamine catabolism during cancer progression.

Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma.

Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in the regulation of key cellular processes might affect LUAD risk and survival outcomes, we built individual (person)-specific gene regulatory networks integrating gene expression, transcription factor protein-protein interaction, and sequence motif data, using PANDA/LIONESS algorithms, for both non-cancerous lung tissue samples from the Genotype Tissue Expression (GTEx) project and LUAD samples from The Cancer Genome Atlas (TCGA). In GTEx, we found that pathways involved in cell proliferation and immune response are increasingly targeted by regulatory transcription factors with age; these aging-associated alterations are accelerated by tobacco smoking and resemble oncogenic shifts in the regulatory landscape observed in LUAD and suggests that dysregulation of aging pathways might be associated with an increased risk of LUAD. Comparing normal adjacent samples from individuals with LUAD with healthy lung tissue samples from those without LUAD, we found that aging-associated genes show greater aging-biased targeting patterns in younger individuals with LUAD compared to their healthy counterparts of similar age, a pattern suggestive of age acceleration. This implies that an accelerated aging process may be responsible for tumor incidence in younger individuals. Using drug repurposing tool CLUEreg, we found small molecule drugs with potential geroprotective effects that may alter the accelerating aging profiles we found. We also observed that, in contrast to chronological age, a network-informed aging signature was associated with survival and response to chemotherapy in LUAD.

Identification of Stem Cell-related Gene Markers by Comprehensive Transcriptome Analysis to Predict the Prognosis and Immunotherapy of Lung Adenocarcinoma.

Cancer stem cells (CSCs) contribute to metastasis and drug resistance to immunotherapy in lung adenocarcinoma (LUAD), so the stemness evaluation of cancer cells is of great significance. The single-cell RNA sequencing (scRNA-seq) data of the GSE149655 dataset were collected and analyzed. Malignant cells were distinguished by CopyKAT. CytoTRACE score of marker genes in malignant cells was counted by CytoTRACE to construct the stemness score formula. Sample stemness score in TCGA was determined by the formula and divided into high-, medium- and low-stemness score groups. LASSO and COX regression analyses were carried out to screen the key genes related to the prognosis of LUAD from the differentially expressed genes (DEGs) in high- and low-stemness score groups and a risk score model was constructed. Seven types of cells were identified from a total of 4 samples, and 193 marker genes of 3455 malignant cells were identified. There were 1098 DEGs between low- and high-stemness score groups of TCGA, of which CPS1, CENPK, GJB3, and TPSB2 constituted gene signatures. The 4-gene signature could independently evaluate LUAD survival in the training and validation sets and showed an acceptable area under the receiver operator characteristic (ROC) curves (AUCs). This study provides insights into the cellular heterogeneity of LUAD and develops a new cancer stemness evaluation indicator and a 4-gene signature as a potential tool for evaluating the response of LUAD to immune checkpoint blockade (ICB) therapy or antineoplastic therapy.

Gene signatures of endoplasmic reticulum stress and mitophagy for prognostic risk prediction in lung adenocarcinoma.

Genes associated with endoplasmic reticulum stress (ERS) and mitophagy can be conducive to predicting solid tumour prognosis. The authors aimed to develop a prognosis prediction model for these genes in lung adenocarcinoma (LUAD). Relevant gene expression and clinical information were collected from public databases including Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A total of 265 differentially expressed genes was finally selected (71 up-regulated and 194 downregulated) in the LUAD dataset. Among these, 15 candidate ERS and mitophagy genes (ATG12, CSNK2A1, MAP1LC3A, MAP1LC3B, MFN2, PGAM5, PINK1, RPS27A, SQSTM1, SRC, UBA52, UBB, UBC, ULK1, and VDAC1) might be critical to LUAD based on the expression analysis after crossing with the ERS and mitochondrial autophagy genes. The prediction model demonstrated the ability to effectively predict the 5-, 3-, and 1-year prognoses of LUAD patients in both GEO and TCGA databases. Moreover, high VDAC1 expression was associated with poor overall survival in LUAD (p<0.001), suggesting it might be a critical gene for LUAD prognosis prediction. Overall, the prognosis model based on ERS and mitophagy genes in LUAD can be useful for evaluating the prognosis of patients with LUAD, and VDAC1 may serve as a promising biomarker for LUAD prognosis.

NECTIN2 is a prognostic biomarker and potential therapeutic target in lung adenocarcinoma

IntroductionNECTINs are transmembrane proteins mediating cell-to-cell adhesion. NECTINs interact with integrins or other membrane receptors to trigger multiple cellular functions. Aberrant NECTIN expression is associated with cancer progression and poor outcomes. While NECTIN2 is overexpressed in various cancer types, its role in lung cancer is not well understood. Material and methodsWe investigated the function of NECTIN2 in lung adenocarcinoma (LUAD) using the Cancer Genome Atlas (TCGA) dataset and clinical samples of 105 LUAD patients who had undergone surgical resection. Cell proliferation, apoptosis, migration and invasion were investigated using human lung adenocarcinoma cell lines. ResultsWe found that high NECTIN2 expression correlated with reduced overall survival in LUAD in TCGA database. In clinical samples, high NECTIN2 expression was associated with lower recurrence-free survival in all patients (P < 0.001) and in stage I patients (P = 0.001). Functional analyses demonstrated that NECTIN2 knockout promoted cell apoptosis and diminished cell proliferation and migration capacity. NECTIN2 overexpression did not significantly affect cellular functions. DiscussionOur results suggest that NECTIN2 plays a significant role in cell apoptosis and cancer cell migration, leading to increased postoperative recurrence. Furthermore, NECTIN2 serves as a prognostic indicator and a potential therapeutic target in LUAD. ConclusionsHigh NECTIN2 expression in LUAD was found to be associated with postoperative recurrence, and was observed to play an important role in cell apoptosis and migration.

Prognostic value and immunological function of cuproptosis-related genes in lung adenocarcinoma

BackgroundLung cancer is one of the malignant tumors with the highest morbidity and fatality rates worldwide. The overall survival (OS) of lung adenocarcinoma (LUAD) is poor. Cuproptosis is a copper-triggered modality of mitochondrial cell death, however, its contribution to the emergence of lung cancer is unknown. The clinical implication and immunological function of cuproptosis-related genes (CRGs) in LUAD has yet to be established. MethodsTCGA, HPA, GEPIA, Kaplan-Meier, TIMER and CancerSEA database were used to explore the prognostic value and biological function of CRGs in LUAD. ResultsCRGs are primarily involved in copper ion transport, the citrate cycle (TCA cycle) and central carbon metabolism in LUAD. The mRNA expression of COA6, UBE2D1, DLAT, SLC25A3, and DBH was significantly increased. The expression of COA6, DLAT, SLC25A3, DBH, and LOXL2 were all strongly associated with the clinicopathological stages in LUAD. Moreover, high expression of COA6, UBE2D1, DLAT, SLC25A3 and LOXL2 was related to poor OS. The expression of SLC25A3 and LOXL2 showed different association with immune cell infiltration. The single cell sequencing demonstrated that CRGs play important roles in the regulation of DNA damage response, inflammation and metastasis in LUAD. ConclusionsIn summary, this study comprehensively uncovered that CRGs could be identified as potential prognostic and immunological biomarkers in LUAD. Our current research could provide a solid theoretical basis for LUAD survival research and clinical decision-making.

Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning

Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tailoring postoperative treatment plans. Addressing the challenging task of accurate RFS prediction using CT images, this paper introduces an innovative approach based on self-supervised pre-training and multi-task learning. We employed a self-supervised learning strategy known as "image transformation to image restoration" to pretrain a 3D-UNet network on publicly available lung CT datasets to extract generic visual features from lung images. Subsequently, we enhanced the network's feature extraction capability through multi-task learning involving segmentation and classification tasks, guiding the network to extract image features relevant to RFS. Additionally, we designed a multi-scale feature aggregation module to comprehensively amalgamate multi-scale image features, and ultimately predicted the RFS risk score for lung adenocarcinoma with the aid of a feed-forward neural network. The predictive performance of the proposed method was assessed by ten-fold cross-validation. The results showed that the consistency index (C-index) of the proposed method for predicting RFS and the area under curve (AUC) for predicting whether recurrence occurs within three years reached 0.691 ± 0.076 and 0.707 ± 0.082, respectively, and the predictive performance was superior to that of existing methods. This study confirms that the proposed method has the potential of RFS prediction in lung adenocarcinoma patients, which is expected to provide a reliable basis for the development of individualized treatment plans.

Construction of a prognostic model based on memory CD4+ T cell-associated genes for lung adenocarcinoma and its applications in immunotherapy.

The association between memory CD4+ T cells and cancer prognosis is increasingly recognized, but their impact on lung adenocarcinoma (LUAD) prognosis remains unclear. In this study, using the cell-type identification by estimating relative subsets of RNA transcripts algorithm, we analyzed immune cell composition and patient survival in LUAD. Weighted gene coexpression network analysis helped identify memory CD4+ T cell-associated gene modules. Combined with module genes, a five-gene LUAD prognostic risk model (HOXB7, MELTF, ABCC2, GNPNAT1, and LDHA) was constructed by regression analysis. The model was validated using the GSE31210 data set. The validation results demonstrated excellent predictive performance of the risk scoring model. Correlation analysis was conducted between the clinical information and risk scores of LUAD samples, revealing that LUAD patients with disease progression exhibited higher risk scores. Furthermore, univariate and multivariate regression analyses demonstrated the model independent prognostic capability. The constructed nomogram results demonstrated that the predictive performance of the nomogram was superior to the prognostic model and outperformed individual clinical factors. Immune landscape assessment was performed to compare different risk score groups. The results revealed a better prognosis in the low-risk group with higher immune infiltration. The low-risk group also showed potential benefits from immunotherapy. Our study proposes a memory CD4+ T cell-associated gene risk model as a reliable prognostic biomarker for personalized treatment in LUAD patients.

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.

SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer.

A Novel Gene Signature Based on Immune Cell Infiltration Landscape Predicts Prognosis in Lung Adenocarcinoma Patients.

The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes. For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions. Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells. We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

Abstract 7640: Prognostic significance of S100A16 expression in patients with resected lung adenocarcinoma

Abstract Background: Lung cancer is the leading cause of cancer death worldwide. Tumor recurrence is the most common cause of treatment failure after surgical resection. The S100 protein family is a multigenic group of cytoplasmic calcium-binding proteins. S100A16 has to be reported to be an independent prognostic indicator of poor overall survival in lung adenocarcinoma recently. However, the prognostic value of S100A16 in recurrence-free survival (RFS) in patients with resected lung adenocarcinoma has not been well demonstrated. The relationship between S100A16 expression and the histological subtypes of lung adenocarcinoma remained unknown. Methods: A total of 79 patients with resected lung adenocarcinoma were included in the study. S100A16 expression was determined by immunohistochemistry in tumor specimens. The prognostic value of S100A16 overexpression and its relationship with clinicopathological variables, including histology subtypes, were investigated. Results: S100A16 overexpression tended to be significantly associated with order age (P = 0.074). There was no significant association between S100A16 overexpression and predominant pattern group (lepidic/acinar/papillary vs. micropapillary/solid) (P = 0.447). Univariate analysis indicated that pathologic stage (II or III vs. I) (P = 0.001) and S100A16 overexpression (P = 0.005) were significant prognostic factors for worse recurrence-free survival (RFS). Predominant pattern group (lepidic/acinar/papillary vs. micropapillary/solid) (P = 0.064) tended to be a significant prognostic factor for worse RFS. In multivariate analysis, pathologic stage (II or III vs. I) (HR, 27.847; 95% CI, 4.173 to 185.831; P = 0.001), and S100A16 overexpression (HR, 21.740; 95% CI, 2.516 to 187.870; P = 0.005) were still significant prognostic factors for worse RFS. Conclusions: S100A16 overexpression was a significant prognostic factor for worse RFS in patients with resected lung adenocarcinoma. This information is useful to stratify high-risk patients of recurrence after resection of lung adenocarcinoma. Citation Format: Jung-Jyh Hung. Prognostic significance of S100A16 expression in patients with resected lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7640.

Super-enhancer mediated upregulation of MYEOV suppresses ferroptosis in lung adenocarcinoma

Super-enhancers (SEs) exerted a crucial role in regulating the transcription of oncogenes across various malignancies while the roles of SEs driven genes and the core regulatory elements remain elusive in LUAD. In this study, cancer-specific-SE-genes of lung adenocarcinoma (LUAD) were profiled through H3K27ac ChIP-seq data of cancer cell lines and normal lung tissues, which enriched in in biological processes and pathways integral to the pathophysiology of LUAD. Based on this study, LUAD cells were susceptible to SEs inhibitors, with a reduction of cell proliferation as well as an elevation of apoptosis upon JQ1 or THZ1 intervention. Moreover, the integration of SEs landscapes, CRISPRi, ChIP-PCR, Hi-C data analysis and dual-luciferase reporter assays revealed that myeloma overexpressed gene (MYEOV) was aberrantly overexpressed in LUAD via transcriptional activation by the core SE elements. Functionally, the knockdown of MYEOV undermined cell proliferation in vitro and tumor growth in vivo. In addition, the knockdown of MYEOV generated a prominent ferroptotic phenotype, characterized by elevation of intracellular ferrous iron, reactive oxygen species and lipid peroxidation, together with alteration in marker proteins (SLC7A11, GPX4, FTH1, and ACSL4). Instead, the overexpression of MYEOV accelerated cell proliferation and abrogated ferroptosis. Clinically, the overexpression of MYEOV was observed in LUAD tissues indicating a poor prognosis in patients with LUAD. Mechanistically, SMPD1-induced autophagic degradation of GPX4 assumed a crucial role in the process of ferroptosis triggered by MYEOV knockdown. Serving as an oncogene repressing ferroptosis, promoting proliferation as well as shortening survival in LUAD, SEs-mediated activation of MYEOV might distinguish as a promising therapeutic target.

Discovery of Multiple Effects of Reactive Oxygen Species on Lung Adenocarcinoma at the Single-cell and Bulk Tissue Levels.

Reactive oxygen species (ROS) are potential targets for treating malignant tumors. The aim of this study was to probe into the mechanisms of disease development and treatment in lung adenocarcinoma (LUAD). This study investigated the impact of ROS on the progression of LUAD at different transcriptomic levels and analyzed key molecules involved in the regulation of LUAD. Single-cell RNA-seq (scRNA-seq) data of LUAD were clustered and annotated to determine cell types. Scissor cells based on LUAD bulk transcriptome and epithelial scRNA-seq data were used to classify subsets associated with ROS phenotypes. Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate regression analyses were performed between the Scissor-positive and Scissor-negative epithelial cells to select key differentially expressed genes (DEGs) for developing a ROS-related signature. The ROS score was significantly negatively correlated with the overall survival (OS) of LUAD. Seven cell types from the LUAD tissues were identified. The ROS-related gene signature was significantly correlated with metabolism, tumor microenvironment (TME) indicators, and the half-maximal inhibitory concentration (IC50) values of 10 drugs. The gene signature was verified as an independent indicator for LUAD prognosis. The current study provided novel insights into the impact of ROS on LUAD pathology at both single-cell and bulk-tissue levels, facilitating the prognostic evaluation and drug therapy development for patients with LUAD.

Prognostic value of tumor‑associated CD177+ neutrophils in lung adenocarcinoma.

The aim of the present study was to detect CD177+ neutrophils in tumor tissues and analyze their association with the clinical characteristics and prognosis of patients with lung adenocarcinoma (LUAD). Immunohistochemistry was used to detect CD177+ neutrophils in tumors and adjacent tissues of 16 patients with LUAD who underwent curative surgical resection. A total of 120 patients with LUAD were recruited, and their clinical data were collected; survival follow-up was performed. CD177+ neutrophils in tumor tissues were detected via immunohistochemistry, and the association between CD177+ neutrophils and clinical characteristics was analyzed. The density of CD177+ neutrophils in tumor tissues and adjacent tissues of patients with LUAD was analyzed using t-test, and the association between CD177+ neutrophils and clinical characteristics was analyzed through the Chi-square test. Survival was calculated using the Kaplan-Meier survival rate curve. Finally, the association between these indicators and the survival of LUAD patients was evaluated using Cox regression analysis. CD177+ neutrophil infiltration was significantly higher in LUAD tumor tissues, and the high density of CD177+ neutrophils was associated with the clinical characteristics of TNM stage, tumor differentiation and poor progression-free and overall survival in LUAD. In conclusion, tumor-associated CD177+ neutrophils associated with malignant progression and poor prognosis may be independent and unfavorable prognostic biomarkers for LUAD.

Elucidating the anti-cancer potential of Cinnamomum tamala essential oil against non-small cell lung cancer: A multifaceted approach involving GC-MS profiling, network pharmacology, and molecular dynamics simulations

Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm., or Indian Bay Leaf, is a well-known traditional ayurvedic medicine used to treat various ailments. However, the molecular mechanism of action of Cinnamomum tamala essential oil (CTEO) against non-small cell lung cancer (NSCLC) remains elusive. The present study aims to decipher the molecular targets and mechanism of CTEO in treating NSCLC. GC-MS analysis detected 49 constituents; 44 successfully passed the drug-likeness screening and were identified as active compounds. A total of 3961 CTEO targets and 4588 anti-NSCLC-related targets were acquired. JUN, P53, IL6, MAPK3, HIF1A, and CASP3 were determined as hub genes, while cinnamaldehyde, ethyl cinnamate and acetophenone were identified as core compounds. Enrichment analysis revealed that targets were mainly involved in apoptosis, TNF, IL17, pathways in cancer and MAPK signalling pathways. mRNA expression, pathological stage, survival analysis, immune infiltrate correlation and genetic alteration analysis of the core hub genes were carried out. Kaplan-Meier overall survival (OS) curve revealed that HIF1A and CASP3 are linked to worse overall survival in Lung Adenocarcinoma (LUAD) cancer patients compared to normal patients. Ethyl cinnamate and cinnamaldehyde showed high binding energy with the MAPK3 and formed stable interactions with MAPK3 during the molecular dynamic simulations for 100 ns. The MM/PBSA analysis revealed that van der Waals (VdW) contributions predominantly account for a significant portion of the compound interactions within the binding pocket of MAPK3. Density functional theory analysis showed cinnamaldehyde as the most reactive and least stable compound. CTEO exhibited selective cytotoxicity by inhibiting the proliferation of A549 cells while sparing normal HEK293 cells. CTEO triggered apoptosis by arresting the cell cycle, increasing ROS accumulation, causing mitochondrial depolarisation, and elevating caspase-3, caspase-8 and caspase-9 levels in A549 cells. The above study provides insights into the pharmacological mechanisms of action of Cinnamomum tamala essential oil against non-small cell lung cancer treatment, suggesting its potential as an adjuvant therapy.

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma.

Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD). Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements. The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson's R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS. Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment.

A novel disulfidptosis-related prognostic gene signature and experimental validation identify ACTN4 as a novel therapeutic target in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a prevalent form of malignancy globally. Disulfidptosis is novel programmed cell death pathway based on disulfide proteins, may have a positive impact on the development of LUAD treatment strategies. To investigate the impact of disulfidptosis-related genes (DRGs) on the prognosis of LUAD, developed a risk model to facilitate the diagnosis and prognostication of patients. We also explored ACTN4 (DRGs) as a new therapeutic biomarker for LUAD. We investigated the expression patterns of DRGs in both LUAD and noncancerous tissues. To assess the prognostic value of the DRGs, we developed risk models through univariate Cox analysis and lasso regression. The expression and function of ACTN4 was evaluated by qRT-PCR, immunohistochemistry and in vitro experiments. The TIMER examined the association between ACTN4 expression and immune infiltration in LUAD. Ten differentially expressed DRGs were identified. And ACTN4 was identified as potential risk factors through univariate Cox regression analysis (P< 0.05). ACTN4 expression and riskscore were used to construct a risk model to predict overall survival in LUAD, and high-risk demonstrated a significantly higher mortality rate compared to the low-risk cohort. qRT-PCR and immunohistochemistry assays indicated ACTN4 was upregulated in LUAD, and the upregulation was associated with clinicopathologic features. In vitro experiments showed the knockdown of ACTN4 expression inhibited the proliferation in LUAD cells. The TIMER analysis demonstrated a correlation between the expression of ACTN4 and the infiltration of diverse immune cells. Elevated ACTN4 expression was associated with a reduction in memory B cell count. Additionally, the ACTN4 expression was associated with m6A modification genes. Our study introduced a prognostic model based on DRGs, which could forecast the prognosis of patients with LUAD. The biomarker ACTN4 exhibits promise for the diagnosis and management of LUAD, given its correlation with tumor immune infiltration and m6A modification.

Exploring diverse programmed cell-death patterns to develop a novel gene signature for predicting the prognosis of lung adenocarcinoma patients.

Programmed cell death (PCD) plays a critical role in tumor progression and malignancy, and exploring its relationship with lung adenocarcinoma (LUAD)'s survival outcomes is important for personalized diagnosis and treatment. This study aimed to identify survival-related genes and construct an effective prognostic indicator for LUAD based on 12 forms of PCD. A total of 1,933 candidate genes related to PCD were collected from published studies and public data center. A prognostic gene signature, called the cell death index (CDI), was established based on RNA-Seq and immunohistochemistry (IHC). IHC staining on tissue microarray was applied for the validation of protein level. Moreover, GSE42127, GSE72094 were used as validation datasets. The CDI based on expression level of nine genes (CCNB2, HMGA1, CACNA2D2, BUB1B, BTG2, KIF14, PTGDS, SERPINB5, BRCA1) was highly predictive for overall survival (OS) of LUAD in our cohort [36-month area under the curve (AUC): 0.750, 60-month AUC: 0.809]. The CDI was further validated in independent cohorts (GSE72094, 36-month AUC: 0.717, 60-month AUC: 0.737; GSE42127, 12-month AUC: 0.829, 60-month AUC: 0.663). And the CDI was found to be an independent prognostic factor after adjusting for other clinical characteristics. Furthermore, the high-CDI group was associated with upregulated tumor immune infiltration compared to the low-CDI group. This study identified a 9-gene signature (CDI) based on PCD-related genes that accurately predicted the prognosis of LUAD patients. The CDI could serve as a valuable prognostic indicator and guide personalized therapeutic strategies for LUAD.

Integrating bulk-RNA sequencing and single-cell sequencing analyses to characterize adenosine-enriched tumor microenvironment landscape and develop an adenosine-related prognostic signature predicting immunotherapy in lung adenocarcinoma.

The adenosine-signaling axis has been recognized as an important immunomodulatory pathway in tumor immunity. However, the biological role of the adenosine-signaling axis in the remodeling of the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unclear. Here, we quantified adenosine signaling (ado_sig) in LUAD samples using the GSVA method and assessed the prognostic value of adenosine in LUAD. Afterward, we explored the heterogeneity of the tumor-immune microenvironment at different adenosine levels. In addition, we analyzed the potential biological pathways engaged by adenosine. Next, we established single-cell transcriptional profiles of LUAD and analyzed cellular composition and cell-cell communication analysis under different adenosine microenvironments. Moreover, we established adenosine-related prognostic signatures (ARS) based on comprehensive bioinformatics analysis and evaluated the efficacy of ARS in predicting immunotherapy. The results demonstrated that adenosine signaling adversely impacted the survival of immune-enriched LUAD. The high-adenosine microenvironment exhibited elevated pro-tumor-immune infiltration, including M2 macrophages and displayed notably increased epithelial-mesenchymal transition (EMT) transformation. Furthermore, adenosine signaling displayed significant associations with the expression patterns and prognostic value of immunomodulators within theTME. Single-cell sequencing data revealed increased fibroblast occupancy and a prominent activation of the SPP1 signaling pathway in the high adenosine-signaling microenvironment. The ARS exhibited promising effectiveness in prognostication and predicting immunotherapy response in LUAD. In summary,overexpression of adenosine can cause a worsened prognosis in the LUAD with abundant immune infiltration. Moreover, increased adenosine levels are associated with pro-tumor-immune infiltration, active EMT transformation, pro-tumor angiogenesis, and other factors promoting cancer progression, which collectively contribute to the formation of an immunosuppressive microenvironment. Importantly, the ARS developed in this study demonstrate high efficacy in evaluating the response to immunotherapy.