Prognosis Of Lung Adenocarcinoma Research Articles

Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma

Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts. The model defined high- and low-risk groups successfully stratified prognosis of the entire cohort. Moreover, multivariable Cox analysis identified the model defined risk groups as an independent predictor for disease-free survival. Importantly, combining TNM stage with the established model helped to distinguish subgroups of patients with high-risk stage II and stage III disease who are highly likely to benefit from adjuvant chemotherapy. Overall, our study highlights the significant value of the constructed model to serve as a complementary biomarker for survival stratification and adjuvant therapy selection for lung adenocarcinoma patients after resection.

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma

To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing and prognosis prediction in lung adenocarcinoma (LUAD). LRG mRNA and long non-coding RNA transcriptomes, epigenetic methylation data, and somatic mutation data from The Cancer Genome Atlas LUAD cohort were analyzed to identify lactylation cancer subtypes (CSs) using 10 multiomics ensemble clustering techniques. The findings were then validated using the GSE31210 and GSE13213 LUAD cohorts. A prognosis model for LUAD was developed using the identified hub LRGs to divide patients into high- and low-risk groups. The effectiveness of this model was validated. We identified two lactylation CSs, which were validated in the GSE31210 and GSE13213 LUAD cohorts. Nine hub LRGs, namely HNRNPC, PPIA, BZW1, GAPDH, H2AFZ, RAN, KIF2C, RACGAP1, and WBP11, were used to construct the prognosis model. In the subsequent prognosis validation, the high-risk group included more patients with stage T3 + 4, N1 + 2 + 3, M1, and III + IV cancer; higher recurrence/metastasis rates; and lower 1, 3, and 5 year overall survival rates. In the oncogenic pathway analysis, most of the oncogenic mutations were detected in the high-risk group. The tumor microenvironment analysis illustrated that immune activity was notably elevated in low-risk patients, indicating they might more strongly respond to immunotherapy than high-risk patients. Further, oncoPredict analysis revealed that low-risk patients have increased sensitivity to chemotherapeutics. Overall, we developed a model that combines multiomic analysis and machine learning for LUAD prognosis. Our findings represent a valuable reference for further understanding the important function of lactylation modification pathways in LUAD progression.

Identification of KRT16 and ANXA10 as cell cycle regulation genes for lung adenocarcinoma based on self-transcriptome sequencing of surgical samples and TCGA public data mining

AimThis study aimed to identify the genes associated with the development of lung adenocarcinoma (LUAD) and potential therapeutic targets.MethodsDifferentially expressed genes (DEGs) were identified by self-transcriptome sequencing of tumor tissues and paracancerous tissues resected during surgery and combined with The Cancer Genome Atlas (TCGA) data to screen for the genes associated with LUAD prognosis. The expression was validated at mRNA and protein levels, and the gene knockdown was used to examine the impact and underlying mechanisms on lung cancer cells.ResultsA total of 227 DEGs were identified by transcriptome sequencing, and the 20 DEGs with the most significant differences were used for co-analysis with TCGA data. The findings suggested that KRT16 and ANXA10 might have an important role in the development of LUAD after validating the mRNA and protein expression levels at the cellular level. The knockdown of KRT16 and ANXA10 inhibited the proliferation of lung cancer cells, and the cell cycle was blocked in the G1 phase. The expression of the G1/S–phase cell cycle checkpoint–related proteins cyclin D1 and cyclin E was inhibited by KRT16 and ANXA10 knockdown, respectively. The tumor formation ability decreased after KRT16 or ANXA10 knockdown in vivo.ConclusionsKRT16 and ANXA10 are potential genes regulating the development of LUAD. Also, they may be potential targets for the targeted therapy of LUAD by inhibiting the proliferation of lung cancer cells and blocking the cell cycle by affecting key protein expression levels at cell cycle checkpoints.

Tertiary lymphoid structure signatures are associated with survival and immunotherapy response in lung adenocarcinoma

The presence of tertiary lymphoid structures (TLSs) has been correlated with improved prognosis and clinical outcomes in response to immunotherapy in certain solid tumors. However, the precise role of TLSs in lung adenocarcinoma (LUAD) remains unclear. Four datasets of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The TLSs model was constructed using a multivariate Cox proportional hazards model. GO and KEGG analyses were performed to explore the biological process associated with the TLSs model. The ESTIMATE and CIBERSORT algorithms were employed to quantify immune infiltration status. TLSs signature genes (TSGs) were identified, including chemokine signature genes, TFH cell markers, TH1 cell and B cell markers, and a plasma cell marker. Diagnostic evaluations identified key genes with high diagnostic value, particularly among chemokine signature genes and TFH cells markers. Furthermore, high expression of CCL20 and IL1R2 was correlated with poorer outcomes, while other TSGs indicated more favorable prognoses. A novel TLSs score model was constructed, integrating 4 TSGs (SGPP2, MS4A1, IL1R2 and CCL20), which accurately predicted patient survival and was independently associated with prognosis. Additionally, the TLSs score served as a robust indicator for LUAD survival prediction, outperforming traditional staging systems. Comprehensive analyses of enriched pathways and immune cell infiltration patterns revealed that this score involved in metabolic processes and immune cell regulation. Furthermore, the TLSs score showed potential as an indicator of response to immunotherapy, with higher scores associated with reduced expression of immune checkpoint genes and poorer response rates. The TLSs score may serve as a predictor of prognosis and immunotherapeutic response in LUAD. These findings may offer new insights on the study of malignancy and personalized immunotherapy for LUAD patients.

Integrated bioinformatics and experimental analysis of CHAF1B as a novel biomarker and immunotherapy target in LUAD

The prognosis and treatment efficacy of lung adenocarcinoma (LUAD), a disease with a high incidence, remains unsatisfactory. Identifying new biomarkers and therapeutic targets for LUAD is essential. Chromosomal assembly factor 1B (CHAF1B), a p60 component of the CAF-1 complex, is closely linked to tumor incidence and cell proliferation. However, CHAF1B's biological role and molecular mechanism in LUAD remain unclear. Here, CHAF1B expression in LUAD was examined using the GEPIA2 and UALCAN databases. Using The Cancer Genome Atlas (TCGA) LUAD database, we analyzed the diagnostic and prognostic significance of CHAF1B and its association with immune infiltration and immunological checkpoints. Gene ontology (GO) enrichment and single-cell function analyses were employed to investigate CHAF1B's possible biological roles. Drug sensitivity analysis predicted CHAF1B's effect on chemotherapeutic drug sensitivity. We also predicted lncRNAs-miRNA-CHAF1B axis to explore the molecular mechanism of CHAF1B in LUAD. Preliminary in vitro studies using qRT-PCR, CCK8, Transwell, glucose, and lactate metabolism confirmed CHAF1B's expression and role in LUAD. Its expression is associated with drug sensitivity, immunological checkpoints, and immune cell infiltration. We predicted that three miRNAs (miR-29c-3p, miR-145-5p, miR-1247-5p) and three lncRNAs (AL139287.1, NEAT1, SHG1) may be target miRNAs and target lncRNAs that regulate CHAF1B. In vitro tests showed that CHAF1B suppression decreased LUAD's migration, invasion, proliferation, and glycolysis. Overall, CHAF1B may be an innovative biomarker and therapeutic target for LUAD.

Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma

BackgroundPatients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients.MethodsDC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, “timeROC”, and validated using GEO datasets.ResultsAnalysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy.ConclusionWe have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD.

Biological characterization and clinical significance of cuproptosis-related genes in lung adenocarcinoma

BackgroundLung cancer has high morbidity and mortality rates, which results in a poor prognosis. Cuproptosis is a novel cell death mechanism. The aim of this study was to examine the biological characteristics and clinical significance of genes associated with cuproptosis in lung adenocarcinoma (LUAD), and to understand the molecular mechanisms underlying the occurrence and progression of LUAD.MethodsWe targeted 10 cuproptosis-related genes from previous studies and used the datasets from GEO and TCGA databases to identify differential genes related to cuproptosis; then the data were analyzed by R package, Cytoscape, TISDB, cBioPortal, STRING, CancerSEA, and Disgenet; and finally, the data were detected by immunohistochemistry validation was performed.ResultsCDKN2A and MTF1 were cuproptosis-associated LUAD differential genes and were differentially expressed in immune subtypes. The expression of CDKN2A and MTF1 showed correlation with multiple functional states of LUAD.CDKN2A was negatively correlated with LUAD survival prognosis.ConclusionCDKN2A and MTF1 were correlated with the diagnosis of LUAD, and CDKN2A was negatively correlated with the survival and prognosis of LUAD. CDKN2A has the potential to contribute to the early diagnosis and prognosis analysis of LUAD.

GIMAP1 interacts with TMX1 to improve lung adenocarcinoma prognosis by influencing tumor immune microenvironment.

Recent studies have indicated that the GIMAP family is downregulated in lung cancer and correlates with poor prognosis, although the underlying mechanisms remain unclear. This study aimed to elucidate the mechanism behind GIMAP1 downregulation in lung cancer. Bioinformatics tools were employed to assess the correlation between the GIMAP family and various cancers. Specifically, GIMAP1 was selected for further investigation, and its role in lung adenocarcinoma was confirmed through RNA sequencing analysis, Gene Set Enrichment Analysis (GSEA) of differentially expressed genes, correlation analysis with immune cell infiltration, and assay of the GIMAP1-TMX1 interaction. Based on bioinformatics analysis and real-world cohort studies, it was found that GIMAP1 was underexpressed in lung cancer tissues but exhibited elevated expression following immunotherapy. Overexpression of GIMAP1 was shown to influence several immune signaling pathways. In patients with high GIMAP1 expression, there was a significant increase in the infiltration of CD8+ T cells, activated memory CD4+ T cells, monocytes, and M1 macrophages; conversely, infiltration by M0 macrophages, resting dendritic cells (DCs), and plasma cells was significantly reduced. In vitro experiments showed that high levels of GIMAP1 increased the percentage of Treg, NK, and NKT cells. Additionally, GIMAP1 directly interacted with TMX1 and modulated the expression of downstream immune-related genes including CMTM5, IL17F, TRAV34, and XCR1. Therefore, GIMAP1 may serve as a promising therapeutic target in lung cancer, influencing both disease initiation and progression.

Establishment of liquid-liquid phase separation-related prognostic model in lung adenocarcinoma and systematic analysis of its clinical significance.

To detect the prognostic importance of liquid-liquid phase separation (LLPS) in lung adenocarcinoma. The gene expression files, copy number variation data, and clinical data were downloaded from The Cancer Genome Atlas cohort. LLPS-related genes were acquired from the DrLLPS website. The prognostic model based on LLPS was constructed by the Cox regression and LASSO regression analyses after the identification of LLPS-related differentially expressed genes (DEGs). Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed. The LLPS-related prognostic risk score was validated by GSE31210 and GSE72094. The overall survival of lung adenocarcinoma patients was predicted by plotting a nomogram. The biological features of the high-risk lung adenocarcinoma were evaluated by the CIBERSORT, ESTIMATE, Gene Set Variation Analysis, and Genomics of Drug Sensitivity in Cancer. Reverse transcription-quantitative polymerase chain reaction detected hub gene expression. A total of 91 DEGs were screened out in LLPS, among which 9 genes were discovered as prognostic biomarkers of lung adenocarcinoma. GRIA1, CRTAC1, MAGEA4, and MAPK4 were identified as hub genes by the LASSO Cox regression analysis. High-risk and low-risk groups were divided according to the risk index, with the high-risk group displaying a markedly worse outcome. CRTAC1 expression was significantly decreased, MAGEA4 and MAPK4 expressions were increased, while GRIA1 expression was altered in lung adenocarcinoma cells. Tumor microenvironment, signaling pathway enrichment, and drug sensitivity significantly differed between different risk groups. This work proposed a prognostic tool based on the LLPS-related gene signature to offer prospective and effective biomarkers for lung adenocarcinoma prognosis.

Integrated analysis of single-cell and bulk-RNA sequencing for the cellular senescence in prognosis of lung adenocarcinoma

Non-small cell lung cancer (NSCLC), half of which are lung adenocarcinoma (LUAD), is one of the most widely spread cancers in the world. Telomerase, which maintains telomere length and chromosomal integrity, enables cancer cells to avoid replicative senescence. When telomerase is inhibited, cancer cells’ senescence began, preventing them from growing indefinitely. Cellular senescence and telomeres are intrinsically linked. As of yet, still laking a systematic study of the involvement of telomere-senescence related genes in lung adenocarcinoma development. In this study, myeloid cells were identified as the cell type which are most correlated with cellular senescence based on its highest telomere-related gene activity. GO, KEGG, GSEA and GSVA analyses were used to explore the biological function of telomere-senescence related genes in LUAD. The combined analysis of single-cell RNA-sequencing and bulk-RNA sequencing identified a gene signature composed of 14 genes which can accurately predict the prognosis of patients with LUAD. In one training and four validation sets, patients with higher Telomere Related Gene Signature (TRGS) had a worse prognosis than those with lower TRGS. Different TRGS patient groups showed varying degrees of immune cell infiltration, frequency of gene missense mutation, sensitivity to different drugs, and tumor mutation burden (TMB). Collectively, we developed a brand new signature composed of telomere-senescence related genes that can accurately predicts patients’ prognosis in LUAD, which provides new insights for future research into the role of cellular senescence in LUAD.

The lysosome-related characteristics affects the prognosis and tumor microenvironment of lung adenocarcinoma.

The lysosome plays a vitally crucial role in tumor development and is a major participant in the cell death process, involving aberrant functional and structural changes. However, there are few studies on lysosome-associated genes (LAGs) in lung adenocarcinoma (LUAD). Bulk RNA-seq of LUAD was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The lysosome risk signature was constructed after univariate and least absolute shrinkage and selection operator (Lasso) cox regression analysis of the TCGA training set, and its capability was validated by additional validation sets from GEO. Single cell sequencing (scRNA) was obtained from GEO to analyze the differences of lysosome risk signature at the single-cell level and the differences in the function and pathway. In vitro experiments have validated the function of CTSH in LUAD. The risk signature contained seven key LAGs, and patients were categorized into high- and low-risk groups based on a specific calculation formula. The LAG risk signature, which accurately predicted the prognostic status of LUAD patients, was still regarded as an independent prognostic indicator in multifactorial cox regression analysis. Subsequently, the combination of the signature and key clinical information was used to construct a column-line diagram for clinical assessment, which had a high discriminatory power. Immune infiltration analysis from bulk RNA-seq and scRNA-seq indicated that the low-risk group was immune-activated and had a better benefit in the prediction of immunotherapy. Finally, we validated its role in inhibiting tumor proliferation and metastasis in LUAD cells by knockdown of CTSH. We defined a new biomarker that provided unique insights for individualized survival prediction and immunotherapy recommendations for LUAD patients.

CYP24A1 Binding to FUS Maintains Tumor Properties by Regulating the miR-200c/ZEB1/EMT Axis.

The active vitamin D-degrading enzyme (CYP24A1) is commonly overexpressed in various types of cancer, which is associated with poor prognosis in cancer patients. Recent studies highlight the antagonism of CYP24A1 toward the anticancer role of active vitamin D. However, the impact of CYP24A1 on tumorigenesis and its underlying mechanisms largely remains unexplored. This study also found that high CYP24A1 mRNA expressions were associated with poor prognosis in ovarian cancer and lung adenocarcinoma (LUAD) patients. Moreover, we demonstrated that the overexpression of CYP24A1 accelerated the proliferation, migration, and invasion of ovarian cancer and LUAD cancer cells in vitro. Furthermore, knockdown of CYP24A1 displayed an anticancer effector both in vitro and in vivo. Mechanically, 87-297 amino acid motif of CYP24A1 bound specifically to FUS protein, consequentially reducing FUS affinity for miR-200c. Considering FUS promotes gene silencing by binding to microRNA targets, a decrease in miR-200c levels led to a notable activation of its target ZEB1, resulting in the promotion of the epithelial-mesenchymal transition (EMT) process. In conclusion, FUS binding specifically by CYP24A1 impaired miR-200c-mediated ZEB1 silencing, thereby augmenting EMT progression and tumorigenesis. These findings elucidate a fundamental mechanism by which CYP24A1 operates as an oncogene, offering potential targets for therapeutic interventions in cancer treatment.

Unveiling the Role of SLC6A17 in Lung Adenocarcinoma: Prognosis, Pathways, and Therapeutic Implications.

The role of solute carrier family 6 member 17 (SLC6A17) in lung adenocarcinoma (LUAD) is unclear. To address this gap in knowledge, we employed bioinformatics analysis and experimental validation. This research aimed to scrutinize the expression patterns of the SLC6A17 gene across a spectrum of cancers and specifically within LUAD, utilizing data extracted from The Cancer Genome Atlas (TCGA). The correlation between SLC6A17 expression and LUAD prognosis was investigated to assess its diagnostic relevance. The study delved into the possible regulatory mechanisms of SLC6A17, focusing on its links to immune cell infiltration and drug response in LUAD. The examination of SLC6A17 expression was extended to single-cell sequencing data in LUAD, alongside an evaluation of the gene's genomic alterations and clinical implications within this disease context. Validation of SLC6A17 expression levels was conducted using datasets from GSE87340 and various cell lines, employing quantitative real-time polymerase chain reaction (qRTPCR) techniques. SLC6A17 exhibited aberrant expression in both pan-cancer and LUAD. Increased expression of SLC6A17 in LUAD patients was significantly associated with poorer overall survival (p = 0.008), progress-free survival (p = 0.019), and disease specific survival (p = 0.030). In LUAD patients, the levels of SLC6A17 expression were found to be a significant standalone indicator of prognosis, with a p-value of 0.031. SLC6A17 exhibited associations with various pathways, including focal adhesion, ECM receptor interaction, cell cycle, linoleic acid metabolism, pathways in cancer, and more. SLC6A17 expression demonstrated correlations with immune infiltration in LUAD. SLC6A17 expression revealed a notably inverse relationship with several substances, including AR-42, T0901317, tubastatin A, SB52334, and amuvatinib, within the context of LUAD. SLC6A17 was found to be significantly positively regulated in LUAD cell lines. These findings suggest that SLC6A17 indicates the potential of a potential prognostic biomarker and immunotherapeutic target for patients with LUAD.

Immunoinfiltration Analysis of Mitochondrial Damage-Related Genes in Lung Adenocarcinoma and Construction of a Classification and Prognostic Model Integrated With WGCNA and Machine Learning Algorithms.

Lung adenocarcinoma (LUAD) exhibits molecular heterogeneity, with mitochondrial damage affecting progression. The relationship between mitochondrial damage and immune infiltration, and Weighted Gene Co-expression Network Analysis (WGCNA)-derived biomarkers for LUAD classification and prognosis, remains unexplored. The objective of our research is to identify gene modules closely related to the clinical stages of LUAD using the WGCNA method. Based on the genes within these modules, we constructed machine learning (ML) models for classification and prognosis prediction, thereby facilitating precise diagnosis and personalized treatment of LUAD. Using GeneCards and The Cancer Genome Atlas (TCGA) databases, we screened differentially expressed mitochondrial damage-related genes in LUAD. Immune cell infiltration patterns were assessed using Single-Sample Gene Set Enrichment Analysis (SSGSEA) method. Functional enrichment analyses were conducted to explore biological functions and signaling pathways. Gene modules related to clinical stages of LUAD were identified by WGCNA. ML models were constructed for classification and prognosis prediction, and validated in an independent Gene Expression Omnibus (GEO) dataset. The study revealed a significant relationship between mitochondrial damage and immune infiltration in LUAD. We identified a gene module closely associated with the clinical stages of LUAD. The ML models for classification and prognosis that were constructed demonstrated good effectiveness and generalization capabilities. Mitochondrial damage-related genes are crucial in LUAD progression and linked to immune infiltration. The gene module and models identified have potential applications in LUAD classification and prognosis, offering novel markers for precision medicine. This study uncovers the relationship between mitochondrial damage and immune infiltration in LUAD, paving the way for molecular classification, prognosis prediction, and personalized treatment strategies.

Comprehensive multi-omics analysis identifies NUSAP1 as a potential prognostic and immunotherapeutic marker for lung adenocarcinoma.

While NUSAP1's association with various tumors is established, its predictive value for prognosis and immunotherapy in lung adenocarcinoma (LUAD) remains unconfirmed. We analyzed Nucleolar Spindle-Associated Protein 1 (NUSAP1) gene expression in TCGA and GTEx datasets and validated it in clinicopathological tissues using qRT-PCR and immunohistochemistry. Additionally, we investigated NUSAP1's relationship with patient prognosis across TCGA and five GEO cohorts. The IMvigor210 cohort was utilized to explore NUSAP1's association with immunotherapy efficacy. Furthermore, single-cell RNA-sequencing data was used to examine the correlation between NUSAP1 and immune cell infiltration. Finally, we analyzed the relationship between NUSAP1 and m6A methylation. NUSAP1 expression was significantly elevated in tumor tissues, correlating with poorer prognosis in LUAD patients. It exhibited a significant correlation with immune cell infiltration in the tumor microenvironment, predominantly expressed in Tprolif cells. LUAD patients with heightened NUSAP1 expression may derive greater benefit from anti-PD-L1 treatment. Additionally, NUSAP1 was tightly linked with m6A methylation. Enrichment analysis revealed its association with key biological functions, including lipid metabolism and cell cycle regulation. Our comprehensive analysis underscores NUSAP1's potential as a prognostic and immunotherapeutic biomarker for LUAD, warranting further investigation.

Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features.

This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)-eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Programmed cell death pathways in lung adenocarcinoma: illuminating tumor drug resistance and therapeutic opportunities through single-cell analysis.

Lung adenocarcinoma (LUAD) is a major contributor to cancer-related deaths, distinguished by its pronounced tumor heterogeneity and persistent challenges in overcoming drug resistance. In this study, we utilized single-cell RNA sequencing (scRNA-seq) to dissect the roles of programmed cell death (PCD) pathways, including apoptosis, necroptosis, pyroptosis, and ferroptosis, in shaping LUAD heterogeneity, immune infiltration, and prognosis. Among these, ferroptosis and pyroptosis were most significantly associated with favorable survival outcomes, highlighting their potential roles in enhancing anti-tumor immunity. Distinct PCD-related LUAD subtypes were identified, characterized by differential pathway activation and immune cell composition. Subtypes enriched with cytotoxic lymphocytes and dendritic cells demonstrated improved survival outcomes and increased potential responsiveness to immunotherapy. Drug sensitivity analysis revealed that these subtypes exhibited heightened sensitivity to targeted therapies and immune checkpoint inhibitors, suggesting opportunities for personalized treatment strategies. Our findings emphasize the interplay between PCD pathways and the tumor microenvironment, providing insights into the mechanisms underlying tumor drug resistance and immune evasion. By linking molecular and immune features to clinical outcomes, this study highlights the potential of targeting PCD pathways to enhance therapeutic efficacy and overcome resistance in LUAD. These results contribute to a growing framework for developing precise and adaptable cancer therapies tailored to specific tumor characteristics.

The regulation of the cell cycle and epithelial-mesenchymal transition through FUCA2/GGH signaling promotes the progression of lung adenocarcinoma.

The development of lung adenocarcinoma (LUAD) is intricately linked with cell cycle regulation and epithelial-mesenchymal transition (EMT). Our study, leveraging bioinformatics and database analysis, identified FUCA2 as a key gene influencing the prognosis and progression of LUAD. We observed that FUCA2 is highly expressed in LUAD and correlates with poor outcomes. Functionally, we assessed the role of this gene through cell cloning, scratch assays, transwell migration, and western blotting, revealing that FUCA2 knockdown significantly inhibits tumor cell proliferation and migration, downregulates the expression of cell cycle and EMT-related proteins, and markedly reduces tumor burden. Mechanistically, pathway enrichment analysis identified GGH as a downstream target of FUCA2. Knockdown of GGH similarly inhibits the proliferation, migration, and cell cycle progression of LUAD cells. FUCA2 upregulates GGH to modulate cell cycle and EMT in LUAD. Collectively, our findings indicate that the FUCA2/GGH axis promotes LUAD progression by regulating cell cycle and EMT.

Prediction of prognosis, efficacy of lung adenocarcinoma by machine learning model based on immune and metabolic related genes

BackgroundThe aim of this study is to integrate immune and metabolism-related genes in order to construct a predictive model for predicting the prognosis and treatment response of LUAD(lung adenocarcinoma) patients, aiming to address the challenges posed by this highly lethal and heterogeneous disease.Material and methodsUsing TCGA-LUAD as the training subset, differential gene expression analysis, batch survival analysis, Lasso regression analysis, univariate and multivariate Cox regression analysis were performed to construct prognostic related gene models. GEO queue as validation subsets, is used to validate build Riskscore. Then, we explore the Riskscore and mutation status, immune cell infiltration, the relationship between immune therapy and chemotherapy, and build the model of the nomogram.ResultsThe Riskscore has been determined to be composed of seven gene. In the high-risk group defined by this score, both early-stage and advanced-stage LUAD patients exhibit a decreased overall survival rate. The mutation status of patients as well as immune cell infiltration show associations with the Riskscore value obtained from these genes’ expression levels. Furthermore, there exist variations in response to immunotherapy as well as sensitivity to commonly used chemotherapy drugs among different individuals. Lastly, when using a column line plot model based on the calculated Riskscore values, we obtain a concordance index (C-index) was 0 .716 (95% CI 0.671–0.762), and time-dependent ROC predicted probabilities of 1-, 3- and 5-year survival for LUAD patients were 0.752, 0.725 and 0.654, respectively.ConclusionIn conclusion, we have successfully developed a predictive model incorporating immune and metabolism-related genes, encompassing gene expression levels of CAT/CCL20/GPI/INSL4 NT5E/GSTA3/GNPNAT1. This comprehensive model not only enables the prognosis prediction for LUAD patients but also facilitates the prediction of their response to first-line chemotherapy drugs and immune checkpoint inhibitors, thus demonstrating its broad potential in clinical applications. However, our study still has limitations as it is based on TCGA and GEO databases with limited pathological characteristics of patients. Therefore, more practical and valuable factors are needed to predict efficacy. The crosstalk between metabolism and immunity remains to be explored. Finally, this study lacks experimental evidence for the underlying gene expression of prognosis and further research is required.

Elevated expression of REV7 correlates with poor prognosis in lung adenocarcinoma and its inactivation in carcinoma cells enhances chemosensitivity.

REV7 is a multifunctional protein involved in the DNA damage response, cell cycle regulation, gene expression, or primordial germ cell maintenance. REV7 expression in tumor cells is associated with clinical aggressive features and chemoresistance in several human malignancies, however, the clinicopathological significance of REV7 in lung adenocarcinoma (LUAD) has not been studied yet. In this study, we investigated the significance of REV7 expression in LUAD using clinical materials and cell lines. REV7 expression in 142 invasive LUADs were determined using immunohistochemistry, and the relationship between REV7 expression and clinicopathological features was analyzed. High levels of REV7 expression in tumor tissues were positively associated with progressive tumor behavior as assessed by Ki-67 labeling indexes (p < 0.001), maximum standardized uptake values on positron emission tomography (p = 0.005), pathological stage (p = 0.031), N factor (p = 0.048), recurrence (p = 0.038), and disease-specific death (p = 0.020). The REV7-high-expression group showed poorer relapse-free survival (RFS) (p = 0.025) and overall survival (OS) (p = 0.019) compared to the REV7-low-expression group, and REV7 was a significant prognostic factor for RFS and OS. CRISPR/Cas9-mediated REV7-knockout and siRNA-mediated REV7 knockdown were carried out using the LUAD cell lines A549 and H1975, respectively, and it was demonstrated that REV7 inactivation led to slower cell growth, attenuated activation of AKT signaling, and enhanced chemosensitivity compared with control cells. These results suggest that REV7 is a potential predictive biomarker for poor prognosis in invasive LUAD and a possible molecular target for LUAD management.