Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards 'hot' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.

The prognostic value of immune escape-related genes in lung adenocarcinoma.

Background: Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted. Methods: RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219. Results: Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue. Conclusions: We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

KLRB1 expression is associated with lung adenocarcinoma prognosis and immune infiltration and regulates lung adenocarcinoma cell proliferation and metastasis through the MAPK/ERK pathway.

Background: Lung cancer is the most common primary malignant tumor of the lung, and as one of the malignant tumors that pose the greatest threat to the health of the population, the incidence rate has remained high in recent years. Previous studies have shown that KLRB1 is transcriptionally repressed in lung adenocarcinoma and correlates with lung adenocarcinoma prognosis. The objective of this study is to investigate the intrinsic mechanisms by which KLRB1 affects the malignant phenotypes of lung adenocarcinoma such as immune infiltration, proliferation, growth and metastasis. Methods: We assessed the expression levels of KLRB1 in publicly available databases and investigated its associations with clinical and pathological variables. Enrichment analysis was subsequently conducted to investigate possible signaling pathways and their associated biological functions. Statistical analysis, including Spearman correlation and the application of multigene prediction models, was utilized to assess the relationship between the expression of KLRB1 and the infiltration of immune cells. The diagnostic and prognostic value of KLRB1 was evaluated using Kaplan-Meier survival curves, diagnostic receptor operating characteristic (ROC) curves, histogram models, and Cox regression analysis. Specimens from lung adenocarcinoma (LUAD) patients were collected, the expression level of KLRB1 was detected by protein blotting analysis, and the expression level of KLRB1 was detected at the mRNA level by real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR). Small interfering RNA (siRNA) was used to silence gene expression, and Transwell, Cell Counting Kit-8 (CCK-8) and colony formation assays were subsequently performed to analyze the effects of KLRB1 on LUAD cell migration, invasion and proliferation. Results: KLRB1 expression was lower in lung cancer tissue than in surrounding healthy tissue. Genes differentially expressed in the low and high KLRB1 expression groups were found to be significantly enriched in pathways related to immunity. KLRB1 exerted an impact on the MAPK/ERK signaling pathway, thereby modulating the growth and proliferation of LUAD cells. KLRB1 expression is linked to prognosis, immune infiltration, and cell migration and proliferation in LUAD. Conclusions: The evidence revealed a correlation between KLRB1 and both prognosis and immune infiltration in LUAD patients.

Identification of a combined hypoxia and lactate metabolism prognostic signature in lung adenocarcinoma : Author.

BACKGROUND: In the tumor microenvironment (TME), a bidirectional relationship exists between hypoxia and lactate metabolism, with each component exerting a reciprocal influence on the other, forming an inextricable link. The aim of the present investigation was to develop a prognostic model by amalgamating genes associated with hypoxia and lactate metabolism. This model is intended to serve as a tool for predicting patient outcomes, including survival rates, the status of the immune microenvironment, and responsiveness to therapy in patients with lung adenocarcinoma (LUAD). METHODS: Transcriptomic sequencing data and patient clinical information specific to LUAD were obtained from comprehensive repositories of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A compendium of genes implicated in hypoxia and lactate metabolism was assembled from an array of accessible datasets. Univariate and multivariate Cox regression analyses were employed. Additional investigative procedures, including tumor mutational load (TMB), microsatellite instability (MSI), functional enrichment assessments and the ESTIMATE, CIBERSORT, and TIDE algorithms, were used to evaluate drug sensitivity and predict the efficacy of immune-based therapies. RESULTS: A novel prognostic signature comprising five lactate and hypoxia-related genes (LHRGs), PKFP, SLC2A1, BCAN, CDKN3, and ANLN, was established. This model demonstrated that LUAD patients with elevated LHRG-related risk scores exhibited significantly reduced survival rates. Both univariate and multivariate Cox analyses confirmed that the risk score was a robust prognostic indicator of overall survival. Immunophenotyping revealed increased infiltration of memory CD4 + T cells, dendritic cells and NK cells in patients classified within the high-risk category compared to their low-risk counterparts. Higher probability of mutations in lung adenocarcinoma driver genes in high-risk groups, and the MSI was associated with the risk-score. Functional enrichment analyses indicated a predominance of cell cycle-related pathways in the high-risk group, whereas metabolic pathways were more prevalent in the low-risk group. Moreover, drug sensitivity analyses revealed increased sensitivity to a variety of drugs in the high-risk group, especially inhibitors of the PI3K-AKT, EGFR, and ELK pathways. CONCLUSIONS: This prognostic model integrates lactate metabolism and hypoxia parameters, offering predictive insights regarding survival, immune cell infiltration and functionality, as well as therapeutic responsiveness in LUAD patients. This model may facilitate personalized treatment strategies, tailoring interventions to the unique molecular profile of each patient's disease.

Silencing of the long non-coding RNA LINC00265 triggers autophagy and apoptosis in lung cancer by reducing protein stability of SIN3A oncogene.

Background: Long non-coding RNAs are important regulators in cancer biology and function either as tumor suppressors or as oncogenes. Their dysregulation has been closely associated with tumorigenesis. LINC00265 is upregulated in lung adenocarcinoma and is a prognostic biomarker of this cancer. However, the mechanism underlying its function in cancer progression remains poorly understood. Methods: Here, the regulatory role of LINC00265 in lung adenocarcinoma was examined using lung cancer cell lines, clinical samples, and xenografts. Results: We found that high levels of LINC00265 expression were associated with shorter overall survival rate of patients, whereas knockdown of LINC00265 inhibited proliferation of cancer cell lines and tumor growth in xenografts. Western blot and flow cytometry analyses indicated that silencing of LINC00265 induced autophagy and apoptosis. Moreover, we showed that LINC00265 interacted with and stabilized the transcriptional co-repressor Switch-independent 3a (SIN3A), which is a scaffold protein functioning either as a tumor repressor or as an oncogene in a context-dependent manner. Silencing of SIN3A also reduced proliferation of lung cancer cells, which was correlated with the induction of autophagy. These observations raise the possibility that LINC00265 functions to promote the oncogenic activity of SIN3A in lung adenocarcinoma. Conclusions: Our findings thus identify SIN3A as a LINC00265-associated protein and should help to understand the mechanism underlying LINC00265-mediated oncogenesis.

LncRNA CYTOR knockdown inhibits tumor development via regulating miR-503-5p/PCSK9 in lung adenocarcinoma.

BACKGROUND: The intricate biological mechanism underlying lung adenocarcinoma (LUAD), characterized by a deficiency of distinctive biomarkers, remain elusive. The presence of Long non-coding RNAs (lncRNAs) have been established to play a role in carcinogenesis. Nevertheless, the regulatory effects and mechanisms of lncRNA CYTOR in LUAD have yet to be elucidated. METHODS: In this study, RT-qPCR and Western blot were adopted to examine gene mRNA and protein expression, respectively. Cell proliferation was evaluated by CCK-8 assays. Transwell was performed to assay cell migration and invasion. The function of CYTOR in vivo was investigated through a xenograft animal model. RESULTS: We observed an apparent upregulation of CYTOR in LUAD. Silencing CYTOR significantly reduced proliferation, migration, and invasion capabilities of LUAD cells. Mechanism analysis indicated that CYTOR targeted the miR-503-5p/PCSK9 axis. Additionally, inhibiting of miR-503-5p partially reversed the inhibitory effects of CYTOR silencing on the malignant progression of LUAD cells. Animal experiments revealed that CYTOR/miR-503-5p/PCSK9 curbed tumor formation of nude mice in vivo. CONCLUSION: These findings demonstrated that lncRNA CYTOR acted as an oncogene in LUAD, regulating tumor malignant progression through the miR-503-5p/PCSK9 axis. This study unveiled a new regulation mechanism of LUAD progression, offering potential therapeutic targets for LUAD.

Knockdown of GNL3 inhibits LUAD cell growth by regulating Wnt-ß-catenin pathway.

BACKGROUND: Lung adenocarcinoma (LUAD) is a leading cause of tumor-associated mortality, and it is needed to find new target to combat this disease. Guanine nucleotide-binding -protein-like 3 (GNL3) mediates cell proliferation and apoptosis in several cancers, but its role in LUAD remains unclear. OBJECTIVE: To explore the expression and function of Guanine nucleotide-binding protein-like 3 (GNL3) in lung adenocarcinoma (LUAD) and its potential mechanism in inhibiting the growth of LUAD cells. METHODS: We evaluated the expression of GNL3 in LUAD tissues and its association with patient prognosis using databases and immunohistochemistry. Cell proliferation was assessed by CCK-8 assay as well as colony formation, while apoptosis was evaluated by FCM. The effect of GNL3 knockdown on the Wnt/ß-catenin axis was investigated by Immunoblot analysis. RESULTS: GNL3 is overexpressed in LUAD tissues and is correlated with poor prognosis. Knockdown of GNL3 significantly inhibited the growth as well as induced apoptosis in A549 as well as H1299 cells. Furthermore, we found that the inhibitory effect of GNL3 knockdown on LUAD cell growth is associated with the downregulation of the Wnt/ß-catenin axis. CONCLUSION: GNL3 is key in the progression of LUAD by metiating Wnt/ß-catenin axis. Targeting GNL3 may represent a novel therapeutic method for LUAD treatment.

Peptidyl-prolyl isomerase F as a prognostic biomarker associated with immune infiltrates and mitophagy in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is among the most prevalent malignancies worldwide, with unfavorable treatment outcomes. Peptidyl-prolyl isomerase F (PPIF) is known to influence the malignancy traits of tumor progression by modulating the bioenergetics and mitochondrial permeability in cancer cells; however, its role in LUAD remains unclear. Our study seeks to investigate the clinical significance, tumor proliferation, and immune regulatory functions of PPIF in LUAD. Methods: The expression of PPIF in LUAD tissues and cells was assessed using bioinformatics analysis, immunohistochemistry (IHC), and Western blotting. Survival curve analysis was conducted to examine the prognostic association between PPIF expression and LUAD. The immunomodulatory role of PPIF in LUAD was assessed through the analysis of PPIF expression and immune cell infiltration. A series of gain- and loss-of-function experiments were conducted on PPIF to investigate its biological functions in LUAD both in vitro and in vivo. The mechanisms underlying PPIF's effects on LUAD were delineated through functional enrichment analysis and Western blotting assays. Results: PPIF exhibited overexpression in LUAD tissues compared to normal controls. Survival curve analysis revealed that patients with LUAD exhibiting higher PPIF expression demonstrated decreased overall survival and a shorter progression-free interval. PPIF was implicated in modulating immune cell infiltration, particularly in regulating the T helper 1-T helper 2 cell balance. Functionally, PPIF was discovered to promote tumor cell proliferation and advance cell-cycle progression. Furthermore, PPIF could impede mitophagy by targeting the FOXO3a/PINK1-Parkin signaling pathway. Conclusions: The findings of this study indicate that the prognosis-related gene PPIF may have a significant role in the regulation of LUAD cell proliferation, tumor-associated immune cell infiltration, and mitophagy, and thus PPIF may be a promising therapeutic target of LUAD.

PSME3 promotes lung adenocarcinoma development by regulating the TGF-ß/SMAD signaling pathway.

Background: Lung adenocarcinoma (LUAD) is one of the most common types of cancer worldwide. Proteasome activator subunit 3 (PSME3) is a subunit of a proteasome activator, and changes in PSME3 can lead to the development of many diseases in organisms. However, the specific mechanism of PSME3 in LUAD has not yet been elucidated. This study initially revealed the mechanism of PSME3 promoting the progression of lung adenocarcinoma, which provided a potential molecular target for clinical treatment. Methods: PSME3 expression in LUAD cells and tissues was assessed by bioinformatics analysis, immunohistochemistry (IHC), Western blotting (WB), and quantitative real time polymerase chain reaction (qRT-PCR). A series of functional experiments were used to evaluate the effects of PSME3 knockdown and overexpression on LUAD cell proliferation, migration, and apoptosis. The potential mechanism of PSME3 was explored by transcriptome sequencing and WB experiments. Results: In this study, our initial findings indicated that PSME3 expression was abnormally high in LUAD and was associated with poor patient prognosis. Further, we found that the downregulation of PSME3 significantly inhibited LUAD cell proliferation, an effect that was verified by subcutaneous tumor formation experiments in nude mice. Similarly, the rate of invasion and migration of LUAD cells significantly decreased after the downregulation of PSME3. Using flow cytometry, we found that the knockdown of PSME3 caused cell cycle arrest at the G1/S phase. Through transcriptome sequencing, we found that the transforming growth factor-beta (TGF-ß)/SMAD signaling pathway was closely related to LUAD, and we then validated the pathway using WB assays. Conclusions: We demonstrated that PSME3 was abnormally highly expressed in LUAD and related to poor patient prognosis; therefore, targeting PSME3 in the treatment of LUAD may represent a novel therapeutic approach.

Bioinformatics analysis of an immunotherapy responsiveness-related gene signature in predicting lung adenocarcinoma prognosis.

Background: Immune therapy has become first-line treatment option for patients with lung cancer, but some patients respond poorly to immune therapy, especially among patients with lung adenocarcinoma (LUAD). Novel tools are needed to screen potential responders to immune therapy in LUAD patients, to better predict the prognosis and guide clinical decision-making. Although many efforts have been made to predict the responsiveness of LUAD patients, the results were limited. During the era of immunotherapy, this study attempts to construct a novel prognostic model for LUAD by utilizing differentially expressed genes (DEGs) among patients with differential immune therapy responses. Methods: Transcriptome data of 598 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) database, which included 539 tumor samples and 59 normal control samples, with a mean follow-up time of 29.69 months (63.1% of patients remained alive by the end of follow-up). Other data sources including three datasets from the Gene Expression Omnibus (GEO) database were analyzed, and the DEGs between immunotherapy responders and nonresponders were identified and screened. Univariate Cox regression analysis was applied with the TCGA cohort as the training set and GSE72094 cohort as the validation set, and least absolute shrinkage and selection operator (LASSO) Cox regression were applied in the prognostic-related genes which fulfilled the filter criteria to establish a prognostic formula, which was then tested with time-dependent receiver operating characteristic (ROC) analysis. Enriched pathways of the prognostic-related genes were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and tumor immune microenvironment (TIME), tumor mutational burden, and drug sensitivity tests were completed with appropriate packages in R (The R Foundation of Statistical Computing). Finally, a nomogram incorporating the prognostic formula was established. Results: A total of 1,636 DEGs were identified, 1,163 prognostic-related DEGs were extracted, and 34 DEGs were selected and incorporated into the immunotherapy responsiveness-related risk score (IRRS) formula. The IRRS formula had good performance in predicting the overall prognoses in patients with LUAD and had excellent performance in prognosis prediction in all LUAD subgroups. Moreover, the IRRS formula could predict anticancer drug sensitivity and immunotherapy responsiveness in patients with LUAD. Mechanistically, immune microenvironments varied profoundly between the two IRRS groups; the most significantly varied pathway between the high-IRRS and low-IRRS groups was ribonucleoprotein complex biogenesis, which correlated closely with the TP53 and TTN mutation burdens. In addition, we established a nomogram incorporating the IRRS, age, sex, clinical stage, T-stage, N-stage, and M-stage as predictors that could predict the prognoses of 1-year, 3-year, and 5-year survival in patients with LUAD, with an area under curve (AUC) of 0.718, 0.702, and 0.68, respectively. Conclusions: The model we established in the present study could predict the prognosis of LUAD patients, help to identify patients with good responses to anticancer drugs and immunotherapy, and serve as a valuable tool to guide clinical decision-making.

Correlation analysis between driver gene mutation and clinicopathological features in lung adenocarcinoma based on real-world cumulative clinical data.

Background: Driver genes are essential predictors of targeted therapeutic efficacy. Detecting driver gene mutations in lung adenocarcinoma (LUAD) patients can help to screen for targeted drugs and improve patient survival benefits. This study aims to investigate the mutation characterization of driver genes and their correlation with clinicopathological features in LUAD. Methods: A total of 440 LUAD patients were selected from Sir Run Run Shaw Hospital between July 2019 and September 2022. Postoperative tissue specimens were analyzed for gene mutations using next-generation sequencing technology, focusing, including epidermal growth factor receptor EGFR, ALK, ROS1, RET, KRAS, MET, BRAF, HER2, PIK3CA and NRAS. At the same time, clinicopathological data were collected and organized for multidimensional correlation analysis. Results: Of 440 LUAD patients, driver gene mutations were not detected in 48 patients. The proportion of patients with driver gene mutations was as high as 89.09%. The top three driver genetic mutations were EGFR, KRAS, and MET. Sixty-nine types of EGFR mutations were detected and distributed in the protein tyrosine kinase catalytic domain (56, 81.16%), Furin-like cysteine-rich region (9, 13.04%), receptor binding domain (3, 4.35%), and EGFR transmembrane domain (1, 1.45%). Single gene locus mutation occurred in 343 LUAD patients, but the mutation gene types covered all tested genes. Our findings showed that EGFR mutations were more commonly observed in non-smoking and female patients (P<0.01), KRAS mutations were more prevalent in male patients and smokers (P<0.01), ROS1 mutations had larger tumor diameters (P<0.01) and RET mutations were more prevalent in smokers (P<0.05). Conclusions: LUAD patients exhibit diverse genetic mutations, which may co-occur simultaneously. Integrated analysis of multiple mutations is essential for accurate diagnosis and effective treatment of the disease. The use of NGS can significantly expand our understanding of gene mutations and facilitate integrated analysis of multiple gene mutations, providing critical evidence for targeted treatment methods.

Spinosad blocks CHRNA5 mediated EGFR signaling pathway activation to inhibit lung adenocarcinoma proliferation.

Lung adenocarcinoma (LUAD) is the leading cause of cancer death worldwide, with high incidence and low survival rates. Nicotinic acetylcholine receptors play an important role in the progression of LUAD. In this study, a screening of 17 nicotinic acetylcholine receptor allosteric agents revealed that spinosad effectively suppressed the proliferation of LUAD cells. The experiments demonstrated that spinosad induced cell cycle arrest in the G1 phase and stimulated apoptosis, thereby impeding the growth of LUAD and enhancing the responsiveness to gefitinib in vitro and vivo. Mechanistic insights obtained through transcriptome sequencing, Co-IP, and protein immunoblots indicated that spinosad disrupted the interaction between CHRNA5 and EGFR, thereby inhibiting the formation of downstream complexes and activation of the EGFR signaling pathway. The supplementation of exogenous acetylcholine showed to mitigate the inhibition of LUAD cell proliferation induced by spinosad. This study elucidates the therapeutic effects and mechanisms of spinosad in LUAD, and offers a theoretical and experimental foundation for novel LUAD treatments.

Integrative metagenomic, transcriptomic, and proteomic analysis reveal the microbiota-host interplay in early-stage lung adenocarcinoma among non-smokers.

BACKGROUND: The incidence of early-stage lung adenocarcinoma (ES-LUAD) is steadily increasing among non-smokers. Previous research has identified dysbiosis in the gut microbiota of patients with lung cancer. However, the local microbial profile of non-smokers with ES-LUAD remains largely unknown. In this study, we systematically characterized the local microbial community and its associated features to enable early intervention. METHODS: A prospective collection of ES-LUAD samples (46 cases) and their corresponding normal tissues adjacent to the tumor (41 cases), along with normal lung tissue samples adjacent to pulmonary bullae in patients with spontaneous pneumothorax (42 cases), were subjected to ultra-deep metagenomic sequencing, host transcriptomic sequencing, and proteomic sequencing. The obtained omics data were subjected to both individual and integrated analysis using Spearman correlation coefficients. RESULTS: We concurrently detected the presence of bacteria, fungi, and viruses in the lung tissues. The microbial profile of ES-LUAD exhibited similarities to NAT but demonstrated significant differences from the healthy controls (HCs), characterized by an overall reduction in species diversity. Patients with ES-LUAD exhibited local microbial dysbiosis, suggesting the potential pathogenicity of certain microbial species. Through multi-omics correlations, intricate local crosstalk between the host and local microbial communities was observed. Additionally, we identified a significant positive correlation (rho > 0.6) between Methyloversatilis discipulorum and GOLM1 at both the transcriptional and protein levels using multi-omics data. This correlated axis may be associated with prognosis. Finally, a diagnostic model composed of six bacterial markers successfully achieved precise differentiation between patients with ES-LUAD and HCs. CONCLUSIONS: Our study depicts the microbial spectrum in patients with ES-LUAD and provides evidence of alterations in lung microbiota and their interplay with the host, enhancing comprehension of the pathogenic mechanisms that underlie ES-LUAD. The specific model incorporating lung microbiota can serve as a potential diagnostic tool for distinguishing between ES-LUAD and HCs.

Identification of a novel prognostic signature based on vitamin metabolism clustering-related genes in lung adenocarcinoma.

Background: Vitamins, and their metabolic processes play essential regulatory roles in controlling proliferation, differentiation, and growth in carcinogenesis. However, the role of vitamin metabolism in lung adenocarcinoma (LUAD) has rarely been reported. Here, we established a novel prognostic model based on vitamin metabolism-related genes in LUAD. Methods: In this research, we aimed to identify vitamin metabolism associated with differentially expressed genes (DEGs) in LUAD utilizing The Cancer Genome Atlas (TCGA)-LUAD, GSE68465 and GSE72094 data. Unsupervised clustering classified patients into distinct subgroups. By utilizing least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, vitamin metabolism-related genes could be used to construct prognostic model. Then the vitamin metabolism gene-related risk score (VRS) was calculated based on best cut-off splitting. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) analysis, univariate and multivariate Cox analyses, chemotherapeutic drugs sensitivity analysis, immune infiltration analysis and nomogram were conducted to verify our models' accuracy. Finally, CPS1 was identified as a relevant diagnostic marker using Random Forests algorithms, single-cell RNA sequencing data was used to confirm its expression. Results: We investigated the relationship between vitamin metabolism patterns, overall survival (OS), and immune infiltration levels of patients with LUAD. A prognostic signature consisting of 11 genes was developed, which was able to classify patients into high and low VRS groups. Through gene enrichment analysis, cell cycle was mainly enriched. Compared to the low VRS group, the high VRS group exhibited poorer OS, as demonstrated by the Kaplan-Meier survival analysis. Furthermore, VRS was identified as an independent predictor of poor prognosis and poor OS, as indicated by both univariate and multivariate Cox regression analyses. Additionally, a nomogram was constructed to improve the accuracy of survival predictions in LUAD patients. We also found that the two groups of patients might respond differently to immune targets and anti-tumor drugs. CPS1 was identified as a relevant diagnostic marker and the expression was also as confirmed by single-cell RNA sequencing data. Conclusions: Overall, our findings suggest that vitamin metabolism can influence the prognosis of LUAD patients, and our prognostic signature represents a potentially helpful resource for predicting patient outcomes and informing clinical decision-making.

Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature.

We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.

Identification of KRT80 as a Novel Prognostic and Predictive Biomarker of Human Lung Adenocarcinoma via Bioinformatics Approaches.

BACKGROUND: According to the 2022 Global Cancer Statistics, lung cancer is the leading cause of cancer-related mortality worldwide. Lung adenocarcinoma (LUAD), which is a histological subtype of Non- Small Cell Lung Cancer (NSCLC), accounts for 40% of primary lung cancer. Therefore, there is an urgent need to identify new prognostic markers as clinical predictive markers for LUAD. OBJECTIVE: This study aimed to investigate the role of Keratin 80 (KRT80) in the prognosis of LUAD and its underlying mechanisms. METHODS: Bioinformatics analysis was conducted using data retrieved from The Cancer Genome Atlas (TCGA) databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were employed to predict the involved biological processes and signaling pathways, respectively. The LinkedOmics database was utilized to identify differentially expressed genes (DEGs) correlated with KRT80. Nomograms and Kaplan-Meier plots were constructed to evaluate the survival outcomes of patients diagnosed with LUAD. Moreover, TIMER was employed to conduct correlation analyses between KRT80 expression and immune cell infiltration, shedding light on the intricate interplay between KRT80 and the tumor microenvironment in LUAD. To ascertain the RNA and protein expression levels of KRT80 in LUAD and adjacent normal tissues, Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) and immunohistochemistry techniques were employed, respectively. RESULTS: Scrutiny of the TCGA dataset revealed KRT80 up-regulation across pan-cancer tissues, notably elevated in LUAD compared to healthy lung tissues. This finding was validated in our clinical samples, where Kaplan-Meier survival curves indicated poorer survival rates for high KRT80 expression in LUAD. A positive correlation was found between the transcription level of KRT80 in LUAD samples and clinical parameters, such as lymph node metastasis stage, distant metastasis, and pathological stage. Survival, logistic regression, and Cox regression analyses emphasized the clinical prognostic significance of high KRT80 expression in LUAD. Nomogram results underscored the robust predictive potential of KRT80 for the survival of LUAD patients. Gene functional enrichment analyses mainly associated KRT80 with cytokine-cytokine receptor interactions, cell cycle, apoptosis, and chemokine signaling pathways. Based on the results of the immune infiltration analysis, it can be found that the expression of KRT80 is related to the immune cell subsets and survival rate of patients with LUAD. CONCLUSION: Our research revealed a significant upregulation of KRT80 in LUAD, with heightened KRT80 expression correlating with unfavorable prognosis. This study represents a comprehensive and systematic evaluation of KRT80 expression in LUAD, encompassing its prognostic and diagnostic significance, as well as underlying mechanisms. Our findings suggest that KRT80 may emerge as a novel prognostic and predictive biomarker in LUAD.

Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung.

Purpose: In situations where pathological acquisition is difficult, there is a lack of consensus on distinguishing between adenocarcinoma and squamous cell carcinoma from imaging images, and each doctor can only make judgments based on their own experience. This study aims to extract imaging features of chest CT, extract sensitive factors through logistic univariate and multivariate analysis, and model to distinguish between lung squamous cell carcinoma and lung adenocarcinoma. Methods: We downloaded chest CT scans with clear diagnosis of adenocarcinoma and squamous cell carcinoma from The Cancer Imaging Archive (TCIA), extracted 19 imaging features by a radiologist and a thoracic surgeon, including location, spicule, lobulation, cavity, vacuolar sign, necrosis, pleural traction sign, vascular bundle sign, air bronchogram sign, calcification, enhancement degree, distance from pulmonary hilum, atelectasis, pulmonary hilum and bronchial lymph nodes, mediastinal lymph nodes, interlobular septal thickening, pulmonary metastasis, adjacent structures invasion, pleural effusion. Firstly, we apply the glm function of R language to perform logistic univariate analysis on all variables to select variables with P < 0.1. Then, perform logistic multivariate analysis on the selected variables to obtain a predictive model. Next, use the roc function in R language to calculate the AUC value and draw the ROC curve, use the val.prob function in R language to draw the Calibrat curve, and use the rmda package in R language to draw the DCA curve and clinical impact curve. At the same time, 45 patients diagnosed with lung squamous cell carcinoma and lung adenocarcinoma through surgery or biopsy in the Radiotherapy Department and Thoracic Surgery Department of our hospital from 2023 to 2024 were included in the validation group. The chest CT features were jointly determined and recorded by the two doctors mentioned above and included in the validation group. The included image feature data are complete and does not require preprocessing, so directly entering statistical calculations. Perform ROC curves, calibration curves, DCA, and clinical impact curves in the validation group to further validate the predictive model. If the predictive model performs well in the validation group, further draw a nomogram to demonstrate. Results: This study extracted 19 imaging features from the chest CT scans of 75 patients downloaded from TCIA and finally selected 18 complete data for analysis. First, univariate analysis and multivariate analysis were performed, and a total of 5 variables were obtained: spicule, necrosis, air bronchogram Sign, atelectasis, pulmonary hilum and bronchial lymph nodes. After conducting modeling analysis with AUC = 0.887, a validation group was established using clinical cases from our hospital, Draw ROC curve with AUC = 0.865 in the validation group, evaluate the accuracy of the model through Calibrate calibration curve, evaluate the reliability of the model in clinical practice through DCA curve, and further evaluate the practicality of the model in clinical practice through clinical impact curve. Conclusion: It is possible to extract influential features from ordinary chest CT scans to determine lung adenocarcinoma and squamous cell carcinoma. The model we have set up performs well in terms of discrimination, accuracy, reliability, and practicality.

GLUT1 promotes cell proliferation via binds and stabilizes phosphorylated EGFR in lung adenocarcinoma.

BACKGROUND: While previous studies have primarily focused on Glucose transporter type 1 (GLUT1) related glucose metabolism signaling, we aim to discover if GLUT1 promotes tumor progression through a non-metabolic pathway. METHODS: The RNA-seq and microarray data were comprehensively analyzed to evaluate the significance of GLUT1 expression in lung adenocarcinoma (LUAD). The cell proliferation, colony formation, invasion, and migration were used to test GLUT1 's oncogenic function. Co-immunoprecipitation and mass spectrum (MS) were used to uncover potential GLUT1 interacting proteins. RNA-seq, DIA-MS, western blot, and qRT-PCR to probe the change of gene and cell signaling pathways. RESULTS: We found that GLUT1 is highly expressed in LUAD, and higher expression is related to poor patient survival. GLUT1 knockdown caused a decrease in cell proliferation, colony formation, migration, invasion, and induced apoptosis in LUAD cells. Mechanistically, GLUT1 directly interacted with phosphor-epidermal growth factor receptor (p-EGFR) and prevented EGFR protein degradation via ubiquitin-mediated proteolysis. The GLUT1 inhibitor WZB117 can increase the sensitivity of LUAD cells to EGFR-tyrosine kinase inhibitors (TKIs) Gefitinib. CONCLUSIONS: GLUT1 expression is higher in LUAD and plays an oncogenic role in lung cancer progression. Combining GLUT1 inhibitors and EGFR-TKIs could be a potential therapeutic option for LUAD treatment.

Significance of Ribonucleoside-diphosphate Reductase Subunit M2 in Lung Adenocarcinoma.

INTRODUCTION: The Ribonucleoside-diphosphate Reductase subunit M2 (RRM2) is known to be overexpressed in various cancers, though its specific functional implications remain unclear. This aims to elucidate the role of RRM2 in the progression of Lung Adenocarcinoma (LUAD) by exploring its involvement and potential impact. METHODS: RRM2 data were sourced from multiple databases to assess its diagnostic and prognostic significance in LUAD. We evaluated the association between RRM2 expression and immune cell infiltration, analyzed its function, and explored the effects of modulating RRM2 expression on LUAD cell characteristics through laboratory experiments. RESULTS: RRM2 was significantly upregulated in LUAD tissues and cells compared to normal counterparts (p<0.05), with rare genetic alterations noted (approximately 2%). This overexpression clearly distinguished LUAD from normal tissue (area under the curve (AUC): 0.963, 95% confidence intervals (CI): 0.946-0.981). Elevated RRM2 expression was significantly associated with adverse clinicopathological characteristics and poor prognosis in LUAD patients. Furthermore, a positive association was observed between RRM2 expression and immune cell infiltration. Pathway analysis revealed a critical connection between RRM2 and the cell cycle signaling pathway within LUAD. Targeting RRM2 inhibition effectively suppressed LUAD cell proliferation, migration, and invasion while promoting apoptosis. This intervention also modified the expression of several crucial proteins, including the downregulation of CDC25A, CDC25C, RAD1, Bcl-2, and PPM1D and the upregulation of TP53 and Bax (p < 0.05). CONCLUSION: Our findings highlight the potential utility of RRM2 expression as a biomarker for diagnosing and predicting prognosis in LUAD, shedding new light on the role of RRM2 in this malignancy.