Prognosis Of Lung Adenocarcinoma Research Articles

An exosomes-related lncRNA prognostic model correlates with the immune microenvironment and therapy response in lung adenocarcinoma.

Recent research highlights the significance of exosomes and long noncoding RNAs (lncRNAs) in cancer progression and drug resistance, but their role in lung adenocarcinoma (LUAD) is not fully understood. We analyzed 121 exosome-related (ER) mRNAs from the ExoBCD database, along with mRNA and lncRNA expression profiles of TCGA-LUAD using "DESeq2", "survival," "ConsensusClusterPlus," "GSVA," "estimate," "glmnet," "clusterProfiler," "rms," and "pRRophetic" R packages. This comprehensive approach included univariate cox regression, unsupervised consensus clustering, GSEA, functional enrichment analysis, and prognostic model construction. Our study identified 134 differentially expressed ER-lncRNAs, with 19 linked to LUAD prognosis. These ER-lncRNAs delineated two patient subtypes, one with poorer outcomes. Additionally, 286 differentially expressed genes were related to these ER-lncRNAs, 261 of which also correlated with LUAD prognosis. We constructed an ER-lncRNA-related prognostic model and calculated an ER-lncRNA-related risk score (ERS), revealing that a higher ERS correlates with poor overall survival in both the Meta cohort and two validation cohorts. The ERS potentially serves as an independent prognostic factor, and the prognostic model demonstrates superior predictive power. Notably, significant differences in the immune landscape were observed between the high- and low-ERS groups. Drug sensitivity analysis indicated varying responses to common chemotherapy drugs based on ERS stratification, with the high-ERS group showing greater sensitivity, except to rapamycin and erlotinib. Experimental validation confirmed that thymidine kinase 1 enhances lung cancer invasion, metastasis, and cell cycle progression. Our study pioneers an ER-lncRNA-related prognostic model for LUAD, proposing that ERS-based risk stratification could inform personalized treatment strategies to improve patient outcomes.

Unraveling Immune Heterogeneity across Pan-Cancer and Deep Insights in Lung Adenocarcinoma based on Alternative Splicing.

Alternative splicing (AS) participates in tumor development and tumor microenvironment formation. However, the landscape of immune infiltrating AS events (IIASE) in pan-cancer and mechanisms of AS in lung adenocarcinoma (LUAD) have not been comprehensively characterized. We systematically profiled the IIASE landscape of pan-cancer using data from The Cancer Genome Atlas (TCGA), analyzing both commonalities and specific characteristics among different cancer types. We found that AS events tend to occur specifically in one cancer type rather than in multiple cancer types. AS events were used to classify 512 LUAD samples into two subtypes by unsupervised clustering: aberrant splicing subtype (ABS) and immune infiltrating subtype (IIS). The two subtypes showed significant differences in clinicopathology, prognosis, transcriptomics, genomics and immune microenvironment. We constructed a classification signature comprising 10 genes involved in 14 AS events using Logistic regression. The robustness of the signature was validated in three independent datasets using survival analysis. To explore AS mechanisms in LUAD, we constructed subtype-specific co-expression networks using Pearson correlation analysis. AS event of AKT3 regulated by splicing factor ENOX1 was associated with poor prognosis in LUAD. Overall, we outline AS events associated with immune infiltration in pan-cancer and this study provides insights into AS mechanisms in LUAD patient classification.

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms.

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

Implications of GCLC in prognosis and immunity of lung adenocarcinoma and multi-omics regulation mechanisms

BackgroundFerroptosis is an iron-dependent type of regulated cell death, and has been implicated in lung adenocarcinoma (LUAD). Evidence has proved the key role of glutamate-cysteine ligase catalytic subunit (GCLC) in ferroptosis, but its role in LUAD remains unclear. Herein, we explored the implications of GCLC and relevant genes in LUAD prognosis and immunity as well as underlying molecular mechanisms.MethodsThis work gathered mRNA, miRNA, DNA methylation, somatic mutation and copy-number variation data from TCGA-LUAD. WGCNA was utilized for selecting GCLC-relevant genes, and a GCLC-relevant prognostic signature was built by uni- and multivariate-cox regression analyses. Immune compositions were estimated via CIBERSORT, and two immunotherapy cohorts of solid tumors were analyzed. Multi-omics regulatory mechanisms were finally assessed.ResultsOur results showed that GCLC was overexpressed in LUAD, and potentially resulted in undesirable survival. A prognostic model was generated, which owned accurate and independent performance in prognostication. GCLC, and relevant genes were notably connected with immune compositions and immune checkpoints. High GCLC expression was linked with better responses to anti-PD-L1 and anti-CTLA-4 treatment. Their possible DNA methylation sites were inferred, e.g., hypomethylation in cg19740353 might contribute to GCLC up-regulation. Frequent genetic mutations also affected their expression. Upstream transcription factors (E2F1/3/4, etc.), post-transcriptional regulation of miRNAs (hsa-mir-30c-1, etc.), lncRNAs (C8orf34-AS1, etc.), and IGF2BP1-mediated m6A modification were identified. It was also found NOP58-mediated SUMOylation post-translational modification.ConclusionsTogether, we show that GCLC and relevant genes exert crucial roles in LUAD prognosis and immunity, and their expression can be controlled by complex multi-omics mechanisms.

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response

BackgroundPrevious studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures.MethodsThis study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD.ResultsUnsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients.ConclusionsICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma.

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan-Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

HMGN1 loss sensitizes lung cancer cells to chemotherapy

The high mobility group nucleosome binding (HMGN) family, constitutes a large family of non-histone protein family known to bind the acidic patch of the nucleosomes with various key cellular functions. Several studies have highlighted the pivotal roles of HMGNs in the pathogenic process of various cancer types. However, the roles of HMGN family in lung adenocarcinoma (LUAD) have not been fully elucidated. Herein, integrative analyses of multiple-omics data revealed that HMGNs frequently exhibit dysregulation in LUAD. Subsequent analysis of the clinical relevance of HMGN1 demonstrated its association with poor prognosis in LUAD and its potential as a diagnostic marker to differentiate LUAD from healthy controls. Additionally, functional enrichment analysis suggested that HMGN1 was mainly involved in DNA repair. To corroborate these findings, cellular experiments were conducted, confirming HMGN1’s crucial involvement in homologous recombination repair and its potential to enhance the sensitivity of LUAD cells to standard chemotherapeutic drugs. This study proposes HMGN1 as a novel prognostic biomarker and a promising target for chemotherapy in lung adenocarcinoma.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

IL1R2 is a Novel Prognostic Biomarker for Lung Adenocarcinoma.

The aim of this study is to figure out the role of IL1R2 in LUAD (lung adenocarcinoma). IL1R2, a special member of IL-1 receptor family, binds to IL-1 and plays an important role in inhibiting IL-1 pathway, which seems to be involved in tumorigenesis. Emerging studies demonstrated higher IL1R2 expression levels in several malignancies. In the present study, we assessed the expression of IL1R2 in LUAD tissues with immunohistochemistry and explored various databases to determine whether it could be a potential prognostic biomarker and therapeutic target. The expression level of IL1R2 in lung adenocarcinoma was analyzed by Immunohistochemistry and UALCAN database. The correlation between IL1R2 expression and the patient prognosis was identified by Kaplan-Meier plotter. The correlation of IL1R2 expression with immune infiltrates was clarified by TIMER database. The protein-protein interaction network and gene functional enrichment analysis were constructed and performed by STRING and Metascape database. Immunohistochemistry showed that the expression of IL1R2 was higher in tumor tissues of LUAD patients and that patients with lower IL1R2 level have a better prognosis than their counterparts. We validated our findings in several online databases and found that IL1R2 gene was also positively correlated with B cells and neutrophils and biomarkers of CD8+T cells and exhausted T cells. PPI network and gene enrichment analyses showed that expression of IL1R2 was also associated with complex functionspecific networks involving IL-1 signal, NF-KappaB transcription factors. According to these findings, we demonstrated that IL1R2 was involved in the progression and prognosis of LUAD and the underlying mechanism needs further investigation.

DEP domain containing 1 as a biomarker for poor prognosis in lung adenocarcinoma

ObjectiveThe DEP domain-containing 1 (DEPDC1) gene is essential in the development and advancement of different types of cancer. This study is to examine the levels of DEPDC1 in lung adenocarcinoma (LUAD), and to determine its relationship with clinical results and immune response. The goal is to assess its potential as a biomarker and therapeutic target for LUAD. MethodsBy comprehensively utilizing the Cancer Genome Atlas (TCGA), gene Expression Synthesis (GEO), UALCAN, cBioPortal, TISIDB databases and online platforms, we conducted a bioinformatics analysis to investigate DEPDC1 gene survival analysis, prognostic diagnosis, prognostic survival, immune cell infiltration, DNA methylation, and the correlation of genetic mutations in LUAD. The results were validated through cell assay and immunohistochemical staining. ResultsDEPDC1 shows high levels of expression in the majority of tumors, with its expression being notably elevated in LUAD compablue to normal tissues. The expression of DEPDC1 varies based on the clinical characteristics of patients with LUAD. DEPDC1 expression affects the survival prognosis and prognostic model construction of LUAD patients. In addition, the presence of DEPDC1 is linked to immune infiltration. Various chemokines and chemokine receptors, immunoinhibitors and immune-stimulators in LUAD are significantly correlated with DEPDC1 methylation levels. Cell experiments confirmed through qPCR that the mRNA expression of DEPDC1 in LUAD was markedly elevated in comparison to the normal population, and immunohistochemistry showed positive DEPDC1 expression in LUAD pathological sections. ConclusionSystematic analysis and experiments have verified that DEPDC1 serves as a biomarker for detecting early, prediction of survival, and evaluation of immune cell infiltration in LUAD.

Screening of immune-related lncRNAs in lung adenocarcinoma and establishing a survival prognostic risk prediction model.

This study aimed to improve lung adenocarcinoma (LUAD) prognosis prediction based on a signature of immune-related long non-coding RNAs (lncRNAs). LUAD samples from the TCGA database were divided into the immunity_H group and the immunity_L group. Differentially expressed RNAs (DERs) between the two groups were identified. Optimized immune-related lncRNAs combination was obtained using LASSO Cox regression. A prognostic risk prediction (RS) model was built and further validated in the training and validation datasets. A network among lncRNAs in the RS model, their co-expressed DERs, and the related KEGG pathways were established. Critical lncRNAs were validated in LUAD tissue samples. In total, 255 DERs were obtained, and 11 immune-related lncRNAs were significantly related to prognosis. Six lncRNAs were demonstrated as an optimal combination for building the RS model, including LINC00944, LINC00930, LINC00607, LINC00582, LINC00543, and LINC00319. The KM curve and ROC curve revealed the RS model to be a reliable indicator for LUAD prognosis. LINC00944 and LINC00582 showed a co-expression relationship with the MS4A1. LINC00944, LINC00582, and MS4A1 were successfully validated in LUAD samples. We have established a promising LUAD patient survival prediction model based on six immune-related lncRNAs. For LUAD patients, this prognostic model could guide personalized treatment.

Sublobectomy and lymph node sampling are adequate for patients with invasive lung adenocarcinoma presenting as pure ground glass nodules.

In this study, we aimed to investigate the prognosis of invasive lung adenocarcinoma that manifests as pure ground glass nodules (pGGNs) and confirm the effectiveness of sublobectomy and lymph node sampling in patients with pGGN-featured invasive adenocarcinoma (IAC). We retrospectively enrolled 139 patients with pGGN-featured IAC, who underwent complete resection in two medical institutions between January 2011 and May 2022. Stratification analysis was conducted to ensure balanced baseline characteristics among the patients. The 5-year overall survival (OS) and disease-free survival (DFS) rates were compared between the groups using Kaplan-Meier survival curves and log-rank test. The 5-year OS and DFS rates for patients with IAC presenting as pGGNs after surgery were 96.5% and 100%, respectively. No lymph node metastasis or recurrence was observed in any of the enrolled patients. There was no statistically significant difference in the 5-year OS between patients who underwent lobectomy or sublobectomy, along with lymph node resection or sampling. IAC presented as pGGNs exhibited low-grade malignancy and had a relatively good prognosis. Therefore, these patients may be treated with sublobectomy and lymph node sampling.

A liquid biopsy assay for the noninvasive detection of lymph node metastases in T1 lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common pathological type of lung cancer. The presence of lymph node metastasis plays a crucial role in determining the overall treatment approach and long-term prognosis for early LUAD, therefore accurate prediction of lymph node metastasis is essential to guide treatment decisions and ultimately improve patient outcomes. We performed transcriptome sequencing on T1 LUAD patients with positive or negative lymph node metastases and combined this data with The Cancer Genome Atlas Program cohort to identify potential risk molecules at the tissue level. Subsequently, by detecting the expression of these risk molecules by real-time quantitative PCR in serum samples, we developed a model to predict the risk of lymph node metastasis from a training cohort of 96 patients and a validation cohort of 158 patients. Through transcriptome sequencing analysis of tissue samples, we identified 11 RNA (miR-412, miR-219, miR-371, FOXC1, ID1, MMP13, COL11A1, PODXL2, CXCL13, SPOCK1 and MECOM) associated with positive lymph node metastases in T1 LUAD. As the expression of FOXC1 and COL11A1 was not detected in serum, we constructed a predictive model that accurately identifies patients with positive lymph node metastases using the remaining nine RNA molecules in the serum of T1 LUAD patients. In the training set, the model achieved an area under the curve (AUC) of 0.89, and in the validation set, the AUC was 0.91. We have established a new risk prediction model using serum samples from T1 LUAD patients, enabling noninvasive identification of those with positive lymph node metastases.

Comprehensive Analysis of a Dendritic Cell Marker Genes Signature to Predict Prognosis and Immunotherapy Response in Lung Adenocarcinoma.

With the development of immune checkpoints inhibitors (ICIs), immunotherapy has recently taken center stage in cancer treatment. Dendritic cells exert complicated and important functions in antitumor immunity. This study aims to construct a novel dendritic cell marker gene signature (DCMGS) to predict the prognosis and immunotherapy response of lung adenocarcinoma (LUAD). DC marker genes in LUAD were identified by analysis of single-cell RNA sequencing data. 6 genes (G0S2, KLF4, ALDH2, IER3, TXN, CD69) were screened as the most prognosis-related genes for constructing DCMGS on a training cohort from TCGA data set. Patients were divided into high-risk and low-risk groups by DCMGS risk score based on overall survival time. Then, the predictive ability of the risk model was validated in 6 independent cohorts. DCMGS was verified to be an independent prognostic factor in multivariate analysis. Furthermore, we performed pathway enrichment analysis to explore possible biological mechanisms of the powerful predictive ability of DCMGS, and immune cell infiltration landscape and inflammatory activities were exhibited to reflect the immune profile. Notably, we bridged DCMGS with expression of immune checkpoints and TCR/BCR repertoire diversity that can inflect immunotherapy response. Finally, the predictive ability of DCMGS in immunotherapy response was also validated by 2 cohorts that had received immunotherapy. As a result, the patients with lower DCMGS risk scores showed a better prognosis and immunotherapy response. In conclusion, DCMGS was suggested to be a promising prognostic indicator for LUAD and a desirable predictor for immunotherapy response.

Up-Regulation of MELK Promotes Cell Growth and Invasion by Accelerating G1/S Transition and Indicates Poor Prognosis in Lung Adenocarcinoma.

Maternal embryonic leucine zipper kinase (MELK) is an oncogene in many tumors, although its contribution to lung adenocarcinoma (LUAD) is unclear. We examined MELK expression in patient LUAD tissue and matched healthy lung tissues. We investigated the connection between MELK expression and tumor differentiation, lymph node metastasis, and patient survival. We downregulated MELK expression using small-hairpin RNA to assess its impact on LUAD cell proliferation, clonogenicity, and invasion. We also investigated the molecular mechanism underlying these effects. MELK expression was significantly heightened in LUAD tissue as opposed to the matching healthy lung tissues. LUAD patients who had MELK overexpression had a worse prognosis. Suppression of MELK hinders proliferation, clonogenicity, and invasion of LUAD cells. The MELK suppression led to the arrest of the cell cycle's G1/S phase by reducing the cyclin E1 and cyclin D expression. Our outcomes manifest that MELK can function as a beneficial prognostic indication and a new therapy target for LUAD. MELK has an essential function in progressing LUAD, manifesting potential as a viable target for therapeutic intervention in this disease management.

A Prognosis and Immunological Study of Cellular Aging Related lncRNAs in Lung Adenocarcinoma

Background: Recently, cellular senescence has been recognized as a novel hallmark of cancer. However, the mechanisms that control cellular senescence remain poorly understood. This study seeks to discover long non-coding RNAs (lncRNAs) that are linked to senescence and can predict outcomes in lung adenocarcinoma (LUAD) patients. Methods: Using RNA sequencing data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a group of lncRNAs related to senescence was initially identified. Subsequently, through univariate and multivariate Cox regression analyses, a senescence-related lncRNA signature (SenLncSig) linked to the prognosis of LUAD was developed. LUAD patients were categorized into high-risk and low-risk groups based on the median risk score of SenLncSig. Kaplan-Meier analysis was employed to assess differences in overall survival (OS) between these subgroups. Additionally, disparities in Gene Set Enrichment Analysis (GSEA), immune infiltration, scores from the tumor immune dysfunction and exclusion (TIDE) module, and choices of chemotherapy and targeted therapy were also evaluated between the high-risk and low-risk groups. Results: Based on univariate and multivariate Cox regression analyses, we established a prognostic model composed of seven senescence-related lncRNAs, named SenLncSig, which includes AL031775.2, AC026355.1, AL365181.2, AC090559.1, AL606489.1, AL513550.1, and C20orf197. This model divides patients into high-risk and low-risk groups, with the high-risk group showing significantly shorter overall survival compared to the low-risk group, with a hazard ratio (HR) of 1.326. The predictive accuracy of SenLncSig was validated through ROC curves and principal component analysis (PCA). Additionally, we developed an intuitive survival prediction tool that combines SenLncSig with clinical pathological features to assess patients' overall survival (OS). Enrichment analysis (GSEA) revealed that SenLncSig is involved in multiple pathways related to tumor immune modulation. Risk model analysis indicated significant differences between the high and low-risk groups in terms of immune status and responses to immunotherapy, chemotherapy, and targeted therapy. Conclusion: In this study, a lncRNA signature named as SenLncSig was developed that not only identifies the senescence phenotype and predicts prognosis but also has the capability to forecast the response of LUAD to immunotherapy.

The characteristics of intratumoral microbial community reflect the development of lung adenocarcinoma.

An increasing number of studies have demonstrated the pivotal role of microbiota changes in the onset, progression, diagnosis, treatment, and prognosis of lung adenocarcinoma (LUAD). However, a comprehensive analysis of intratumoral microbiome variation across distinct LUAD stages has not been performed. The aim of this study was to identify the microbial markers that significantly vary during tumor stage of LUAD. Here, we used the cancer genome atlas (TCGA) database to comprehensively compare and analyze the differences in microbial composition between 267 patients with early and 224 patients with advanced LUAD. In order to determine the best biomarkers, we used the random forest (RF) model and found that the microbial markers have a certain ability in predicting the stage of LUAD. We found that there were certain differences in the microbiome of patients with LUAD at different stages, especially in the tumor tissues of patients with advanced LUAD, whose co-abundance network was significantly more complex. We also found that five bacterial biomarkers (Pseudoalteromonas, Luteibacter, Caldicellulosiruptor, Loktanella, and Serratia) were correlated with LUAD stage, among which Pseudoalteromonas, Luteibacter, Caldicellulosiruptor, and Serratia were significantly overexpressed in patients with advanced LUAD. In particular, after integrating the biomarkers of mRNA, we achieved an area under the curve (AUC) of 0.70. Our study revealed the microbial profile of patients with LUAD and the intrinsic pathogenic mechanism between the microbiome and the disease, and established a multi-omics model to determine LUAD tumor stage.

Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma

Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The “BayesPrism” and “Seurat” R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.

Analysis of cancer-associated fibroblasts related genes identifies COL11A1 associated with lung adenocarcinoma prognosis

BackgroundThe treatment of lung adenocarcinoma is difficult due to the limited therapeutic options. Cancer-associated fibroblasts play an important role in the development of cancers. This study aimed to identify a promising molecular target associated with cancer-associated fibroblasts for the treatment of lung adenocarcinoma.MethodsThe Cancer Genome Atlas lung adenocarcinoma dataset was used to screen hub genes associated with cancer-associated fibroblasts via the EPIC algorithm and Weighted Gene Co-expression Network Analysis. Multiple databases were used together with our data to verify the differential expression and survival of COL11A1. Functional enrichment analysis and the single-cell TISCH database were used to elucidate the mechanisms underlying COL11A1 expression. The correlation between COL11A1 and immune checkpoint genes in human cancers was also evaluated.ResultsUsing the EPIC algorithm and Weighted Gene Co-expression Network Analysis, 13 hub genes associated with cancer-associated fibroblasts in lung adenocarcinoma were screened. Using the GEPIA database, Kaplan-Meier Plotter database, GSE72094, GSE75037, GSE32863, and our immunohistochemistry experiment data, we confirmed that COL11A1 overexpresses in lung adenocarcinoma and that high expression of COL11A1 is associated with a poor prognosis. COL11A1 has a genetic alteration frequency of 22% in patients with lung adenocarcinoma. COL11A1 is involved in the extracellular matrix activities of lung adenocarcinoma. Using the TISCH database, we found that COL11A1 is mainly expressed by cancer-associated fibroblasts in the tumor microenvironment rather than by lung adenocarcinoma cells. Finally, we found that COL11A1 is positively correlated with HAVCR2(TIM3), CD274 (PD-L1), CTLA4, and LAG3 in lung adenocarcinoma.ConclusionCOL11A1 may be expressed and secreted by cancer-associated fibroblasts, and a high expression of COL11A1 may result in T cell exhaustion in the tumor microenvironment of lung adenocarcinoma. COL11A1 may serve as an attractive biomarker to provide new insights into cancer therapeutics.

Identification of Lung Adenocarcinoma Subtypes Based on MHC-II Gene Expression Profile and Immunological Analysis

Introduction: Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported. Methods: We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis. We used R package to conduct a series of analyses on different subtypes, exploring their survival differences, gene expression differences, pathway enrichment differences, and differences in immune characteristics and immune therapy. Finally, we screened potential drugs from the cMAP database. Results: We identified two MHC-II-related LUAD subtypes. Our analyses presented that patients with cluster2 subtype showed better prognosis, higher immune scores, higher levels of immune cell infiltration and immune function activation. In addition, patients with this subtype had higher immunophenoscore, lower TIDE scores, and DEPTH scores. We also identified 10 small molecule drugs, such as lenalidomide, VX-745, and tyrphostin-AG-1295. Conclusion: Overall, MHC-II is not only a potential biomarker for accurately distinguishing LUAD subtypes but also a predictive factor for their survival. Our study offers novel insights into understanding of impact of MHC-II in LUAD and offers a new perspective for improving the accurate classification of LUAD patients and enhancing drug treatment.