Prognosis Of Lung Adenocarcinoma Research Articles

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

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

Bioinformatics and Experimental Study revealed LINC00982/ miR-183-5p/ABCA8 Axis Suppresses LUAD Progression.

Lung adenocarcinoma (LUAD) is a major health challenge worldwide with an undesirable prognosis. LINC00982 has been implicated as a tumor suppressor in diverse human cancers; however, its role in LUAD has not been fully characterized. Expression level and prognostic value of LINC00982 were investigated in pan-cancer and lung cancer from The Cancer Genome Atlas (TCGA) project. Differential expression analysis based on the LINC00982 expression level was performed in LUAD followed by gene set enrichment analysis (GSEA) and functional enrichment analyses. The association between LINC00982 expression and tumor immune microenvironment characteristics was evaluated. A potential ceRNA regulatory axis was identified and experimentally validated. We found that LINC00982 expression was downregulated and correlated with poor prognosis in LUAD. Enrichment analyses revealed that LINC00982 could inhibit DNA damage repair and cell proliferation, but enhance tumor metabolic reprogramming. We identified a competing endogenous RNA network involving LINC00982, miR-183-5p, and ATP-binding cassette subfamily A member 8 (ABCA8). Luciferase assays confirmed that miR-183-5p can interact with LINC00982 and ABCA8. Forced miR-183-5p expression reduced LINC00982 transcript levels and suppressed ABCA8 expression. Our findings revealed the LINC00982/miR-183-5p/ABCA8 axis as a potential therapeutic target in LUAD.

Identification of Cancer Stem Cell-related Gene by Single-cell and Machine Learning Predicts Immune Status, Chemotherapy Drug, and Prognosis in Lung Adenocarcinoma.

This study aimed to identify the molecular type and prognostic model of lung adenocarcinoma (LUAD) based on cancer stem cell-related genes. Studies have shown that cancer stem cells (CSC) are involved in the development, recurrence, metastasis, and drug resistance of tumors. The clinical information and RNA-seq of LUAD were obtained from the TCGA database. scRNA dataset GSE131907 and 5 GSE datasets were downloaded from the GEO database. Molecular subtypes were identified by ConsensusClusterPlus. A CSC-related prognostic signature was then constructed via univariate Cox and LASSO Cox-regression analysis. A scRNA-seq GSE131907 dataset was employed to obtain 11 cell clusters, among which, 173 differentially expressed genes in CSC were identified. Moreover, the CSC score and mRNAsi were higher in tumor samples. 18 of 173 genes were survival time-associated genes in both the TCGA-LUDA dataset and the GSE dataset. Next, two molecular subtypes (namely, CSC1 and CSC2) were identified based on 18 survival-related CSC genes with distinct immune profiles and noticeably different prognoses as well as differences in the sensitivity of chemotherapy drugs. 8 genes were used to build a prognostic model in the TCGA-LUAD dataset. High-risk patients faced worse survival than those with a low risk. The robust predictive ability of the risk score was validated by the time-dependent ROC curve revealed as well as the GSE dataset. TIDE analysis showed a higher sensitivity of patients in the low group to immunotherapy. This study has revealed the effect of CSC on the heterogeneity of LUAD, and created an 8 genes prognosis model that can be potentially valuable for predicting the prognosis of LUAD and response to immunotherapy.

Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data.

The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.

RNAi screens identify HES4 as a regulator of redox balance supporting pyrimidine synthesis and tumor growth.

NADH/NAD+ redox balance is pivotal for cellular metabolism. Systematic identification of NAD(H) redox regulators, although currently lacking, would help uncover unknown effectors critically implicated in the coordination of growth metabolism. In this study, we performed a genome-scale RNA interference (RNAi) screen to globally survey the genes involved in redox modulation and identified the HES family bHLH transcription factor HES4 as a negative regulator of NADH/NAD+ ratio. Functionally, HES4 is shown to be crucial for maintaining mitochondrial electron transport chain (ETC) activity and pyrimidine synthesis. More specifically, HES4 directly represses transcription of SLC44A2 and SDS, thereby inhibiting mitochondrial choline oxidation and cytosolic serine deamination, respectively, which, in turn, ensures coenzyme Q reduction capacity for DHODH-mediated UMP synthesis and serine-derived dTMP production. Accordingly, inhibition of choline oxidation preserves mitochondrial serine catabolism and ETC-coupled redox balance. Furthermore, HES4 protein stability is enhanced under EGFR activation, and increased HES4 levels facilitate EGFR-driven tumor growth and predict poor prognosis of lung adenocarcinoma. These findings illustrate an unidentified mechanism, underlying pyrimidine biosynthesis in the intersection between serine and choline catabolism, and underscore the physiological importance of HES4 in tumor metabolism.

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.

Clinical relevance of somatic mutations in Chinese lung adenocarcinoma and their prognostic implications for survival.

To comprehensively elucidate the genomic and mutational features of lung cancer cases, and lung adenocarcinoma (LUAD), it is imperative to conduct ongoing investigations into the genomic landscape. In this study, we aim to analyze the somatic mutation profile and assessed the significance of these informative genes utilizing a retrospective LUAD cohort. A total of 247 Chinese samples were analyzed to exhibit the tumor somatic genomic alterations in patients with LUAD. The Cox regression analysis was employed to identify prognosis-related genes and establish a predictive model for stratifying patients with LUAD. In the Dianjiang People's Hospital (DPH) cohort, the top five frequent mutated genes were (Epidermal growth factor receptor) EGFR (68%), TP53 (30%), RBM10 (13%), LRP1B (9%), and KRAS (9%). Of which, EGFR is a mostly altered driver gene, and most mutation sites are located in tyrosine kinase regions. Oncogene pathway alteration and mutation signature analysis demonstrated the RTK-RAS pathway alteration, and smoking was the main carcinogenic factor of the DPH cohort. Furthermore, we identified 34 driver genes in the DPH cohort, including EGFR (68%), TP53 (30.4%), RBM10 (12.6%), KRAS (8.5%), LRP1B (8.5%), and so on, and 45 Clinical Characteristic-Related Genes (CCRGs) were found to closely related to the clinical high-risk factors. We developed a Multiple Parameter Gene Mutation (MPGM) risk model by integrating critical genes and oncogenic pathway alterations in LUAD patients from the DPH cohort. Based on publicly available LUAD datasets, we identified five genes, including BRCA2, Anaplastic lymphoma kinase (ALK), BRAF, EGFR, and Platelet-Derived Growth Factor Receptor Alpha (PDGFRA), according to the multivariable Cox regression analysis. The MPGM-low group showed significantly better overall survival (OS) compared to the MPGM-high group (p < 0.0001, area under the curve (AUC) = 0.754). The robust performance was validated in 55 LUAD patients from the DPH cohort and another LUAD dataset. Immune characteristics analysis revealed a higher proportion of primarily DCs and mononuclear cells in the MPGM-low risk group, while the MPGM-high risk group showed lower immune cells and higher tumor cell infiltration. This study provides a comprehensive genomic landscape of Chinese LUAD patients and develops an MPGM risk model for LUAD prognosis stratification. Further follow-up will be performed for the patients in the DPH cohort consistently to explore the resistance and prognosis genetic features.

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.