Prognosis Of Lung Adenocarcinoma Research Articles

Consensus clustering and novel risk score model construction based on m6A methylation regulators to evaluate the prognosis and tumor immune microenvironment of early-stage lung adenocarcinoma.

The aim of this study was to investigate the correlation between m6A methylation regulators and cell infiltration characteristics in tumor immune microenvironment (TIME), so as to help understand the immune mechanism of early-stage lung adenocarcinoma (LUAD). The expression and consensus cluster analyses of m6A methylation regulators in early-stage LUAD were performed. The clinicopathological features, immune cell infiltration, survival and functional enrichment in different subtypes were analyzed. We also constructed a prognostic model. Clinical tissue samples were used to validate the expression of model genes through real-time polymerase chain reaction (RT-PCR). In addition, cell scratch assay and Transwell assay were also performed. Expression of m6A methylation regulators was abnormal in early-stage LUAD. According to the consensus clustering of m6A methylation regulators, patients with early-stage LUAD were divided into two subtypes. Two subtypes showed different infiltration levels of immune cell and survival time. A prognostic model consisting of HNRNPC, IGF2BP1 and IGF2BP3 could be used to predict the survival of early-stage LUAD. RT-PCR results showed that HNRNPC, IGF2BP1 and IGF2BP3 were significantly up-regulated in early-stage LUAD tissues. The results of cell scratch assay and Transwell assay showed that overexpression of HNRNPC promotes the migration and invasion of NCI-H1299 cells, while knockdown HNRNPC inhibits the migration and invasion of NCI-H1299 cells. This work reveals that m6A methylation regulators may be potential biomarkers for prognosis in patients with early-stage LUAD. Our prognostic model may be of great value in predicting the prognosis of early-stage LUAD.

TICRR as a potential prognostic biomarker for lung adenocarcinoma: A comprehensive analysis using TCGA database.

To investigate the role of TopBP1-interacting checkpoint and replication regulator (TICRR) in the tumorigenesis and prognosis of lung adenocarcinoma (LUAD) patients. Wilcoxon signed-rank test and logistic regression were utilized to analyze the relationship between clinical characteristics and TICRR expression in LUAD from TCGA dataset. Kaplan-Meier plots and Cox regressions were used to assess the impact of TICRR impact on prognosis. ROC curves and nomograms were generated to further evaluate the relationship between TICRR expression and the risk of LUAD. Gene set enrichment analysis (GSEA) was conducted on TCGA dataset, and ssGSEA was employed to investigate the association between TICRR and immune infiltrates. The results showed that high TICRR expression was significantly associated with various clinical factors including gender, age, pathological stage, T stage, N stage, M stage, outcome of primary therapy and smoking status. ROC curves also demonstrated that TICRR was a promising biomarker for molecular pathology diagnosis in LUAD patients (AUC = 0.952). Further analysis using gene ontology (GO) term enrichment and GSEA revealed an abnormal correlation between TICRR expression and cell division. Interestingly, ssGSEA analysis showed that TICRR expression correlated with multiple immune cell types, such as Th2 cell, TFH cell, mast cell, iDC, eosinophils, and dendritic cell. Lastly, the KM-plotters indicated that LUAD patients with high TICRR expression obtained worse life expectancy (P < .001). TICRR has proven to be a valuable tool in predicting disease progression and prognosis in patients with LUAD, thereby establishing itself as a fitting biomarker for forecasting overall survival (OS) of LUAD patients.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis.

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

Prognostic signature based on S100 calcium-binding protein family members for lung adenocarcinoma and its clinical significance

The S100 family proteins (S100s) participate in multiple stages of tumorigenesis and are considered to have potential value as biomarkers for detecting and predicting various cancers. But the role of S100s in lung adenocarcinoma (LUAD) prognosis is elusive. Transcriptional data of LUAD patients were retrieved from TCGA, and relevant literature was extensively reviewed to collect S100 genes. Differential gene expression analysis was performed on the LUAD data, followed by intersection analysis between the differentially expressed genes (DEGs) and S100 genes. Unsupervised consensus clustering analysis identified two clusters. Significant variations in overall survival between the two clusters were shown by Kaplan-Meier analysis. DEGs between the two clusters were analyzed using Lasso regression and univariate/multivariate Cox regression analysis, leading to construction of an 11-gene prognostic signature. The signature exhibited stable and accurate predictive capability in TCGA and GEO datasets. Subsequently, we observed distinct immune cell infiltration, immunotherapy response, and tumor mutation characteristics in high and low-risk groups. Finally, small molecular compounds targeting prognostic genes were screened using CellMiner database, and molecular docking confirmed the binding of AMG-176, Estramustine, and TAK-632 with prognostic genes. In conclusion, we generated a prognostic signature with robust and reliable predictive ability, which may provide guidance for prognosis and treatment of LUAD.

Association of R-loop binding proteins with prognosis and anti-tumor drug sensitivity in lung adenocarcinoma: a bioinfor-matic study.

To investigate the association of R-loop binding proteins with prognosis and chemotherapy efficacy in lung adenocarcinoma. The data related to R-loop regulatory genes were obtained from literature of R-loop proteomics and relevant databases. We used 403 cases of lung adenocarcinoma in the Cancer Genome Atlas as training set, and two datasets GSE14814 and GSE31210 in Gene Expression Omnibus as validation sets. The weighted gene co-expression network analysis (WGCNA) was employed to identify R-loop genes with a significant impact on the clinical phenotype of lung adenocarcinoma. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized to eliminate genes exhibiting multicollinearity. A multivariate Cox regression analysis was employed to scrutinize clinical variables and R-loop characteristic genes that exert independent prognostic effects on patient survival. Subsequently, a risk score model was constructed. The predictive capacity of this model for the prognosis of patients was analyzed and validated. Additionally, the performance of risk model on the anti-tumor drug sensitivity was assessed. The mutations of R-loop genes were analyzed by maftools. The effect of PLEC expression on anti-tumor drug sensitivity was tested on non-small cell lung adenocarcinoma H1299 and A549 cells in vitro. A collection of 1551 R-loop genes were obtained, and 78 genes exhibited significant effects on the clinical phenotype shown on WGCNA. The LASSO regression analysis retained fourteen R-loop genes. A multivariate Cox regression analysis further identified three R-loop genes (HEXIM1, GLI2, PLEC) and a clinical variable (tumor grading) that were associated with patient prognosis. Risk prediction model was established according to the regression coefficients of each parameter. Kaplan-Meier survival analysis showed that the prognosis of high-risk group was significantly worse than that of low-risk group (P<0.01). The time-dependent ROC curve showed that the risk model had good predictive ability in both training and validation sets. Predictive analyses of anti-neoplastic drug sensitivity indicated a diminished responsiveness to both chemotherapy and targeted treatment drugs among high-risk patients. The expression of PLEC was strongly correlated with sensitivity to gefitinib, a classical EGFR inhibitor. R-loop binding proteins have been identified as significant determinants in the prognosis and therapeutic strategies for lung adenocarcinoma, which indicates that therapeutic interventions targeting these specific R-loop binding proteins might contribute to a better survival of the patients.

WFDC2 is a potential prognostic and immunotherapy biomarker in lung adenocarcinoma.

The prognosis of lung adenocarcinoma (LUAD), which is the most common type of lung cancer, remains poor. Little is known about the function and mechanism of whey acidic protein four-disulfide core domain 2 (WFDC2) in LUAD. In this study, we used online databases to compare WFDC2 expression between LUAD and normal tissues, to analyze the relationship between WFDC2 and overall survival, and to investigate the potential roles of WFDC2. We found that WFDC2 protein and mRNA expression levels were significantly higher in LUAD tissue than in normal tissue, and high WFDC2 mRNA expression was associated with better overall survival. WFDC2 mRNA expression was correlated with the mutation status of TP53. The biological function of WFDC2 was associated with the cell cycle, and low WFDC2 mRNA expression was associated with an elevated tumor mutational burden and neoantigen levels. A negative relationship was observed between WFDC2 and immune gene expression, and high WFDC2 mRNA expression was found in patients with LUAD and low programed cell death 1 mRNA expression. We propose that WFDC2 is associated with clinical benefits of immune checkpoint inhibitors in LUAD.

Single cell transcriptomic analysis reveals tumor immune infiltration by NK cells gene signature in lung adenocarcinoma

BackgroundNatural Killer (NK) cells are vital components of the innate immune system, crucial for combating infections and tumor growth, making them pivotal in cancer prognosis and immunotherapy. We sought to understand the diverse characteristics of NK cells within lung adenocarcinoma (LUAD) by conducting single-cell RNA sequencing analyses. MethodsUsing the scRNA-seq dataset for multiple primary lung cancers (MPLCs), we examined two major NK cell groups, NK1 and NK2, comparing the expression profiles of 422 differentially expressed NK signature genes. We identified eight genes (SPON2, PLEKHG3, CAMK2N1, RAB27B, CTBP2, EFHD2, GOLM1, and PLOD1) that distinguish NK1 from NK2 cells. A prognostic signature, the NK gene signature (NKGS) score, was established through LASSO Cox regression. High NKGS scores were linked to poorer overall survival in TCGA-LUAD patients and consistently validated in other datasets (GSE31210 and GSE14814). ResultsFunctional analysis revealed an enrichment of genes related to the TGF-β signaling pathway in the high NKGS score group. Moreover, a high NKGS score correlated with an immunosuppressive tumor microenvironment (TME) driven by immune evasion mechanisms. We also observed reduced T-cell receptor (TCR) repertoire diversity in the high-risk NKGS group, indicating a negative association between inflammation and risk score. ConclusionThis study introduced the innovative NKGS score, differentiating NK1 from NK2 cells. High NKGS scores were associated with the TGF-β pathway and provided insights into LUAD prognosis and immune activities.

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intelligence (AI)-driven stemness-related gene signature (SRS) that deciphered LUAD prognosis and immunotherapy response. CytoTRACE analysis of single-cell RNA sequencing data identified genes associated with stemness in LUAD epithelial cells. An AI network integrating traditional regression, machine learning, and deep learning algorithms constructed the SRS based on genes associated with stemness. Subsequently, we conducted a comprehensive exploration of the connection between SRS and both intrinsic and extrinsic immune environments using multi-omics data. Experimental validation through siRNA knockdown in LUAD cell lines, followed by assessments of proliferation, migration, and invasion, confirmed the functional role of CKS1B, a top SRS gene. The SRS demonstrated high precision in predicting LUAD prognosis and likelihood of benefiting from immunotherapy. High-risk groups classified by the SRS exhibited decreased immunogenicity and reduced immune cell infiltration, indicating challenges for immunotherapy. Conversely, invitro experiments revealed CKS1B knockdown significantly impaired aggressive cancer phenotypes like proliferation, migration, and invasion of LUAD cells, highlighting its pivotal role. These results underscore a close association between stemness and tumour immunity, offering predictive insights into the immune landscape and immunotherapy responses in LUAD. The newly established SRS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

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.

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

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. 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. 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. The evidence revealed a correlation between KLRB1 and both prognosis and immune infiltration in LUAD patients.

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

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. 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. 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. 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.

Exploring cellular diversity in lung adenocarcinoma epithelium: Advancing prognostic methods and immunotherapeutic strategies

AbstractImmunotherapy has brought significant advancements in the treatment of lung adenocarcinoma (LUAD), but identifying suitable candidates remains challenging. In this study, we investigated tumour cell heterogeneity using extensive single‐cell data and explored the impact of different tumour cell cluster abundances on immunotherapy in the POPLAR and OAK immunotherapy cohorts. Notably, we found a significant correlation between CKS1B+ tumour cell abundance and treatment response, as well as stemness potential. Leveraging marker genes from the CKS1B+ tumour cell cluster, we employed machine learning algorithms to establish a prognostic and immunotherapeutic signature (PIS) for LUAD. In multiple cohorts, PIS outperformed 144 previously published signatures in predicting LUAD prognosis. Importantly, PIS reliably predicted genomic alterations, chemotherapy sensitivity and immunotherapy responses. Immunohistochemistry validated lower expression of immune markers in the low‐PIS group, while in vitro experiments underscored the role of the key gene PSMB7 in LUAD progression. In conclusion, PIS represents a novel biomarker facilitating the selection of suitable LUAD patients for immunotherapy, ultimately improving prognosis and guiding clinical decisions.

Therapeutic and prognostic effect of disulfidptosis-related genes in lung adenocarcinoma

Disulfidptosis, a new form of cell death, may be induced by disulfide stress associated with cystine disulfide buildup, which can promote cell toxicity, leading to cell death. Nevertheless, the role of direct prognosis and the mechanism underlying the regulation of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD) are still unknown. This study aimed to investigate the role of DRGs in LUAD prognosis and diagnosis through multiomics analysis. First, copy number variations (CNVs) and mutations in the 10 genes were assessed. Considering that five differentially expressed genes (DEGs) were associated with disulfidptosis, a novel DRG score that can be utilized to anticipate LUAD prognosis was developed. Next, the generated receiver operating characteristic (ROC) and survival curves demonstrated that the model had an excellent predictive quality in LUAD in both the training and validation cohorts. Meanwhile, substantial functional disparities between the high DRG group and the low DRG group were observed, and the second gap mitosis (G2M) checkpoint, E2 promoter-binding factor (E2F) targets, and myelocytomatosis (MYC) target activities were consistently higher in the high DRG group than in the low DRG group. Additionally, the T-cell dysfunction score and tumor inflammation signature (Merck18) were negatively correlated with DRGs, whereas myeloid-derived suppressor cells (MDSCs) were positively correlated with DRGs. Moreover, DRGs were negatively linked to most of the immunological checkpoints. Meanwhile, samples of low DRGs benefited more from immune checkpoint blockade (ICB). The correlation analysis between DRGs and clinical characteristics revealed increasing malignancy with increasing DRG scores. Drug sensitization experiment results indicated that sensitivity to cisplatin, vincristine, docetaxel, and gemcitabine was higher in the high DRG group than in the low DRG group. The function of model genes in LUAD was also verified using immunohistochemistry, quantitative reverse transcription-polymerase chain reaction (qRT-PCR), western blotting, 5-ethynyl-2′-deoxyuridine (EDU), and clonogenic formation.

FKBP3, a poor prognostic indicator, promotes the progression of LUAD via regulating ferroptosis and immune infiltration.

Ferroptosis was reported to possess the therapeutic potentials in various human cancers. In the present study, we explored the expression, clinical significance and the molecular mechanism of FK506 binding protein 3 (FKBP3) in the progression of lung adenocarcinoma (LUAD). Cox regression was performed to obtain the prognosis related to differentially expressed genes (DEGs) in LUAD datasets from TCGA. We also downloaded the ferroptosis-related gene datasets from GeneCards. Venn diagram was performed to find the intersecting genes and FKBP3 was selected as the targeted gene by analyzing the diagnostic and prognostic values of Top10 intersecting genes. Moreover, univariate and multivariate analyses were performed to evaluate the association between clinicopathological factors and survival rates. GO/KEGG and GSEA analysis was performed to explore the function of FKBP3 in LUAD progression. Protein-protein interaction (PPI) network was performed via STRING database and the top10 hub genes were selected. Finally, the relationship between FKBP3 and immune infiltration was explored by ssGSEA analysis. Firstly, 184 genes associated with the prognosis of LUAD and ferroptosis were obtained. FKBP3 was found to be significantly associated with a poor overall survival rate of LUAD patients. Immunohistochemical staining results showed that FKBP3 was highly located in cytoplasm and membrane of cells in LUAD tissues. PPI network analysis results showed that HDAC1, YY1, HDAC2, MTOR, PSMA3, PIN1, NCL, C14orf166, PIN4, and LARP6 were the top10 hub genes. Furthermore, spearman analysis results showed that the expression of FKBP3 was positively correlated with the abundance of Th2 cells and T helper cells. High level of FKBP3 was associated with poor prognostic outcomes of LUAD patients, which also inhibited immune infiltration in LUAD tissues. Additionally, FKBP3 was involved in regulating the ferroptosis process in LUAD patients. Thus, FKBP3 possessed the tumor promotion role might be involving in regulating ferroptosis and immune infiltration in LUAD progression.

IL-34 and its receptors as predictors of brain metastasis and prognosis in lung adenocarcinoma: Unveiling insights through bioinformatic and immunohistochemical investigations

BackgroundBrain metastasis (BM) is a prevalent form of metastasis in lung adenocarcinoma (LUAD), necessitating investigations into the underlying mechanisms. Interleukin 34 (IL-34) and its receptors, macrophage colony-stimulating factor-1 receptor (CSF-IR), Syndecan-1 (SDC-1), and protein-tyrosine phosphatase zeta receptor (PTPRZ1), are known to play pivotal roles in the metastasis of malignant tumors, thereby holding promise as potential biomarkers for studying BM in LUAD. MethodsWe performed immunohistochemistry to analyze the expression of IL-34, CSF-1R, SDC-1, and PTPRZ1 in 10 pairs of LUAD primary tissues and BMs, along with 96 unpaired primary tissues and 68 unpaired BMs. Subsequently, we evaluated the association between protein expression and the occurrence of BM. Furthermore, Kaplan-Meier survival curve analysis was conducted on both network and clinical data to explore the association between protein expression and patient prognosis and survival. ResultsAt the protein level, the expression of IL-34 and its receptors showed significant variation between paired primary tumors and BMs in 10 LUAD patients. The levels of IL-34, CSF-1R, and SDC-1 expression are typically elevated in brain metastatic lesions of LUAD compared to primary LUAD tumors. Furthermore, patients with high CSF-1R expression in primary LUAD are at a greater risk of developing brain metastases. High expression of IL-34 and CSF-1R in primary LUAD lesions indicated poor disease-free survival (DFS) and overall survival (OS), while high expression of SDC-1 indicated poor OS. Cox multivariate analysis further revealed that CSF-1R and IL-34+CSF-1R positivity independently affected LUAD OS. These findings were further substantiated in unpaired samples. ConclusionsOur results indicate significant alterations in the expression of IL-34 and its receptors, CSF-1R and SDC-1, between LUAD primary lesions and BMs, with increased expression observed in BMs. LUAD patients with positive CSF-1R expression in primary lesions exhibited a higher likelihood of developing BM, and high expression of IL-34, CSF-1R, and SDC-1 correlated with poor prognosis. These findings contribute novel insights towards identifying potential treatment or diagnostic targets for metastatic LUAD.

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combinationof10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results,through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells.The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.

Exploring the potential biological significance of KDELR family genes in lung adenocarcinoma

The Lys-Asp-Glu-Leu receptor (KDELR) family genes play critical roles in a variety of biological processes in different tumors. Our study aimed to provide a comprehensive analysis of the potential roles of KDELRs in lung adenocarcinoma (LUAD). Utilizing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, as well as clinical samples, we conducted a series of analyses and validations using R software tools and various online resources. The results showed that KDELR family genes and proteins were highly expressed and associated with a poor prognosis of LUAD. Promoter hypomethylation and the competing endogenous RNA (ceRNA) network of PCAT6/hsa-miR-326/KDELR1 might be potential causes of aberrant KDELR1 overexpression in LUAD. Three key Transcription factors (TFs) (SPI1, EP300, and MAZ) and a TFs-miRNAs-KDELRs network (involving 11 TFs) might be involved in modulating KDELRs expression abnormalities. Gene Set Enrichment Analysis (GSEA) indicated enrichment of genes highly expressing KDELR1, KDELR2, and KDELR3 in MTORC1_SIGNALING, P53_PATHWAY, and ANGIOGENESIS. Negative correlations between KDELRs expression and CD8 + T cell infiltration, as well as CTLA-4 expression. Our multiple analyses suggested that the KDELRs are important signaling molecules in LUAD. These results provided novel insights for developing prognostic markers and novel therapies of LUAD.

A genomic instability-associated lncRNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

Genomic instability (GI) was associated with tumorigenesis. However, GI-related lncRNA signature (GILncSig) in lung adenocarcinoma (LUAD) is still unknown. In this study, the lncRNA expression data, somatic mutation information and clinical survival information of LUAD were downloaded from The Cancer Genome Atlas (TCGA) and performed differential analysis. Functional and prognosis analysis revealed that multiple GI-related pathways were enriched. By using univariate and multivariate Cox regression analysis, 5 GI-associated lncRNAs (AC012085.2, FAM83A-AS1, MIR223HG, MIR193BHG, LINC01116) were identified and used to construct a GILncSig model. Mutation burden analysis indicated that the high-risk GI group had much higher somatic mutation count and the risk score constructed by the 5 GI-associated lncRNAs was an independent predictor for overall survival (OS) (P < 0.05). Overall, our study provides valuable insights into the involvement of GI-associated lncRNAs in LUAD and highlights their potential as therapeutic targets.

Dendritic Cell-Related Immune Marker CD1C for Predicting Prognosis and Immunotherapy Opportunities of Lung Adenocarcinoma Patients.

Lung adenocarcinoma (LUAD) is the most frequent type of lung cancer with a high mortality rate. Here, we aim to explore novel immune-related biomarkers for LUAD patients. Datasets, mRNA expression profiles, and clinical data concerned with LUAD were obtained from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA), respectively. Differential expression analysis was performed to obtain differentially expressed genes (DEGs). Based on DEGs, we conducted functional enrichment analyses. Subsequently, Kaplan‑Meier (KM) was performed to analyze survival differences among different groups. Furthermore, immune cell infiltration proportion was calculated by CIBERSORT and TIMER. The relationship between gene and immune response was analyzed using Tumor Immune System Interactions (TISIDB) database. Finally, Pearson correlation analysis was performed between CD1C and six immune checkpoints. We identified dendritic cells (DCs)-related expression profiles from four LUAD samples. DCs' immune marker CD1C in LUAD was selected by univariate Cox regression analysis. Low CD1C expression patients had a poor prognosis. A total of 332 DEGs were identified in high and low CD1C expression groups, which primarily enriched in 348 GO terms and 30 KEGG pathways. There were significant differences in the infiltration proportion of 17 immune cells between high and low CD1C expression groups. Most immunomodulators, chemokines, and chemokine receptors were positively associated with CD1C expression. Six immune checkpoints were also positively correlated with CD1C expression. DCs related immunomarker CD1C probably plays a pivotal part in prognosis and immunotherapy of LUAD via a joint analysis of single-cell and bulk sequencing data.

The predictive value of E2F7 in immunotherapy efficacy for lung adenocarcinoma: An observational study.

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. In recent years, immunotherapy has greatly changed the treatment pattern of advanced LUAD. However, only a small proportion of LUAD patients benefitted from immune checkpoint inhibitor therapy. There is an urgent need to develop a biomarker to predict immune therapy response. E2F7 has been shown to be closely related to immune cell infiltration and immune checkpoint expression in tumors. However, it is unclear whether the E2F7 expression is related to the immunotherapy efficacy in LUAD. Therefore, we conducted this study to investigate the clinical characteristics, function, and immunotherapy responsiveness of E2F7 expression, and to explore the potential of E2F7 as an immunotherapy response biomarker in LUAD. We analyzed the clinical characteristics and biological function of E2F7 expression based on data from the Cancer Genome Atlas and Gene Expression Omnibus database. In addition, we used single-cell sequencing data to analyze the immune regulatory effects of E2F7 in LUAD. Furthermore, we analyzed the immunotherapy response prediction ability of E2F7 expression based on the immunotherapy database. Compared to normal lung tissue, E2F7 was specifically overexpressed in LUAD, and its expression was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. E2F7 was enriched in cell division and cell cycle functions. In addition, the expressions of immune checkpoints were correlated with the E2F7 expression. E2F7 was highly expressed in myeloid cells, and E2F7 highly expressed myeloid cells were associated with immune and inflammatory responses. Moreover, the expression level of E2F7 can effectively distinguish different immune therapy responses in LUAD patients. E2F7 was upregulated in LUAD, and high expression of E2F7 was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. Moreover, E2F7 may exert its immunosuppressive effect by affecting the function of myeloid cells. These results indicated the potential role of E2F7 as a biomarker for predicting LUAD immunotherapy responses.