Prognostic Signature For Lung Adenocarcinoma Research Articles

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

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

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals immune suppression subtypes and establishes a novel signature for determining the prognosis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with lower survival rates. Recent advancements in targeted therapies and immunotherapies targeting immune checkpoints have achieved remarkable success, there is still a large percentage of LUAD that lacks available therapeutic options. Due to tumor heterogeneity, the diagnosis and treatment of LUAD are challenging. Exploring the biology of LUAD and identifying new biomarker and therapeutic targets options are essential. We performed single-cell RNA sequencing (scRNA-seq) of 6 paired primary and adjacent LUAD tissues, and integrative omics analysis of the scRNA-seq, bulk RNA-seq and whole-exome sequencing data revealed molecular subtype characteristics. Our experimental results confirm that CDC25C gene can serve as a potential marker for poor prognosis in LUAD. We investigated aberrant gene expression in diverse cell types in LUAD via the scRNA-seq data. Moreover, multi-omics clustering revealed four subgroups defined by transcriptional profile and molecular subtype 4 (MS4) with poor survival probability, and immune cell infiltration signatures revealed that MS4 tended to be the immunosuppressive subtype. Our study revealed that the CDC25C gene can be a distinct prognostic biomarker that indicates immune infiltration levels and response to immunotherapy in LUAD patients. Our experimental results concluded that CDC25C expression affects lung cancer cell invasion and migration, might play a key role in regulating Epithelial-Mesenchymal Transition (EMT) pathways. Our multi-omics result revealed a comprehensive set of molecular attributes associated with prognosis-related genes in LUAD at the cellular and tissue level. Identification of a subtype of immunosuppressive TME and prognostic signature for LUAD. We identified the cell cycle regulation gene CDC25C affects lung cancer cell invasion and migration, which can be used as a potential biomarker for LUAD.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma.

Disulfidoptosis is an unconventional form of programmed cell death that distinguishes itself from well-established cell death pathways like ferroptosis, pyroptosis, and necroptosis. Initially, we conducted a single-cell analysis of the GSE131907 dataset from the GEO database to identify disulfidoptosis-related genes (DRGs). We utilized differentially expressed DRGs to classify TCGA samples with an unsupervised clustering algorithm. Prognostic models were built using Cox regression and LASSO regression. Two DRG-related clusters (C1 and C2) were identified based on the DEGs from single-cell sequencing data analysis. In comparison to C1, C2 exhibited significantly worse overall prognosis, along with lower expression levels of immune checkpoint genes (ICGs) and chemoradiotherapy sensitivity-related genes (CRSGs). Furthermore, C2 displayed a notable enrichment in metabolic pathways and cell cycle-associated mechanisms. C2 was also linked to the development and spread of tumors. We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs. Patients were categorized into high-risk and low-risk groups depending on their DRG score, with the former group being linked to a poorer prognosis and higher TMB score. Moreover, the DRG score displayed significant correlations with CRSGs, ICGs, the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapeutic sensitivity. Subsequently, we identified a significant correlation between the DRG score and monocyte macrophages. Additionally, crucial DRGs were additionally validated using qRT-PCR. Our new DRG score can predict the immune landscape and prognosis of LUAD, serving as a reference for immunotherapy and chemotherapy.

Novel prognostic signature for lung adenocarcinoma based on immune-related mRNA pairs

Lung adenocarcinoma (LUAD) is a highly lethal malignant tumor. While the involvement of multiple mRNAs in the progression of LUAD is well established, the potential diagnostic value of immune-related mRNAs (irmRNAs) in LUAD remains largely unexplored. In this study, we utilized RNA-seq, clinical data, and immune-related gene information from LUAD patients to identify differentially expressed immune-related mRNAs (DEirmRNAs) and developed a predictive risk model based on specific DEirmRNA pairs closely linked with patient prognosis. We classified patients into high-risk and low-risk groups and analyzed factors such as survival rate, clinical characteristics, gene enrichment, immune cell infiltration, tumor mutation load, and drug susceptibility. We confirmed the expression levels of these DEirmRNAs in tumor tissues using qRT-PCR assay. Our results showed that the low-risk group had a longer survival time and lower tumor mutation burden (TMB) and microsatellite instability (MSI) compared to the high-risk group. The high-risk group also had a significant reduction in the number of certain immune cells and a lower half-maximum inhibitor concentration (IC50). We identified specific DEirmRNA pairs that were up-regulated or down-regulated in tumor tissues compared to adjacent tissues. Our prognostic risk model based on DEirmRNA pairs could be used to predict the prognosis of LUAD patients and provide reference for better treatment.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed T cell marker genes based molecular sub-types and a prognostic signature in lung adenocarcinoma

Immunotherapy has emerged as a promising modality for addressing advanced or conventionally drug-resistant malignancies. When it comes to lung adenocarcinoma (LUAD), T cells have demonstrated significant influence on both antitumor activity and the tumor microenvironment. However, their specific contributions remain largely unexplored. This investigation aimed to delineate molecular subtypes and prognostic indicators founded on T cell marker genes, thereby shedding light on the significance of T cells in LUAD prognosis and precision treatment. The cellular phenotypes were identified by scrutinizing the single-cell data obtained from the GEO repository. Subsequently, T cell marker genes derived from single-cell sequencing analyses were integrated with differentially expressed genes from the TCGA repository to pinpoint T cell-associated genes. Utilizing Cox analysis, molecular subtypes and prognostic signatures were established and subsequently verified using the GEO dataset. The ensuing molecular and immunological distinctions, along with therapy sensitivity between the two sub-cohorts, were examined via the ESTIMATE, CIBERSORT, and ssGSEA methodologies. Compartmentalization, somatic mutation, nomogram development, chemotherapy sensitivity prediction, and potential drug prediction analyses were also conducted according to the risk signature. Additionally, real-time qPCR and the HPA database corroborated the mRNA and protein expression patterns of signature genes in LUAD tissues. In summary, this research yielded an innovative T cell marker gene-based signature with remarkable potential to prognosis and anticipate immunotherapeutic outcomes in LUAD patients.

Retracted: Identifying a 6-Gene Prognostic Signature for Lung Adenocarcinoma Based on Copy Number Variation and Gene Expression Data.

[This retracts the article DOI: 10.1155/2022/6962163.].

Identification and validation of a disulfidptosis-related genes prognostic signature in lung adenocarcinoma

Disulfidptosis, a newly revealed form of cell death, regulated by numerous genes that has been recently identified. The exact role of disulfidptosis in lung adenocarcinoma (LUAD) still uncertain. Objective of this study was to explore potential prognostic markers among disulfidptosis genes in LUAD. By combining transcriptomic information from Gene Expression Omnibus databases and The Cancer Genome Atlas, we identified differentially expressed and prognostic disulfidptosis genes. By conducting least absolute shrinkage and selection operator with multivariate Cox regression, four disulfidptosis genes were selected to create the prognostic signature. The implementation of the signature separated the training and validation cohorts into groups with high- and low-risk. Subsequently, the model was verified by conducting an independent analysis of receiver operating characteristic (ROC) curves. Further comparisons were made between the two risk-divided groups with regards the tumor microenvironment, immune cell infiltration, immunotherapy response, and drug sensitivity. The signature was constructed using four disulfidptosis-related genes: SLC7A11, SLC3A2, NCKAP1, and GYS1. According to ROC curves, the signature was effective for predicting LUAD prognosis. In addition, the prognostic signature correlated with sensitivity to chemotherapeutic agents and the efficacy of immunotherapy in LUAD. Finally, through external validation, we showed that NCKAP1 are correlated with tumor migration, proliferation, and invasion of LUAD cells. GYS1 affects immune cell, especially M2 macrophage infiltration in the tumor microenvironment. The disulfidptosis four-gene model can reliably predict the prognosis of patients diagnosed with LUAD, thereby providing valuable information for clinical applications and immunotherapy.

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer. Finding prognostic biomarkers is helpful in stratifying LUAD patients with different prognosis. We explored the correlation of LUAD prognosis and genes associated with chemotherapy in LUAD and obtained data of LUAD patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Drug sensitivity data were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Differential and enrichment analyses were used to screen the target genes utilizing limma and "clusterProfiler" packages. Then univariate and LASSO Cox analyses were used to select the prognosis-related genes. Survival analysis was used to estimate the overall survival (OS) of different groups. Twenty-three differentially expressed genes (DEGs) were screened between LUAD samples and healthy samples, and BTK, FGFR2, PIM2, CHEK1, and CDK1 were selected to construct a prognostic signature. The OS of patients in the high-risk group (risk score higher than 0.69) was worse than that in the low-risk group (risk score lower than 0.69). The risk score model constructed by five genes is a potential prognostic biomarker for LUAD patients.

Construction of an EMT-related lncRNA prognostic signature for lung adenocarcinoma and functional verification of its hub gene LINC01615.

The epithelial-mesenchymal transition (EMT) plays a vital role in the progression of lung adenocarcinoma (LUAD). Long non-coding RNAs (lncRNAs) participate in the EMT process as an important regulatory factor and have the potential to serve as prognostic biomarkers. We aimed to construct a novel lncRNA prognostic signature for LUAD based on EMT-related lncRNAs, identify EMT-related hub lncRNA, and investigate its biological functions. RNA-seq data, clinical and survival information were obtained from The Cancer Genome Atlas database. The EMT-related lncRNA prognostic signature (EMTscore) was constructed using the Least Absolute Shrinkage and Selection Operator Cox regression analysis. The efficiency of EMTscore in predicting the prognosis of LUAD was evaluated through the area under the time-dependent receiver operating characteristic (ROC) curves. The hub lncRNA of the prognostic signature was selected using a co-expression network map, and its effects on cell proliferation and metastasis were explored by in vitro experiments. We constructed a prognostic signature (EMTscore) containing 8 tumor-high expressed lncRNAs. The EMTscore performed well in predicting overall survival rates with AUC values of 0.708 at 5years in the training set. EMTscore could independently predict the survival of LUAD, with HR = 4.011 (95% CI 2.430-6.622) in the multivariate Cox regression. Importantly, we identified LINC01615 as the hub lncRNA in the EMTscore and revealed that LINC01615 enhanced the proliferation, migration, and EMT of lung cancer cells. A new EMT-related lncRNA prognostic signature named EMTscore was developed, and LINC01615 was identified as the hub lncRNA of EMTscore. The hub lncRNA LINC01615 had an oncogenic biological function in LUAD.

Immune characteristics analysis and construction of a four-gene prognostic signature for lung adenocarcinoma based on estrogen reactivity

Lung adenocarcinoma (LUAD) is a common type of malignant tumor with poor prognosis and high mortality. In our previous studies, we found that estrogen is an important risk factor for LUAD, and different estrogen statuses can predict different prognoses. Therefore, in this study, we constructed a prognostic signature related to estrogen reactivity to determine the relationship between different estrogen reactivities and prognosis. We downloaded the LUAD dataset from The Cancer Genome Atlas (TCGA) database, calculated the estrogen reactivity of each sample, and divided them into a high-estrogen reactivity group and a low-estrogen reactivity group. The difference in overall survival between the groups was significant. We also analyzed the status of immune cell infiltration and immune checkpoint expression between the groups. We analyzed the differential gene expression between the groups and screened four key prognostic factors by the least absolute shrinkage and selection operator (LASSO) regression and univariable and multivariable Cox regression. Based on the four genes, a risk signature was established. To a certain extent, the receiver operating characteristic (ROC) curve showed the predictive ability of the risk signature, which was further verified using the GSE31210 dataset. We also determined the role of estrogen in LUAD using an orthotopic mouse model. Additionally, we developed a predictive nomogram combining the risk signature with other clinical characteristics. In conclusion, our four-gene prognostic signature based on estrogen reactivity had prognostic value and can provide new insights into the development of treatment strategies for LUAD.

Genome instability-derived genes as a novel prognostic signature for lung adenocarcinoma.

Background: An increasing number of patients are being diagnosed with lung adenocarcinoma, but there remains limited progress in enhancing prognostic outcomes and improving survival rates for these patients. Genome instability is considered a contributing factor, as it enables other hallmarks of cancer to acquire functional capabilities, thus allowing cancer cells to survive, proliferate, and disseminate. Despite the importance of genome instability in cancer development, few studies have explored the prognostic signature associated with genome instability for lung adenocarcinoma. Methods: In the study, we randomly divided 397 lung adenocarcinoma patients from The Cancer Genome Atlas database into a training group (n = 199) and a testing group (n = 198). By calculating the cumulative counts of genomic alterations for each patient in the training group, we distinguished the top 25% and bottom 25% of patients. We then compared their gene expressions to identify genome instability-related genes. Next, we used univariate and multivariate Cox regression analyses to identify the prognostic signature. We also performed the Kaplan-Meier survival analysis and the log-rank test to evaluate the performance of the identified prognostic signature. The performance of the signature was further validated in the testing group, in The Cancer Genome Atlas dataset, and in external datasets. We also conducted a time-dependent receiver operating characteristic analysis to compare our signature with established prognostic signatures to demonstrate its potential clinical value. Results: We identified GULPsig, which includes IGF2BP1, IGF2BP3, SMC1B, CLDN6, and LY6K, as a prognostic signature for lung adenocarcinoma patients from 42 genome instability-related genes. Based on the risk score of the risk model with GULPsig, we successfully stratified the patients into high- and low-risk groups according to the results of the Kaplan-Meier survival analysis and the log-rank test. We further validated the performance of GULPsig as an independent prognostic signature and observed that it outperformed established prognostic signatures. Conclusion: We provided new insights to explore the clinical application of genome instability and identified GULPsig as a potential prognostic signature for lung adenocarcinoma patients.

Construction and validation of an efferocytosis-related prognostic signature in lung adenocarcinoma.

Accumulating evidence highlights the potential significance of efferocytosis in tumor progression. This study is directed towards the construction of a prognostic risk model for lung adenocarcinoma (LUAD), grounded on efferocytosis-related genes (ERGs). Employing LASSO-COX regression analysis, a risk-prognostic model was formulated, centered on seven ERGs. Concurrently, a nomogram was established that incorporated patient clinical features and risk scores. The predictive accuracy of the risk model and the nomogram was substantiated via external validation sets. The landscapes of immune infiltration and genetic mutation were evaluated for high- and low-risk groups, with the expression of seven key genes validated through RT-PCR. Our findings reveal that the high-risk group displayed considerably inferior survival outcomes in comparison to the low-risk group. A diminished abundance of immune cell infiltrates and a higher prevalence of gene mutations characterized the high-risk group. Genes with high expression were markedly enriched in pathways related to cell proliferation. The superior predictive performance of the risk model and nomogram was adequately substantiated by the external validation sets (GSE31210, GSE30219, and GSE50081). In addition, we discerned several potential therapeutic drugs demonstrating different sensitivities across patient risk groups. The differential expression of seven central genes was confirmed in A549, H1299, and BEAS-2B cell lines. The constructed risk model and nomogram display high accuracy in predicting the survival and immune landscape of LUAD patients, thus providing invaluable prognostic tools in clinical scenarios.

Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma.

Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.

Construction and validation of a novel ferroptosis-related prognostic signature for lung adenocarcinoma.

Lung cancer has the highest prevalence and mortality of all cancers, and lung adenocarcinoma (LUAD) occupies the largest proportion of lung cancers. Herein, this study is aimed at constructing a ferroptosis-related prognostic signature for LUAD and conducting functional analysis based on the signature, highlighting the importance of ferroptosis in LUAD. We employed RNA-sequencing (RNA-seq) and clinical data from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were conducted to build the ferroptosis-related genes (FRGs) prognostic signature. The efficacy of this FRG signature was further analyzed with Kaplan-Meier (KM) plot, multivariate Cox regression, and the receiver operating characteristic (ROC) curves. Enrichment analysis was used to evaluate key pathways. The expression of immunomodulators, immune infiltration status, and drug sensitivity correlation were explored to predict the response to various therapies. The expression of FRGs was validated in LUAD samples with western blot (WB) and immunohistochemistry (IHC) staining. Cell viability assay and lipid peroxidation detection were measured after small interfering RNA (siRNA) knockdown of two FRGs in lung cancer cell lines. A seven-gene signature was constructed and used to divide LUAD patients into high- and low-risk groups. High-risk patients were notably related to shorter overall survival (OS), and multivariate Cox regression demonstrated that our signature was an independent predictor of OS. ROC curve analysis presented a maximum area under the curve (AUC) value of 0.740 for the experimental cohort and 0.705 for the validation cohort. The low-risk group showed higher levels of plasma cell infiltration and higher expression of programmed cell death protein 1 (PDCD1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4). Ferroptosis inducers such as talazoparib or cisplatin had lower IC50 values in the high-risk group, while navitoclax (BCL-2 gene family inhibition and apoptosis inducer) had higher IC50 values in the high-risk group. Additionally, peroxiredoxin-6 (PRDX6) and acyl-CoA synthetase long chain family member 3 (ACSL3) were upregulated in LUAD tissues. Lipid peroxide assay showed that silencing PRDX6 or ACSL3 promoted lipid peroxidation and ferroptosis in lung cancer cells. Our novel ferroptosis-related signature shows potential clinical and functional importance in LUAD patients, and further research on ferroptosis as a therapeutic target in LUAD is warranted.

Construction and Validation of a Prognostic Model Based on Novel Senescence-Related Genes in Non-Small Cell Lung Cancer Patients with Drug Sensitivity and Tumor Microenvironment.

Cellular senescence contributes to cancer pathogenesis and immune regulation. Using the LASSO Cox regression, we developed a 12-gene prognostic signature for lung adenocarcinoma (LUAD) from The Cancer Genome Atlas (TCGA) and a Gene Expression Omnibus (GEO) dataset. We assessed gene expression, drug sensitivity, immune infiltration, and conducted cell line experiments. High-risk LUAD patients showed increased mortality risk and shorter survival (P<0.001). Senescence-related gene analysis indicated differences in protein phosphorylation and DNA methylation between normal individuals and LUAD patients. The high-risk group showed a positive association with PD-L1 expression (P=0.003). Single-cell sequencing data suggested PEBP1 might significantly impact T cell infiltration. We predicted potential sensitive compounds for 12 senescence genes and found GAPDH promoted cell line proliferation. We established a novel prognostic system based on a newly identified senescence gene. High-risk patients had elevated immunosuppressive markers, and PEBP1 might influence T cell infiltration significantly. GAPDH, expressed at higher levels in tumors, could affect cancer progression. Our drug prediction model may guide treatment selection.

Identification and validation of molecular subtype and prognostic signature for lung adenocarcinoma based on neutrophil extracellular traps.

Background: Neutrophil Extracellular Traps (NETs) are fibrous networks made of DNA-histone complexes and proteins protruded from activated neutrophils. Accumulating evidences have highlighted the vital role of NETs in tumor progression and diffusion. However, limited systematic studies regarding the role of NETs in LUAD have been performed. Methods: Differentially expressed NETs-related genes and their mutation landscape were identified with TCGA database. Consensus clustering analysis was performed to determine the NETs-related subtypes of LUAD. LASSO algorithm was employed to construct a prognostic signature. Moreover, GSE30219 and GSE31210 were used as independent validation. We also constructed a lncRNA-miRNA-mRNA regulatory axis with several miRNA and lncRNA databases. Results: Consensus clustering identified two NETs-related clusters in LUAD. High NETs score was correlated with a favorable overall survival, abundant immune cell infiltration, and high activity of immune response signal pathways. Six NET-related genes (G0S2, KCNJ15, S100A12, AKT2, CTSG, and HMGB1) with significant prognostic value were screened to develop a prognostic signature. LUAD patients with low-risk had a significantly favorable overall survival both in the training set and validation set. Moreover, NETs-related risk score and clinical stage could act as an independent prognostic factor for LUAD patients. Significant correlation was obtained between risk score and tumor immune microenvironment. We also identified lncRNA BCYRN1/miR-3664-5p/CTSG regulatory axis that may be involved in the progression of LUAD. Conclusion: We developed two molecular subtypes and a prognostic signature for LUAD based on NETs-related genes. This stratification could provide more evidences for estimating the prognosis and immunotherapy of LAUD patients.

A novel pyroptosis-related prognostic signature for lung adenocarcinoma: Identification and multi-angle verification.

Background: Lung adenocarcinoma (LUAD) is an aggressive disease of heterogeneous characteristics with poor prognosis and high mortality. Pyroptosis, a newly uncovered type of programmed cell death with an inflammatory nature, has been determined to hold substantial importance in the progression of tumors. Despite this, the knowledge about pyroptosis-related genes (PRGs) in LUAD is limited. This study aimed to develop and validate a prognostic signature for LUAD based on PRGs. Methods: In this research, gene expression information from The Cancer Genome Atlas (TCGA) served as the training cohort and data from Gene Expression Omnibus (GEO) was utilized as the validation cohort. PRGs list was taken from the Molecular Signatures Database (MSigDB) and previous studies. Univariate Cox regression and Lasso analysis were then conducted to identify prognostic PRGs and develop a LUAD prognostic signature. The Kaplan-Meier method, univariate and multivariate Cox regression models were employed to assess the independent prognostic value and forecasting accuracy of the pyroptosis-related prognostic signature. The correlation between prognostic signature and immune infiltrating was analyzed to examine the role in tumor diagnosis and immunotherapy. Further, RNA-seq as well as quantitative real-time polymerase chain reaction (qRT-PCR) analysis in separate data sets was applied in order to validate the potential biomarkers for LUAD. Results: A novel prognostic signature based on 8 PRGs (BAK1, CHMP2A, CYCS, IL1A, CASP9, NLRC4, NLRP1, and NOD1) was established to predict the survival of LUAD. The prognostic signature proved to be an independent prognostic factor of LUAD with satisfactory sensitivity and specificity in the training and validation sets. High-risk scores subgroups in the prognostic signature were significantly associated with advanced tumor stage, poor prognosis, less immune cell infiltration, and immune function deficiency. RNA sequencing and qRT-PCR analysis confirmed that the expression of CHMP2A and NLRC4 could be used as biomarkers for LUAD. Conclusion: We have successfully developed a prognostic signature consisting of eight PRGs that providing a novel perspective on predicting prognosis, assessing infiltration levels of tumor immune cells, and determining the outcomes of immunotherapy for LUAD.

Establishment of a prognostic signature for lung adenocarcinoma using cuproptosis-related lncRNAs

ObjectiveTo establish a prognostic signature for lung adenocarcinoma (LUAD) based on cuproptosis-related long non-coding RNAs (lncRNAs), and to study the immune-related functions of LUAD.MethodsFirst, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate COX analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate COX analysis were performed to analyze the cuproptosis-related lncRNAs, and a prognostic signature was established. Second, univariate COX analysis and multivariate COX analysis were performed for independent prognostic analyses. Receiver operating characteristic (ROC) curves, C index, survival curve, nomogram, and principal component analysis (PCA) were performed to evaluate the results of the independent prognostic analyses. Finally, gene enrichment analyses and immune-related function analyses were also carried out.Results(1) A total of 1,297 cuproptosis-related lncRNAs were screened. (2) A LUAD prognostic signature containing 13 cuproptosis-related lncRNAs was constructed (NIFK-AS1, AC026355.2, SEPSECS-AS1, AL360270.1, AC010999.2, ABCA9-AS1, AC032011.1, AL162632.3, LINC02518, LINC0059, AL031600.2, AP000346.1, AC012409.4). (3) The area under the multi-indicator ROC curves at 1, 3, and 5 years were AUC1 = 0.742, AUC2 = 0.708, and AUC3 = 0.762, respectively. The risk score of the prognostic signature could be used as an independent prognostic factor that was independent of other clinical indicators. (4) The results of gene enrichment analyses showed that 13 biomarkers were primarily related to amoebiasis, the wnt signaling pathway, hematopoietic cell lineage. The ssGSEA volcano map showed significant differences between high- and low-risk groups in immune-related functions, such as human leukocyte antigen (HLA), Type_II_IFN_Reponse, MHC_class_I, and Parainflammation (P < 0.001).ConclusionsThirteen cuproptosis-related lncRNAs may be clinical molecular biomarkers for the prognosis of LUAD.

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD. RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465. A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues. Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.

Identification of a cuproptosis-related lncRNA prognostic signature in lung adenocarcinoma.

Cuproptosis-related long non-coding RNA (lncRNA) diseases are associated with the occurrence and development of tumors. This study aimed to investigate whether cuproptosis-related lncRNA can predict the prognosis of patients with lung adenocarcinoma (LUAD). Cuproptosis-related lncRNA prognosis (CLPS) model was successfully constructed through cox regression and lasso regression analyses. Then, the prognostic value of CLPS model was tested through the survival analysis, the ROC curve and the nomogram. Finally, the correlation of CLPS model with tumor immunity and tumor mutation burden was analyzed, and the potential susceptibility of drugs for LUAD were predicted. CLPS model for LUAD (AC090948.1, CRIM1-DT, AC026356.2, AC004832.5, AL161431.1) was successfully constructed, which has an independent prognostic value. Furthermore, the risk score of CLPS model was correlated with tumor immune characteristics and immune escape, which can predict the sensitivity of drugs including Cisplatin, Etoposide, Gemcitabine, and Erlotinib. In conclusion, it was found that CLPS model was associated with tumor immunity and tumor mutation load, which also predicted four potentially sensitive drugs for LUAD patients at different risks.