Signature For Lung Cancer Research Articles

Prognostic significance of a 3-gene ferroptosis-related signature in lung cancer via LASSO analysis and cellular functions of UBE2Z

Ferroptosis is a newly identified form of non-apoptotic programmed cell death resulting from iron-dependent lipid peroxidation. It is controlled by integrated oxidation and antioxidant systems. Ferroptosis exerts a crucial effect on the carcinogenesis of several cancers, including pulmonary cancer. Herein, a ferroptosis-associated gene signature for lung cancer prognosis and diagnosis was identified using integrative bioinformatics analyses. From the FerrDB database, 256 ferroptotic regulators and markers were identified. Of these, 25 exhibited differential expression between lung cancer and non-cancerous samples, as evidenced by the GSE19804 and GSE7670 datasets from the GEO database. Utilizing LASSO Cox regression analysis on TCGA-LUAD data, a potent 3-gene risk signature comprising CAV1, RRM2, and EGFR was established. This signature adeptly differentiates various survival outcomes in lung cancer patients, including overall survival and disease-specific intervals. Based on the 3-gene risk signature, lung cancer patients were categorized into high-risk and low-risk groups. Comparative analysis revealed 69 differentially expressed genes between these groups, with UBE2Z significantly associated with overall survival in TCGA-LUAD. UBE2Z was found to be upregulated in LUAD tissues and cells compared to normal controls. Functionally, the knockdown of UBE2Z curtailed aggressive behaviors in LUAD cells, including viability, migration, and invasion. Moreover, this knockdown led to a decrease in the mesenchymal marker vimentin while elevating the epithelial marker E-cadherin within LUAD cell lines. In conclusion, the ferroptosis-associated 3-gene risk signature effectively differentiates prognosis and clinical features in patients with lung cancer. UBE2Z was identified through this model, and it is upregulated in LUAD samples. Its knockdown inhibits aggressive cellular behaviors, suggesting UBE2Z's potential as a therapeutic target for lung cancer treatment.

Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images.

Lung cancer, the second most common type of cancer worldwide, presents significant health challenges. Detecting this disease early is essential for improving patient outcomes and simplifying treatment. In this study, we propose a hybrid framework that combines deep learning (DL) with quantum computing to enhance the accuracy of lung cancer detection using chest radiographs (CXR) and computerized tomography (CT) images. Our system utilizes pre-trained models for feature extraction and quantum circuits for classification, achieving state-of-the-art performance in various metrics. Not only does our system achieve an overall accuracy of 92.12%, it also excels in other crucial performance measures, such as sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%). These results demonstrate that our hybrid approach can more accurately identify lung cancer signatures compared to traditional methods. Moreover, the incorporation of quantum computing enhances processing speed and scalability, making our system a promising tool for early lung cancer screening and diagnosis. By leveraging the strengths of quantum computing, our approach surpasses traditional methods in terms of speed, accuracy, and efficiency. This study highlights the potential of hybrid computational technologies to transform early cancer detection, paving the way for wider clinical applications and improved patient care outcomes.

Promising microRNAs in pre-diagnostic serum associated with lung cancer up to eight years before diagnosis: a HUNT study

IntroductionBlood biomarkers for early detection of lung cancer (LC) are in demand. There are few studies of the full microRNome in serum of asymptomatic subjects that later develop LC. Here we searched for novel microRNA biomarkers in blood from non-cancer, ever-smokers populations up to eight years before diagnosis.MethodsSerum samples from 98,737 subjects from two prospective population studies, HUNT2 and HUNT3, were considered initially. Inclusion criteria for cases were: ever-smokers; no known cancer at study entrance; 0–8 years from blood sampling to LC diagnosis. Each future LC case had one control matched to sex, age at study entrance, pack-years, smoking cessation time, and similar HUNT Lung Cancer Model risk score. A total of 240 and 72 serum samples were included in the discovery (HUNT2) and validation (HUNT3) datasets, respectively, and analysed by next-generation sequencing. The validated serum microRNAs were also tested in two pre-diagnostic plasma datasets from the prospective population studies NOWAC (n = 266) and NSHDS (n = 258). A new model adding clinical variables was also developed and validated.ResultsFifteen unique microRNAs were discovered and validated in the pre-diagnostic serum datasets when all cases were contrasted against all controls, all with AUC > 0.60. In combination as a 15-microRNAs signature, the AUC reached 0.708 (discovery) and 0.703 (validation). A non-small cell lung cancer signature of six microRNAs showed AUC 0.777 (discovery) and 0.806 (validation). Combined with clinical variables of the HUNT Lung Cancer Model (age, gender, pack-years, daily cough parts of the year, hours of indoor smoke exposure, quit time in years, number of cigarettes daily, body mass index (BMI)) the AUC reached 0.790 (discovery) and 0.833 (validation). These results could not be validated in the plasma samples.ConclusionThere were a few significantly differential expressed microRNAs in serum up to eight years before diagnosis. These promising microRNAs alone, in concert, or combined with clinical variables have the potential to serve as early diagnostic LC biomarkers. Plasma is not suitable for this analysis. Further validation in larger prospective serum datasets is needed.

MiR-29 and MiR-140 regulate TRAIL-induced drug tolerance in lung cancer

ABSTRACT Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has chemotherapeutic potential as a regulator of an extrinsic apoptotic ligand, but its effect as a drug is limited by innate and acquired resistance. Recent findings suggest that an intermediate drug tolerance could mediate acquired resistance, which has made the main obstacle for limited utility of TRAIL as an anti-cancer therapeutics. We propose miRNA-dependent epigenetic modification drives the drug tolerant state in TRAIL-induced drug tolerant (TDT). Transcriptomic analysis revealed miR-29 target gene activation in TDT cells, showing oncogenic signature in lung cancer. Also, the restored TRAIL-sensitivity was associated with miR-29ac and 140-5p expressions, which is known as tumor suppressor by suppressing oncogenic protein RSK2 (p90 ribosomal S6 kinase), further confirmed in patient samples. Moreover, we extended this finding into 119 lung cancer cell lines from public data set, suggesting a significant correlation between TRAIL-sensitivity and RSK2 mRNA expression. Finally, we found that increased RSK2 mRNA is responsible for NF-κB activation, which we previously showed as a key determinant in both innate and acquired TRAIL-resistance. Our findings support further investigation of miR-29ac and -140-5p inhibition to maintain TRAIL-sensitivity and improve the durability of response to TRAIL in lung cancer.

Prognostic and immunotherapeutic significances of M2 macrophage-related genes signature in lung cancer.

Objective: We aimed to investigate the immunological significance of M2 macrophage-related genes in lung cancer (LC) patients, specifically focusing on constructing a risk score to predict patient prognosis and response to immunotherapy. Methods: We developed a novel risk score by identifying and incorporating 12 M2 macrophage-related genes. The risk score was calculated by multiplying the expression levels of risk genes by their respective coefficients. Through comprehensive enrichment analysis, we explored the potential functions distinguishing high- and low-risk groups. Moreover, we examined the relationship between patients in different risk groups and immune infiltration as well as their response to immunotherapy. The single-cell RNA sequencing data were acquired to ascertain the spatial pattern of RNF130 expression. The expression of RNF130 was examined using TCGA datasets and verified by HPA. The qRT-PCR was employed to examine RNF130 expression in LC cells. Finally, in vitro experiments were carried out to validate the expression and function of RNF130. Results: Our results indicated that the risk score constructed from 12 M2 macrophage-related genes was an independent prognostic factor. Patients in the high-risk group had a significantly worse prognosis compared to those in the low-risk group. Functional enrichment analysis showed a significant relationship between the risk score and immunity. Furthermore, we explored immune infiltration in different risk groups using seven immune algorithms. The results demonstrated a negative correlation between high-risk group patients and immune infiltration of B cells, CD4+ cells, and CD8+ cells. We further validated these findings using an immunotherapy response database, which revealed that high-risk patients were more likely to exhibit immune evasion and might have poorer immunotherapy outcomes. Additionally, drug sensitivity analysis indicated that patients in the high-risk group were more sensitive to certain chemotherapeutic and targeted drugs than those in the low-risk group. Single-cell analysis indicated that macrophages were the primary site of RNF130 distribution. The results from the TCGA and HPA database demonstrated a trend toward a low expression of RNF130 in LC. Finally, in vitro experiments further validated the expression and function of RNF130 in LC cells. Conclusions: The high-risk group constructed with M2 macrophage-related genes in LC was closely associated with poor prognosis, low immune cell infiltration, and poorer response to immunotherapy. This risk score can help differentiate and predict the prognosis and immune status of LC patients, thereby aiding in the development of precise and personalized immunotherapy strategies.

Metallomic Signatures of Lung Cancer and Chronic Obstructive Pulmonary Disease.

Lung cancer (LC) is the leading cause of cancer deaths, and chronic obstructive pulmonary disease (COPD) can increase LC risk. Metallomics may provide insights into both of these tobacco-related diseases and their shared etiology. We conducted an observational study of 191 human serum samples, including those of healthy controls, LC patients, COPD patients, and patients with both COPD and LC. We found 18 elements (V, Al, As, Mn, Co, Cu, Zn, Cd, Se, W, Mo, Sb, Pb, Tl, Cr, Mg, Ni, and U) in these samples. In addition, we evaluated the elemental profiles of COPD cases of varying severity. The ratios and associations between the elements were also studied as possible signatures of the diseases. COPD severity and LC have a significant impact on the elemental composition of human serum. The severity of COPD was found to reduce the serum concentrations of As, Cd, and Tl and increased the serum concentrations of Mn and Sb compared with healthy control samples, while LC was found to increase Al, As, Mn, and Pb concentrations. This study provides new insights into the effects of LC and COPD on the human serum elemental profile that will pave the way for the potential use of elements as biomarkers for diagnosis and prognosis. It also sheds light on the potential link between the two diseases, i.e., the evolution of COPD to LC.

Metabolomic investigation of urinary extracellular vesicles for early detection and screening of lung cancer

Lung cancer is a prevalent cancer type worldwide that often remains asymptomatic in its early stages and is frequently diagnosed at an advanced stage with a poor prognosis due to the lack of effective diagnostic techniques and molecular biomarkers. However, emerging evidence suggests that extracellular vesicles (EVs) may promote lung cancer cell proliferation and metastasis, and modulate the anti-tumor immune response in lung cancer carcinogenesis, making them potential biomarkers for early cancer detection. To investigate the potential of urinary EVs for non-invasive detection and screening of patients at early stages, we studied metabolomic signatures of lung cancer. Specifically, we conducted metabolomic analysis of 102 EV samples and identified metabolome profiles of urinary EVs, including organic acids and derivatives, lipids and lipid-like molecules, organheterocyclic compounds, and benzenoids. Using machine learning with a random forest model, we screened for potential markers of lung cancer and identified a marker panel consisting of Kanzonol Z, Xanthosine, Nervonyl carnitine, and 3,4-Dihydroxybenzaldehyde, which exhibited a diagnostic potency of 96% for the testing cohort (AUC value). Importantly, this marker panel also demonstrated effective prediction for the validation set, with an AUC value of 84%, indicating the reliability of the marker screening process. Our findings suggest that the metabolomic analysis of urinary EVs provides a promising source of non-invasive markers for lung cancer diagnostics. We believe that the EV metabolic signatures could be used to develop clinical applications for the early detection and screening of lung cancer, potentially improving patient outcomes.

Genomic Characterization Revealed PM2.5-Associated Mutational Signatures in Lung Cancer Including Activation of APOBEC3B

Fine particulate matter (PM2.5) exposure causes DNA mutations and abnormal gene expression leading to lung cancer, but the detailed mechanisms remain unknown. Here, analysis of genomic and transcriptomic changes upon a PM2.5 exposure-induced human bronchial epithelial cell-based malignant transformed cell model in vitro showed that PM2.5 exposure led to APOBEC mutational signatures and transcriptional activation of APOBEC3B along with other potential oncogenes. Moreover, by analyzing mutational profiles of 1117 non-small cell lung cancers (NSCLCs) from patients across four different geographic regions, we observed a significantly higher prevalence of APOBEC mutational signatures in non-smoking NSCLCs than smoking in the Chinese cohorts, but this difference was not observed in TCGA or Singapore cohorts. We further validated this association by showing that the PM2.5 exposure-induced transcriptional pattern was significantly enriched in Chinese NSCLC patients compared with other geographic regions. Finally, our results showed that PM2.5 exposure activated the DNA damage repair pathway. Overall, here we report a previously uncharacterized association between PM2.5 and APOBEC activation, revealing a potential molecular mechanism of PM2.5 exposure and lung cancer.

Dissection of Cancer Mutational Signatures with Individual Components of Cigarette Smoking.

Tobacco smoke delivers a complex mixture of hazardous and potentially hazardous chemicals. Some of these may induce the formation of DNA mutations, which increases the risk of various cancers that display characteristic patterns of accumulated mutations arising from the causative exposures. Tracking the contributions of individual mutagens to mutational signatures present in human cancers can help understand cancer etiology and advance disease prevention strategies. To characterize the potential contributions of individual constituents of tobacco smoke to tobacco exposure-associated mutational signatures, we first assessed the toxic potential of 13 tobacco-relevant compounds by determining their impact on the viability of a human bronchial lung epithelial cell line (BEAS-2B). Experimentally derived high-resolution mutational profiles were characterized for the seven most potent compounds by sequencing the genomes of clonally expanded mutants that arose after exposure to the individual chemicals. Analogous to the classification of mutagenic processes on the basis of signatures from human cancers, we extracted mutational signatures from the mutant clones. We confirmed the formation of previously characterized benzo[a]pyrene mutational signatures. Furthermore, we discovered three novel mutational signatures. The mutational signatures arising from benzo[a]pyrene and norharmane were similar to human lung cancer signatures attributed to tobacco smoking. However, the signatures arising from N-methyl-N'-nitro-N-nitrosoguanidine and 4-(acetoxymethyl)nitrosamino]-1-(3-pyridyl)-1-butanone were not directly related to known tobacco-linked mutational signatures from human cancers. This new data set expands the scope of the in vitro mutational signature catalog and advances understanding of how environmental agents mutate DNA.

Development and validation of a novel M1 macrophage-related gene prognostic signature for lung cancer.

Lung cancer (LC) is the most common cancer. Using data from The Cancer Genome Atlas (TCGA), we analyzed the functional roles of M1 macrophage status in LC patients. Clinical and transcriptome data of LC patients were obtained from the TCGA dataset. We identified M1 macrophage-related genes in LC patients and investigated the underlying molecular mechanisms of these genes in LC patients. After performing a least absolute shrinkage and selection operator (LASSO) Cox regression analysis, the LC patients were divided into two subtypes, and the underlying mechanism of the association between them was further explored. A comparison of immune infiltration was conducted between the two subtypes. Based on gene set enrichment analysis (GSEA), the key regulators associated with subtypes were further explored. M1 macrophage-related genes were identified using TCGA data, and these genes might be related to the activation of the immune response and cytokine-mediated signaling pathways in LC. A seven M1 macrophage-related gene signature (including STAT1, TAP1, UBE2L6, TAP2, CXCR6, PSMB8 and CD2) was identified in LC using LASSO Cox regression analysis. Two subtypes (low risk and high risk) of LC patients were created based on the seven M1 macrophage-related gene signature. Univariate and multivariate survival analyses further confirmed that the subtype classification was an effective independent prognostic factor. Moreover, the two subtypes were correlated with immune infiltration, and GSEA revealed that the pathways of tumor cell proliferation and immune-related biological processes (BPs) might play an important role in LC in the high-risk group and low-risk group, respectively. M1 macrophage-related subtypes of LC were identified and were closely associated with immune infiltration. The gene signature involved in M1 macrophage-related genes could help make a distinction and predict prognosis for LC patients.

Retracted: Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning.

[This retracts the article DOI: 10.1155/2022/6495301.].

Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Lung Cancer

Despite the significant progress made towards comprehending the deregulated signatures in lung cancer, these vary from study to study. We reanalyzed 25 studies from the Gene Expression Omnibus (GEO) to detect and annotate co-deregulated signatures in lung cancer and in single-gene or single-drug perturbation experiments. We aimed to decipher the networks that these co-deregulated genes (co-DEGs) form along with their upstream regulators. Differential expression and upstream regulators were computed using Characteristic Direction and Systems Biology tools, including GEO2Enrichr and X2K. Co-deregulated gene expression profiles were further validated across different molecular and immune subtypes in lung adenocarcinoma (TCGA-LUAD) and lung adenocarcinoma (TCGA-LUSC) datasets, as well as using immunohistochemistry data from the Human Protein Atlas, before being subjected to subsequent GO and KEGG enrichment analysis. The functional alterations of the co-upregulated genes in lung cancer were mostly related to immune response regulating the cell surface signaling pathway, in contrast to the co-downregulated genes, which were related to S-nitrosylation. Networks of hub proteins across the co-DEGs consisted of overlapping TFs (SOX2, MYC, KAT2A) and kinases (MAPK14, CSNK2A1 and CDKs). Furthermore, using Connectivity Map we highlighted putative repurposing drugs, including valproic acid, betonicine and astemizole. Similarly, we analyzed the co-DEG signatures in single-gene and single-drug perturbation experiments in lung cancer cell lines. In summary, we identified critical co-DEGs in lung cancer providing an innovative framework for their potential use in developing personalized therapeutic strategies.

The tRNA-derived fragment signature in lung cancer

A new class of regulatory small non-coding RNAs called tRNA-derived fragments (tRDFs) has distinct biological roles in various types of cancer. In this issue of Molecular Therapy – Oncolytics, Zitong Gao et al. performed a thorough study of tRDFs in 1,550 patient samples of non-small cell lung cancer (NSCLC) and discovered 52 tRDFs with four subtypes. Based on expression profiles, six tRDFs were selected as diagnostic biomarkers. Two of these biomarkers were successfully validated in plasma samples, and six biomarkers confirmed the consistency of distinguished expression in NSCLC cell lines.

Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Lung Cancer Based on Deep Learning.

Acyl-CoA synthetase long-chain family member 4 (ACSL4) has been linked to the occurrence of tumors and is implicated in the ferroptosis process. Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, classification of cancer, and prediction of metastasis. Nonetheless, neither the level of ACSL4 expression nor its predictive significance in non-small-cell lung cancer (NSCLC) is well understood at this time. Predictions of the ACSL4 mRNA expressions in NSCLC and its link to NSCLC prognosis were made with the aid of the Oncomine and TCGA databases. By performing real-time PCR, we detected the levels of ACSL4 expression that were present in human NSCLC samples. Analyses of the diagnostic, as well as the prognostic significance of ACSL4 in NSCLC, were performed with the use of Kaplan-Meier curves. To assess the influence of ACSL4 on ferroptosis in NSCLC cell lines, an inducer of ferroptosis, namely, erastin, was utilized in this study. In NSCLC tissues, there was a substantial decrease in the level of ACSL4 expression (p < 0.001), and this was in line with the findings of the inquiry into the Oncomine and TCGA databases. After that, the findings of the immunohistochemistry analysis revealed that the ACSL4 staining was weakened in NSCLC samples in contrast with the normal samples. It was shown that the differential expression of ACSL4 was substantially linked to the stages of cancer, smoking behaviors, and the status of nodal metastases (all p < 0.001). According to the findings of the survival analysis, both RFS and OS were favorable among NSCLC patients who had elevated expression of ACSL4. The ferroptosis sensitization in cancer cells may be reestablished with upregulation of ACSL4 through gene transfection. Mechanistically, protein ubiquitination could perform a remarkable function in ACSL4-induced ferroptosis. ACSL4, which has a function in ferroptosis as both a contributor and monitor, was shown to be downregulated in NSCLC. This finding suggests that ACSL4 might function as a helpful diagnostic and prognostic biological marker and might also be considered a novel possible treatment target for NSCLC.

Prognostic implication of an energy metabolism-related 11-gene signature in lung cancer.

Energy metabolism therapy has gradually shown its potential in the treatment of tumor patients, but it has significant selectivity, thus distinguishing energy subtypes of lung adenocarcinoma (LUAD) is necessary to identify patients who may benefit from energy metabolism interference therapy. Gene expression data downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, molecular subtypes were selected using NMF algorithm, prognostic differentially expressed genes (DEGs) were identified with DESeq. 2 and survival package, Lasso and cox regression analysis were used to Construct of Risk Signature. The relationship between molecular subtypes and prognosis as well as clinical characteristics were evaluated. Univariate and multivariate COX regression were used to analyze the correlation between the signature and patient prognosis. Based on 592 energy metabolism-related genes, 430 LUAD samples were divided into three subtypes, of which C2 has the worst prognosis, and 942 prognostic DEGs were identified. 11-gene prognostic risk signature was constructed. Compared with the traditional clinical features of T, N, and age, this 11-gene signature performs better in predicting the risk of LUAD prognosis. At the same time, it is an independent risk factor for patient prognosis. The signature showed strong robustness in different cohorts. Compared with other published signatures, 11-gene signatures have strong clinical applicability and accuracy. The predictive signature will enable patients with LUAD to be more accurately managed in clinical practice.

Single-Cell RNA Sequencing Reveals the Tissue-Specific Immune Signatures of Lung Cancer with COPD

Single-Cell RNA Sequencing Reveals the Tissue-Specific Immune Signatures of Lung Cancer with COPD

Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) fingerprinting enabled treatment monitoring of pulmonary carcinoma cells in real time

Lung cancer is one of the leading causes of cancer related deaths in the United States. A novel volatile analysis platform is needed to complement current diagnostic techniques and better elucidate chemical signatures of lung cancer and subsequent treatments. A systems biology bottom-up approach using cell culture volatilomics was employed to identify pathological volatile fingerprints of lung cancer in real time. An advanced secondary electrospray ionization (SESI) source, named SuperSESI was used in this study and directly attached to a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). We performed a series of experiments to determine if our optimized SESI-HRMS platform can distinguish between cancer types by sampling their in vitro volatilome profiles. We detected 60 significant volatile organic compound (VOC) features in positive mode that were deemed of cancer cell origin. The cell derived features were used for subsequent analyses to distinguish between our two studied lung cancer types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Partial least squares-discriminant analysis (PLS-DA) model revealed a good separation of the two cancer types, suggesting unique chemical composition of their headspace profiles. A receiver operating characteristic (ROC) curve using 10 prominent features was used to predict disease type, with an area under the curve (AUC) of 0.811. Cultures were also treated with cisplatin to determine the feasibility of classifying drug treatment from expelled gases. A PLS-DA model revealed independent clustering based on their headspace profiles. An ROC curve using the top features driving separation of PLS-DA model suggested good accuracy with an AUC of 1. It is thus possible to benefit from the advantages of this platform to distinguish the unique volatile fingerprints of cancers to uncover potential biomarkers for cancer type differentiation and treatment monitoring.

2 - Genetic mutations and signatures in lung cancer – findings from a whole exome sequencing platform

2 - Genetic mutations and signatures in lung cancer – findings from a whole exome sequencing platform

Serum Exosomes and Their miRNA Load-A Potential Biomarker of Lung Cancer.

Simple SummaryExosomes are an emerging source of cancer biomarkers. Molecular components of serum-derived exosomes have been addressed in several reports in the context of biomarkers for early detection of lung cancer. However, despite the promising results of pilot studies, the clinical applicability of such biomarkers has not been validated yet. In this review, the diagnostic potential of miRNA content of serum-derived exosomes is presented. Moreover, potential target genes and signaling pathways affected by miRNA present in lung cancer signatures are discussed.Early detection of lung cancer in screening programs is a rational way to reduce mortality associated with this malignancy. Low-dose computed tomography, a diagnostic tool used in lung cancer screening, generates a relatively large number of false-positive results, and its complementation with molecular biomarkers would greatly improve the effectiveness of such programs. Several biomarkers of lung cancer based on different components of blood, including miRNA signatures, were proposed. However, only a few of them have been positively validated in the context of early cancer detection yet, which imposes a constant need for new biomarker candidates. An emerging source of cancer biomarkers are exosomes and other types of extracellular vesicles circulating in body fluids. Hence, different molecular components of serum/plasma-derived exosomes were tested and showed different levels in lung cancer patients and healthy individuals. Several studies focused on the miRNA component of these vesicles. Proposed signatures of exosome miRNA had promising diagnostic value, though none of them have yet been clinically validated. These signatures involved a few dozen miRNA species overall, including a few species that recurred in different signatures. It is worth noting that all these miRNA species have cancer-related functions and have been associated with lung cancer progression. Moreover, a few of them, including known oncomirs miR-17, miR-19, miR-21, and miR-221, appeared in multiple miRNA signatures of lung cancer based on both the whole serum/plasma and serum/plasma-derived exosomes.

Identification of Survival-Associated Gene Signature in Lung Cancer Coexisting With COPD.

Background: Chronic obstructive pulmonary disease (COPD) and lung cancer often coexist, which is associated with a worse prognosis. Thousands of biomarkers related to the survival of lung cancer have been investigated. However, those which can predict the survival of lung cancer coexisting with COPD are currently lacking. The present study aimed to identify novel gene signatures to predict the survival of patients with lung cancer coexisting COPD.Method: RNA-sequence data of lung cancer and control accompanying with matched clinical information were retrieved from the Cancer Genome Atlas (TCGA). Differently expressed genes (DEGs) associated with lung cancer coexisting COPD were screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. Univariate and multivariate Cox regression analyses were applied to identify survival-associated DEGs and to construct survival-associated gene signature. Kaplan-Meier survival analysis and calibration plots of the nomogram were performed to test the predictive accuracy of the gene signature. qPCR was performed to validate the genes in the prognostic signature.Results: Sequence data from 70 patients with lung cancer coexisting COPD, 127 with lung cancer alone and 108 control tissues were included for analysis. A total of 2424 DEGs were identified when comparing lung cancer coexisting COPD with controls. The biological process was primarily associated with DNA-binding transcription activator activity, peptidase inhibitor activity, endopeptidase inhibitor activity, et al. KEGG pathways were mainly enriched in neuroactive ligand-receptor interaction, cell cycle, and Staphylococcus aureus infection. A survival-associated gene signature consisting of CEACAM5, RASAL1, CSTL1, CNGB1, and SLC4A3 was identified and represented as risk score. The high-risk score group had significantly worse survival than the low-risk score group (P < 0.001). Areas under receiver operating characteristic curves were 0.943, 0.773, 0.888 for predicting overall survival at 1-, 3-, and 5-year, respectively. The risk score was an independent predictor of survival, independent of clinical factors. High conformity of the actual survival and the nomogram–predicted probability of survival by applying the risk score. Upregulation of the five genes in patients with lung cancer coexisting COPD were confirmed by qPCR in an independent cohort.Conclusion: Our study constructed and validated a novel prognostic gene signature for predicting survival of patient with lung cancer coexisting COPD, which may contribute to the clinical treatment decisions.