Ovarian cancer (OC) usually progresses rapidly and is associated with high mortality, while a reliable clinical factor for OC patients to predict prognosis is currently lacking. Recently, the pathogenic role of neutrophils releasing neutrophil extracellular traps (NETs) in various cancers including OC has gradually been recognized. The study objective was to determine whether NETs-related biomarkers can be used to accurately predict the prognosis and guide clinical decision-making in OC. In this study, we utilized univariate and multivariate Cox regression to identify key prognostic features and developed a model with six NETs-related lncRNAs, selected via LASSO regression. The model's predictive capability was assessed through Kaplan-Meier, ROC, and Cox analyses. To understand the model's mechanisms, we conducted GO term analysis, KEGG pathway enrichment, and GSEA. We also analyzed gene mutation status, tumor mutation load, survival rates, and model correlation. Additionally, we compared immune functions, immune checkpoint expression, and chemotherapy sensitivity between risk groups. Besides, we validated the model's predictive value using test data and tissues acquired from our institution. Finally, we performed in vitro and in vivo experiments to confirm the expression of model lncRNAs and the cellular level function of GAS5. We developed a model using six NETs-associated lncRNAs: GAS5, GBP1P1, LINC00702, LINC01933, LINC02362, and ZNF687-AS1. The model's predictive performance, evaluated via ROC curve, was compared with traditional clinicopathological features. GO process analysis highlighted molecular functions related to antigen binding and immune system biological processes. Variations were observed in transcription regulators affecting immune response, inflammation, cytotoxicity, and regulation. We also predicted IC50 values for chemotherapeutic drugs (bexarotene, bicalutamide, embelin, GDC0941, and thapsigargin) in high- and low-risk groups, finding higher IC50 values in low-risk patients. The risk model's robustness was validated using OC cells, tissues, and clinical datas.

Neuroblastoma (NB) is a malignant tumor originating from the peripheral sympathetic nervous system and high-risk NB patients have a dismal prognosis. Recent studies have underscored the pivotal role of neutrophil extracellular traps (NETs) in the proliferation, metastasis and immune evasion of cancer. To explore the effect of NETs on NB, we have carried out a systematic analysis and showed several findings in the present work. First, expression profiles along with clinical data were analyzed using the training dataset GSE62564 and 36 NETs-related genes were identified to be significantly associated with overall survival. Following LASSO regression analysis, 11 genes were enrolled to construct the NETs signature, which exhibited a robust predictive capability for overall survival with exhibiting high AUC values within the training set. Validation cohorts confirmed a similar predictive efficacy. Next, NB patients were classified into subgroups based on median risk scores and differentially expressed genes were analyzed. Furthermore, the study performed comprehensive analyses encompassing functional enrichment, immune infiltration and drug sensitivity. Enrichment analysis revealed that the high-risk NBs with high-risk score displayed characteristics of oncogenic malignancy, poor prognosis and immunosuppression. Notably, the risk score exhibited a strong correlation with infiltration levels of various immune cells and the sensitivity to anti-cancer drugs, and was further recognized as an independent prognostic factor for NB patients. In summary, our study elucidates a novel NETs-related gene signature comprising 11 genes, which serves a reliable predictor for NB prognosis.

Lung cancer is one of the most common and lethal malignancies in China. In the context of the tumor microenvironment, neutrophil extracellular traps (NETs) released by neutrophils exert a profound impact on the occurrence and progression of lung cancer. Although the exact mechanisms by which NETs promote tumor growth have not been fully elucidated, existing research has revealed their multiple roles in tumor growth, invasion, metastasis, and cancer-related thrombosis. This article will review the molecular biology mechanisms and research progress of NETs in lung cancer based on recent studies.
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Ovarian cancer (OvCa) is the most lethal gynaecology malignancies worldwide. Neutrophil extracellular traps (NETs), net-like protein structures produced by activated neutrophils and DNA-histone complexes, have a central role in tumours, though haven't been fully explored in OvCa. We obtained transcriptome data from TCGA-OvCa database (n = 376) as training, ICGC-OvCa database (n = 111) as validation and GTEx database (n = 180) as controls. Through LASSO-COX Regression analysis, we identified an eight-gene signature among 87 NETs-related genes, which was significantly related to poor prognosis in both TCGA-OvCa and ICGC-OvCa cohorts (Log-rank p-value = 0.0003 and 0.0014). Next, we constructed and validated a prognostic nomogram, consist of NETs-related signature and clinical features (C-index = 0.82). We evaluated 22 typical immune cell infiltration through CIBERSORT analysis, which implied upregulation of memory CD4 + T cells, follicular helper T cells and neutrophils in high-risk group. Additionally, we predicted therapy sensitivity through TIDE algorithm, indicating that high NETs-riskscore exhibited more sensitivity towards Sorafenib and less sensitivity towards immunotherapy. We initially reported that RAC2 upregulation was associated with NETs formation and poor prognosis (p-value < 0.05) through IHC analysis of tissue microarrays (n = 125). Conclusively, NETs-related signature was reliable for OvCa prognosis prediction and therapy assessment. Especially, RAC2 was predominantly related to NETs formation, thus providing hints towards anti-tumour mechanism of NETs in OvCa.

Neutrophil extracellular traps (NETs), crucial in immune defense mechanisms, are renowned for their propensity to expel decondensed chromatin embedded with inflammatory proteins. Our comprehension of NETs in pathogen clearance, immune regulation and disease pathogenesis, has grown significantly in recent years. NETs are not only pivotal in the context of infections but also exhibit significant involvement in sterile inflammation. Evidence suggests that excessive accumulation of NETs can result in vessel occlusion, tissue damage, and prolonged inflammatory responses, thereby contributing to the progression and exacerbation of various pathological states. Nevertheless, NETs exhibit dual functionalities in certain pathological contexts. While NETs may act as autoantigens, aggregated NET complexes can function as inflammatory mediators by degrading proinflammatory cytokines and chemokines. The delineation of molecules and signaling pathways governing NET formation aids in refining our appreciation of NETs' role in immune homeostasis, inflammation, autoimmune diseases, metabolic dysregulation, and cancer. In this comprehensive review, we delve into the multifaceted roles of NETs in both homeostasis and disease, whilst discussing their potential as therapeutic targets. Our aim is to enhance the understanding of the intricate functions of NETs across the spectrum from physiology to pathology.

Neutrophil extracellular traps (NETs) are related to the prognosis of cancer patients. Nevertheless, the potential prognostic values of NETs in skin cutaneous melanoma (SKCM) remains largely unknown.

The NET-related gene signature was constructed by LASSO Cox regression analysis using the TCGA-SKCM cohort. The overall survival (OS) and immune status in SKCM patients between the high- and low-NET score (high-score, low-score) groups were explored. The scRNA-seq dataset GSE115978 was used to understand the role of NET score in SKCM at single cell resolution.

A five NET genes-based signature (TLR2, CLEC6A, PDE4B, SLC22A4 and CYP4F3) was constructed as the NET-related prognostic model for SKCM. The OS of SKCM patients with low-score was better than that in patients with high-score. Additionally, NET score was negatively associated with infiltration of some immune cells (e.g. type I Macrophages, CD8-T cells, CD4-T cells). Moreover, patients with high-score had low stromal, immune and ESTIMATE scores. Furthermore, drug sensitivity analysis results showed that Lapatinib, Trametinib and Erlotinib may have better therapeutic advantages in patients with high-score.

We established a NET-related five gene signature in SKCM and found that the NET-related signature may exhibit a good predictive ability for SKCM prognosis. The NET score may not only predict the survival outcome and drug sensitivity in SKCM, but also reflect the immune conditions of SKCM patients.

Non-small cell lung cancer (NSCLC) is the primary form of lung cancer, and the combination of chemotherapy with immunotherapy offers promising treatment options for patients suffering from this disease. However, the emergence of drug resistance significantly limits the effectiveness of these therapeutic strategies. Consequently, it is imperative to devise methods for accurately detecting and evaluating the efficacy of these treatments.

To identify the metabolic signatures associated with neutrophil extracellular traps (NETs) and chemoimmunotherapy efficacy in NSCLC patients.

In total, 159 NSCLC patients undergoing first-line chemoimmunotherapy were enrolled. We first investigated the characteristics influencing clinical efficacy. Circulating levels of NETs and cytokines were measured by commercial kits. Liquid chromatography tandem mass spectrometry quantified plasma metabolites, and differential metabolites were identified. Least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest algorithms were employed. By using plasma metabolic profiles and machine learning algorithms, predictive metabolic signatures were established.

First, the levels of circulating interleukin-8, neutrophil-to-lymphocyte ratio, and NETs were closely related to poor efficacy of first-line chemoimmunotherapy. Patients were classed into a low NET group or a high NET group. A total of 54 differential plasma metabolites were identified. These metabolites were primarily involved in arachidonic acid and purine metabolism. Three key metabolites were identified as crucial variables, including 8,9-epoxyeicosatrienoic acid, L-malate, and bis(monoacylglycerol)phosphate (18:1/16:0). Using metabolomic sequencing data and machine learning methods, key metabolic signatures were screened to predict NET level as well as chemoimmunotherapy efficacy.

The identified metabolic signatures may effectively distinguish NET levels and predict clinical benefit from chemoimmunotherapy in NSCLC patients.

Cell death plays a crucial role in various physiological and pathological processes. Until recently, programmed cell death was mainly attributed to caspase-dependent apoptosis. However, emerging evidence suggests that caspase-independent cell death (CICD) mechanisms also contribute significantly to cellular demise. We and others have reported and functionally characterized numerous long noncoding RNAs (lncRNAs) that modulate caspase-dependent apoptotic pathways potentially in a pathway-dependent manner. However, the interplay between lncRNAs and CICD pathways has not been comprehensively documented. One major reason for this is that most CICD pathways have been recently discovered with some being partially characterized at the molecular level. In this review, we discuss the emerging evidence that implicates specific lncRNAs in the regulation and execution of CICD. We summarize the diverse mechanisms through which lncRNAs modulate different forms of CICD, including ferroptosis, necroptosis, cuproptosis, and others. Furthermore, we highlight the intricate regulatory networks involving lncRNAs, protein-coding genes, and signaling pathways that orchestrate CICD in health and disease. Understanding the molecular mechanisms and functional implications of lncRNAs in CICD may unravel novel therapeutic targets and diagnostic tools for various diseases, paving the way for innovative strategies in disease management and personalized medicine. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Activated neutrophils release depolymerized chromatin and protein particles into the extracellular space, forming reticular Neutrophil Extracellular Traps (NETs). This process is accompanied by programmed inflammatory cell death of neutrophils, known as NETosis. Previous reports have demonstrated that NETosis plays a significant role in immune resistance and microenvironmental regulation in cancer. This study sought to characterize the function and molecular mechanism of NETosis-correlated long non-coding RNAs (NCLs) in the prognostic treatment of liver hepatocellular carcinoma (LIHC).

We obtained the transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and evaluated the expression of NCLs in LIHC. A prognostic signature of NCLs was constructed using Cox and Last Absolute Shrinkage and Selection Operator (Lasso) regression, while the accuracy of model was validated by the ROC curves and nomogram, etc. In addition, we analyzed the associations between NCLs and oncogenic mutation, immune infiltration and evasion. Finally, LIHC patients were classified into four subgroups based on consensus cluster analysis, and drug sensitivity was predicted.

After screening, we established a risk model combining 5 hub-NCLs and demonstrated its reliability. Independence checks suggest that the model may serve as an independent predictor of LIHC prognosis. Enrichment analysis revealed a concentration of immune-related pathways in the high-risk group. Immune infiltration indicates that immunotherapy could be more effective in the low-risk group. Upon consistent cluster analysis, cluster subgroup 4 presented a better prognosis. Sensitivity tests showed the distinctions in therapeutic effectiveness among various drugs in different subgroups.

Overall, we have developed a prognostic signature that can discriminate different LIHC subgroups through the 5 selected NCLs, with the objective of providing LIHC patients a more precise, personalized treatment regimen.

Neutrophil extracellular traps (NETs) have been reported to be crucial in tumorigenesis and malignant progression. However, their prognostic significance, association with tumor immune microenvironment (TIME), and therapeutic response in osteosarcoma (OS) stills remain unclear. Hence, TARGET and GSE21257 cohorts were included for analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were conducted to extract NETs-derived genes. Subsequently, the NETs score (NETScore) model, consisting of 4 signature genes, was established and validated with the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. Our results indicated that NETScore has satisfactory predictability of the patient's overall survival, with AUC values at 1-, 3- and 5-year in the training cohort of 0.798, 0.792 and 0.804, respectively; similar prominent prediction performance was obtained in three validation cohorts. Further, real-time quantitative PCR (RT-qPCR) assay was conducted to determine the expression of signature genes in human osteoblasts and OS cells. Besides, NETScore and clinical factors (age, gender, metastatic status) were integrated to construct a nomogram. C-index and AUC values at 1-, 3-, and 5-year were above 0.800, displaying robust predictive performance. Patients with high and low NETScore had different immune statuses and drug sensitivity. Meanwhile, several positive regulatory immune function pathways, including T cell proliferation, activation and migration, were significantly suppressed among patients with high NETScore. Summarily, we established a novel NETScore that can accurately predict OS patients' prognosis, which correlated closely with the immune landscape and therapeutic response and might help to guide clinical decision-making.

Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS.

We applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR).

Using the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS.

In conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases. Immediate early response 5 like (IER5L) plays crucial roles in progression and prognosis for several tumors, but its role in NSCLC remains unclear.

Gene expression and mutation profiles, DNA methylation data, and clinical information for cancers were downloaded from multiple databases. Relative expression, prognostic value, and correlation with disease progression of IER5L were analyzed in multiple cancers, including NSCLC. Upstream mechanisms were explored using a transcriptional network. Functional enrichment analysis, protein-protein interaction network, and gene set enrichment analysis were applied to study downstream mechanisms. Correlations of IER5L with immune infiltration, immune molecules, methylation status, and tumor mutation burden (TMB) were analyzed using R language. Finally, quantitative polymerase chain reaction (qPCR) and single-cell RNA sequencing (scRNA seq) analysis were performed to validate IER5L expression in NSCLC.

Pan-cancer analysis displayed that IER5L expression was upregulated in multiple cancers and was associated with disease prognosis and progression, including NSCLC, which was validated using qPCR. scRNA seq analysis showed that multiple cells had increased IER5L expression. An EGR1-hsa-miR-8075-IER5L network was constructed for NSCLC. A total of 191 DEGs were identified between the two IER5L groups, which were significantly enriched in biological process of action potential, sodium ion transport, and regulation of membrane potential. Increased IER5L expression was primarily enriched in cell cycle, NOTCH signaling pathway, and oxidative phosphorylation pathway, and was correlated with increased regulatory T cells and neutrophils, elevated levels of immune molecules, and higher TMB.

Our findings show that increased IER5L expression was correlated with progression and prognosis in multiple cancers as well as with immune infiltration and immune molecules in NSCLC. Thus, IER5L is a prognostic biomarker in multiple cancers and may correlate with immunotherapeutic response in NSCLC.

Bladder cancer is the most common malignant tumor of urinary system, and its morbidity and mortality are increasing rapidly. Although great advances have been made in medical technology in recent years, there is still a lack of effective prognostic and therapeutic methods for bladder cancer. NETs are reticulated DNA structures decorated with various protein substances released extracellularly by neutrophils stimulated by strong signals. Recently, it has been found that NETs are closely related to the growth, metastasis and drug resistance of many types of cancers. However, up to now, the research on the relationship between NETs and bladder cancer is still not enough. In this study, we aimed to conduct a comprehensive analysis of NRGs in bladder cancer tissues to evaluate the relationship between NRGs and prognosis prediction and sensitivity to therapy in patients with bladder cancer. We scored NRGs in each tissue by using ssGSEA, and selected gene sets that were significantly associated with NRGs scores by using the WCGNA algorithm. Based on the expression profiles of NRGs-related genes, NMF clustering analysis was performed to identify different BLCA molecular subtypes. For the differentially expressed genes between subtypes, we used univariate COX regression, LASSO regression and multivariate COX regression to further construct a hierarchical model of BLCA patients containing 10 genes. This model and the nomogram based on this model can accurately predict the prognosis of BLCA patients in multiple datasets. Besides, BLCA patients classified based on this model differ greatly in their sensitivity to immunotherapy and targeted therapies, which providing a reference for individualized treatment of patients with bladder cancer.

The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.

This study aimed to investigate CCNB1, CENPF, and Neutrophils as diagnostic predictors of lung cancer and to explore their association with clinical prognosis. Clinical data were obtained for a total of 52 patients. In addition, we downloaded 555 lung cancer-related samples from the cancer genome atlas (TCGA) database. Differentially expressed genes were further screened. Immune cell infiltration and survival analysis were performed. Immunohistochemistry was used to confirm gene expression. Peripheral blood analysis showed that neutrophil percentages were significantly reduced in patients with lung cancer. The least absolute shrinkage and selection operator and multivariate regression analysis revealed that CCNB1 and CENPF were lung cancer risk factors. Both CCNB1 and CENPF are overexpressed in lung cancer. The clinical diagnostic model constructed using CCNB1, CENPF, and neutrophils had a C-index of 0.994. This model area under the curve (AUC) and internal validation C-index values were 0.994 and 0.993, respectively. The elevated expression of CCNB1 and CENPF showed that the survival rate of lung cancer patients was reduced. CCNB1 and CENPF expression was positively correlated with the clinical stage of lung cancer. Further studies confirmed that CCNB1 and CENPF are overexpressed in lung cancer tissues. The clinically constructed model with high accuracy based on CCNB1, CENPF, and neutrophils demonstrated that these are crucial indicators for lung cancer diagnosis. High expression of CCNB1 and CENPF indicates a poor prognosis in patients with lung cancer.

Primary colorectal cancer (CRC) often leads to liver metastasis, possibly due to the formation of pre-metastatic niche (PMN) in liver. Thus, unravelling the key modulator in metastasis is important for the development of clinical therapies. Gut microbiota dysregulation is a key event during CRC progression and metastasis. Numerous studies have elucidated the correlation between specific gut bacteria strains (e.g., pks + E. coli and Bacteroides fragilis) and CRC initiation, and gut bacteria translocation is commonly witnessed during CRC progression. Gut microbiota shapes tumor microenvironment (TME) through direct contact with immune cells or through its functional metabolites. However, how gut microbiota facilitates CRC metastasis remains controversial. Meanwhile, recent studies identify the dissemination of bacteria from gut lumen to liver, suggesting the role of gut microbiota in shaping tumor PMN. A pro-tumoral PMN is characterized by the infiltration of immunosuppressive cells and increased pro-inflammatory immune responses. Notably, neutrophils form web-like structures known as neutrophil extracellular traps (NETs) both in primary TME and metastatic sites, NETs are involved in cancer progression and metastasis. In this review, we focus on the role of gut microbiota in CRC progression and metastasis, highlight the multiple functions of different immune cell types in TME, especially neutrophils and NETs, discuss the possible mechanisms of gut microbiota in shaping PMN formation, and provide therapeutical indications in clinic.

The notion that neutrophils only perform a specific set of single functions in the body has changed with the advancement of research methods. As the most abundant myeloid cells in human blood, neutrophils are currently emerging as important regulators of cancer. Given the duality of neutrophils, neutrophil-based tumor therapy has been clinically carried out in recent years and has made some progress. But due to the complexity of the tumor microenvironment, the therapeutic effect is still not satisfactory. Therefore, in this review, we discuss the direct interaction of neutrophils with the five most common cancer cells and other immune cells in the tumor microenvironment. Also, this review covered current limitations, potential future possibilities, and therapeutic approaches targeting neutrophil function in cancer therapy.

Neutrophil extracellular traps (NETs) released by neutrophils are crucial for cancer development, metastasis, and can indicate gastric cancer (GC) patients' prognosis. This study reveals the relevance of NETs-related genes to GC through transcriptome analysis.

We obtained transcriptome sequencing data of GC from UCSC Xena and screened prognostic NETs-related genes by GEPIA2 database. The signature for NETs was subsequently created using the LASSO-Cox regression. The clinical value of model was further explored using the nomogram and was externally validated by the GEO database. After that, we employed GO, KEGG, and GSEA enrichment analyses to evaluate the bio-functional enrichment and related pathways. Additionally, ESTIMATE, MCP counter, and ssGSEA scores were used to investigate the immunological microenvironment of GC patients. Finally, in the external cohort, neutrophil elastase (NE)-DNA complexes were measured by ELISA, and the prognostic value of NE-DNA in GC was investigated using Cox analysis.

Seven NETs-associated genes (PDE4B, CD93, CTSG, IL6, ELANE, KCNJ15, and CRISPLD2) were filtered to establish the signature and participated in building the nomogram. In comparison to the high-risk group, the overall survival (OS) was much longer in the low-risk group (P=0.005). The validation cohort demonstrated the acceptable predictive ability of the nomogram. The signature was enriched in biological features such as extracellular matrix organization, epithelial-mesenchymal transition and inflammatory response. Moreover, there were substantial differences in immune cell infiltration across the different risk groups (p<0.001), especially the high-risk group having more immune cells that are engaged in the antigen presentation process and associated functions. Finally, in the external cohort, NE-DNA levels were shown to be an independent factor affecting OS prognosis (p=0.006).

Overall, this research identified a novel signature based on seven NETs-associated genes to predict prognosis and identify tumor microenvironment of GC. And high NE-DNA level may be a critical factor in the poor OS associated with NETs.

Neutrophil extracellular traps (NETs) could promote tumor growth and distant metastases. Molecular subtypes of bladder cancer were identified with consensus cluster analysis. A NETs-related prognostic signature was constructed with LASSO cox regression analysis. As a result, we identified three subtypes of bladder cancer, which had a distinct difference in prognosis, immune microenvironment, TIDE score, mRNAsi score and IC50 score. We also developed a prognostic signature based on 5 NETs-related genes, which had a good performance in clinical outcome prediction of bladder cancer. These results may provide more data about the vital role of NETs in bladder cancer.

Before very sensitive current genomics platforms were discovered, long non-coding RNAs (lncRNAs) as controllers of gene expression, were thought to be accumulated genetic garbage. The past few years have seen a lot of interest in a large classification of non-coding transcripts with an indeterminate length of more than 200 nucleotides [1]. lncRNAs' association with immunity and disease progression has been revealed by a growing body of experimental research. Only a limited subset of lncRNAs, however, has solid proof of their role. It is also clear that various immune cells express lncRNAs differently. In this review, we concentrated on the role of lncRNA expression in the regulation of immune cell function and response to pathological conditions in macrophages, dendritic cells, natural killer (NK) cells, neutrophils, Myeloid-derived suppressor cells (MDSCs), T cells, and B cells. The innate and adaptive immune response systems may be significantly regulated by lncRNAs, according to emerging research. To discover possible therapeutic targets for the therapy of different diseases, it may be helpful to have a better realization of the molecular mechanisms beyond the role of lncRNAs in the immune response. Therefore, it is crucial to investigate lncRNA expression and comprehend its significance for the immune system.

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.

Neutrophil extracellular traps (NETs) are web-like structures formed by neutrophils, and their main function is antimicrobial defense. Moreover, NETs have numerous roles in the pathogenesis and progression of cancers. However, the potential roles of NET-related genes in renal cell carcinoma remain unclear. In this study, we comprehensively investigated the NETs patterns and their relationships with tumor environment (TME), clinicopathological features, prognosis, and prediction of therapeutic benefits in the clear cell renal cell carcinoma (ccRCC) cohort.

We obtained the gene expression profiles, clinical characteristics, and somatic mutations of patients with ccRCC from The Cancer Genome Atlas database (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress datasets, respectively. ConsensusCluster was performed to identify the NET clusters. The tumor environment scores were evaluated by the "ESTIMATE," "CIBERSORT," and ssGSEA methods. The differential analysis was performed by the "limma" R package. The NET-scores were constructed based on the differentially expressed genes (DEGs) among the three cluster patterns using the ssGSEA method. The roles of NET scores in the prediction of immunotherapy were investigated by Immunophenoscores (TCIA database) and validated in two independent cohorts (GSE135222 and IMvigor210). The prediction of targeted drug benefits was implemented using the "pRRophetic" and Gene Set Cancer Analysis (GSCA) datasets. Real-time quantitative reverse transcription polymerase chain reaction (RT-PCR) was performed to identify the reliability of the core genes' expression in kidney cancer cells.

Three NET-related clusters were identified in the ccRCC cohort. The patients in Cluster A had more metabolism-associated pathways and better overall survival outcomes, whereas the patients in Cluster C had more immune-related pathways, a higher immune score, and a poorer prognosis than those in Cluster B. Based on the DEGs among different subtypes, patients with ccRCC were divided into two gene clusters. These gene clusters demonstrated significantly different immune statuses and clinical features. The NET scores were calculated based on the ten core genes by the Gene Set Variation Analysis (GSVA) package and then divided ccRCC patients into two risk groups. We observed that high NET scores were associated with favorable survival outcomes, which were validated in the E-MTAB-1980 dataset. Moreover, the NET scores were significantly associated with immune cell infiltration, targeted drug response, and immunotherapy benefits. Subsequently, we explored the expression profiles, methylation, mutation, and survival prediction of the 10 core genes in TCGA-KIRC. Though all of them were associated with survival information, only four out of the 10 core genes were differentially expressed genes in tumor samples compared to normal tissues. Finally, RT-PCR showed that MAP7, SLC16A12, and SLC27A2 decreased, while SLC3A1 increased, in cancer cells.

NETs play significant roles in the tumor immune microenvironment of ccRCC. Identifying NET clusters and scores could enhance our understanding of the heterogeneity of ccRCC, thus providing novel insights for precise individual treatment.

Background: Renal cell carcinoma (RCC) is one of the most common cancers, with an annual incidence of nearly 400,000 cases worldwide. Increasing evidence has also demonstrated the vital role of neutrophil extracellular traps (NETs) in cancer progression and metastatic dissemination. Methods: Consensus cluster analysis was performed to determine the number of ccRCC subtypes. The Kruskal-Wallis test or Student t-test was performed to evaluate the difference of infiltrating immune cell and gene expression in different groups. The Kaplan-Meier (KM) method was used to draw the survival curve. LASSO cox regression analysis was conducted to construct a NET-related prognostic signature. We also constructed a lncRNA-miRNA-mRNA regulatory axis by several miRNA and lncRNA target databases. Results: A total of 23 differentially expressed NET-related genes were obtained in ccRCC. Three clusters of ccRCC cases with significant difference in prognosis, immune infiltration, and chemotherapy and targeted therapy were identified. LASSO Cox regression analysis identified a NET-related prognostic signature including six genes (G0S2, DYSF, MMP9, SLC22A4, SELP, and KCNJ15), and this signature had a good performance in predicting the overall survival of ccRCC patients. The expression of prognostic signature genes was significantly correlated with the pTMN stage, immune infiltration, tumor mutational burdens, microsatellite instability, and drug sensitivity of ccRCC patients. MMP9 was identified as the hub gene. We also identified the lncRNA UBA6-AS1/miR-149-5p/MMP9 regulatory axis for the progression of ccRCC. Conclusion: Collectively, the current study identified three molecular clusters and a prognostic signature for ccRCC based on neutrophil extracellular traps. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical transformation.

Backgrounds: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis, and immune escape of cancers. We aim to investigate Long non-coding RNAs (lncRNAs) that are correlated to NETs to find some potentially useful biomarkers for lung adenocarcinoma (LUAD), and to explore their correlations with immunotherapy and chemotherapy, as well as the tumor microenvironment. Methods: Based on the The Cancer Genome Atlas (TCGA) database, we identified the prognosis-related lncRNAs which are associated with NETs using cox regression. The patients were then separated into two clusters based on the expression of NETs-associated lncRNAs to perform tumor microenvironment analysis and immune-checkpoint analysis. Least absolute shrinkage and selection operator (LASSO) regression was then performed to establish a prognostic signature. Furthermore, nomogram analysis, tumor mutation burden analysis, immune infiltration analysis, as well as drug sensitivity analysis were performed to test the signature. Results: Using univariate cox regression, we found 10 NETs-associated lncRNAs that are associated with the outcomes of LUAD patients. Also, further analysis which separated the patients into 2 clusters showed that the 10 lncRNAs had significant correlations with the tumor microenvironment. Using LASSO regression, we finally constructed a signature to predict the outcomes of the patients based on 4 NETs-associated lncRNAs. The 4 NETs-associated lncRNAs were namely SIRLNT, AL365181.3, FAM83A-AS1, and AJ003147.2. Using Kaplan-Meier (K-M) analysis, we found that the risk model was strongly associated with the survival outcomes of the patients both in the training group and in the validation group 1 and 2 (p < 0.001, p = 0.026, and p < 0.01). Using receiver operating characteristic (ROC) curve, we tested the sensitivity combined with the specificity of the model and found that the risk model had a satisfactory level of 1-year, 3-year, and 5-year concordance index (C-index) (C = 0.661 in the training group, C = 0.679 in validation group 1, C = 0.692 in validation group 2). We also explored the immune microenvironment and immune checkpoint correlation of the risk model and found some significant results. Conclusion: We constructed a NETs-associated lncRNA signature to predict the outcome of patients with LUAD, which is associated with immunephenoscores and immune checkpoint-gene expression.

Neoadjuvant chemoimmunotherapy for non-small cell lung cancer (NSCLC) has improved pathological responses and survival rates compared with chemotherapy alone, leading to Food and Drug Administration (FDA) approval of nivolumab plus chemotherapy for resectable stage IB-IIIA NSCLC (AJCC 7th edition) without ALK or EGFR alterations. Unfortunately, a considerable percentage of tumors do not completely respond to therapy, which has been associated with early disease progression. So far, it is impossible to predict these events due to lack of knowledge. In this study, we characterized the gene expression profile of tumor samples to identify new biomarkers and mechanisms behind tumor responses to neoadjuvant chemoimmunotherapy and disease recurrence after surgery.

Tumor bulk RNA sequencing was performed in 16 pretreatment and 36 post-treatment tissue samples from 41 patients with resectable stage IIIA NSCLC treated with neoadjuvant chemoimmunotherapy from NADIM trial. A panel targeting 395 genes related to immunological processes was used. Tumors were classified as complete pathological response (CPR) and non-CPR, based on the total absence of viable tumor cells in tumor bed and lymph nodes tested at surgery. Differential-expressed genes between groups and pathway enrichment analysis were assessed using DESeq2 and gene set enrichment analysis. CIBERSORTx was used to estimate the proportions of immune cell subtypes.

CPR tumors had a stronger pre-established immune infiltrate at baseline than non-CPR, characterized by higher levels of IFNG, GZMB, NKG7, and M1 macrophages, all with a significant area under the receiver operating characteristic curve (ROC) >0.9 for CPR prediction. A greater effect of neoadjuvant therapy was also seen in CPR tumors with a reduction of tumor markers and IFNγ signaling after treatment. Additionally, the higher expression of several genes, including AKT1, BST2, OAS3, or CD8B; or higher dendritic cells and neutrophils proportions in post-treatment non-CPR samples, were associated with relapse after surgery. Also, high pretreatment PD-L1 and tumor mutational burden levels influenced the post-treatment immune landscape with the downregulation of proliferation markers and type I interferon signaling molecules in surgery samples.

Our results reinforce the differences between CPR and non-CPR responses, describing possible response and relapse immune mechanisms, opening the possibility of therapy personalization of immunotherapy-based regimens in the neoadjuvant setting of NSCLC.

Neutrophil extracellular traps (NETs) that are produced in the tumour microenvironment (TME) have been suggested to play an essential role in the dissemination of metastatic cancer under multiple infectious and inflammatory conditions. However, the functions of NETs in promoting non-small cell lung cancer (NSCLC) metastasis and the underlying mechanisms remain incompletely understood. Here, we found that NETs promoted NSCLC cell invasion and migration by inducing epithelial to mesenchymal transition (EMT). To explore how NETs contribute to NSCLC metastasis, microarrays were performed to identify substantial numbers of long noncoding RNAs (lncRNAs) and mRNAs that were differentially expressed in NSCLC cells after stimulation with NETs. Interestingly, we observed that the expression of lncRNA MIR503HG was downregulated after NETs stimulation, and ectopic MIR503HG expression reversed the metastasis-promoting effect of NETs in vitro and in vivo. Notably, bioinformatics analysis revealed that differentially expressed genes were involved in the NOD-like receptor and NF-κB signalling pathways that are associated with inflammation. NETs facilitated EMT and thereby contributed to NSCLC metastasis by activating the NF-κB/NOD-like receptor protein 3 (NLRP3) signalling pathway. Further studies revealed that MIR503HG inhibited NETs-triggered NSCLC cell metastasis in an NF-κB/NLRP3-dependent manner, as overexpression of NF-κB or NLRP3 impaired the suppressive effect of MIR503HG on NETs-induced cancer cell metastasis. Together, these results show that NETs activate the NF-κB/NLRP3 pathway by downregulating MIR503HG expression to promote EMT and NSCLC metastasis. Targeting the formation of NETs may be a novel therapeutic strategy for treating NSCLC metastasis.