Identifying novel risk genes in intracranial aneurysm by integrating human proteomes and genetics.

Genome-wide association studies (GWAS) have become increasingly popular for detecting numerous loci associated with intracranial aneurysm (IA), but how these loci function remains unclear. In this study, we employed an integrative analytical pipeline to efficiently transform genetic associations and identify novel genes for IA. Using multidimensional high-throughput data, we integrated proteome-wide association studies (PWAS), transcriptome-wide association studies (TWAS), Mendelian randomization (MR) and Bayesian co-localization analyses to prioritize genes that can increase IA risk by altering their expression and protein abundances in the brain and blood. Moreover, single-cell RNA sequencing (scRNA-seq) of the circle of Willis was performed to enrich filtered genes in cells, and gene set enrichment analysis (GSEA) was conducted for each gene using bulk RNA-seq data for IA. No significant genes with cis-regulated plasma protein levels were proven to be associated with IA. The protein abundances of five genes in the brain were found to be associated with IA. According to cellular enrichment analysis, these five genes were expressed mainly in the endothelium, fibroblasts and vascular smooth muscle cells. Only three genes, CNNM2, GPRIN3 and UFL1, passed MR and Bayesian co-localization analyses. While UFL1 was not validated in confirmation PWAS as it was not profiled, it was validated in TWAS. GSEA suggested these three genes are associated with the cell cycle. In addition, the protein abundance of CNNM2 was found to be associated with IA rupture (based on PWAS, MR and co-localization analyses). Our findings indicated that CNNM2, GPRIN3 and UFL1 (CNNM2 correlated with IA rupture) are potential IA risk genes that may provide a broad hint for future research on possible mechanisms and therapeutic targets for IA.

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailments is well-documented, its specific impact on HCM pathogenesis remains uncertain. Five distinct machine learning algorithms, namely LASSO, SVM, RF, Boruta and XGBoost, were utilized to discover new biomarkers associated with HCM. A unique nomogram was developed using two newly identified biomarkers and subsequently validated. Furthermore, samples of HCM and normal heart tissues were gathered from our institution to confirm the variance in expression levels and prognostic significance of GATM and MGST1. Five novel biomarkers (DARS2, GATM, MGST1, SDSL and ARG2) associated with HCM were identified. Subsequent validation revealed that GATM and MGST1 exhibited significant diagnostic utility for HCM in both the training and test cohorts, with all AUC values exceeding 0.8. Furthermore, a novel risk assessment model for HCM patients based on the expression levels of GATM and MGST1 demonstrated favourable performance in both the training (AUC = 0.88) and test cohorts (AUC = 0.9). Furthermore, our study revealed that GATM and MGST1 exhibited elevated expression levels in HCM tissues, demonstrating strong discriminatory ability between HCM and normal cardiac tissues (AUC of GATM = 0.79; MGST1 = 0.86). Our findings suggest that two specific cell types, monocytes and multipotent progenitors (MPP), may play crucial roles in the pathogenesis of HCM. Notably, GATM and MGST1 were found to be highly expressed in various tumours and showed significant prognostic implications. Functionally, GATM and MGST1 are likely involved in xenobiotic metabolism and epithelial mesenchymal transition in a wide range of cancer types. GATM and MGST1 have been identified as novel biomarkers implicated in the progression of both HCM and cancer. Additionally, monocytes and MPP may also play a role in facilitating the progression of HCM.

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

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

CTLs heterogeneity and plasticity: implications for cancer immunotherapy.

Cytotoxic T lymphocytes (CTLs) play critical antitumor roles, encompassing diverse subsets including CD4+, NK, and γδ T cells beyond conventional CD8+ CTLs. However, definitive CTLs biomarkers remain elusive, as cytotoxicity-molecule expression does not necessarily confer cytotoxic capacity. CTLs differentiation involves transcriptional regulation by factors such as T-bet and Blimp-1, although epigenetic regulation of CTLs is less clear. CTLs promote tumor killing through cytotoxic granules and death receptor pathways, but may also stimulate tumorigenesis in some contexts. Given that CTLs cytotoxicity varies across tumors, enhancing this function is critical. This review summarizes current knowledge on CTLs subsets, biomarkers, differentiation mechanisms, cancer-related functions, and strategies for improving cytotoxicity. Key outstanding questions include refining the CTLs definition, characterizing subtype diversity, elucidating differentiation and senescence pathways, delineating CTL-microbe relationships, and enabling multi-omics profiling. A more comprehensive understanding of CTLs biology will facilitate optimization of their immunotherapy applications. Overall, this review synthesizes the heterogeneity, regulation, functional roles, and enhancement strategies of CTLs in antitumor immunity, highlighting gaps in our knowledge of subtype diversity, definitive biomarkers, epigenetic control, microbial interactions, and multi-omics characterization. Addressing these questions will refine our understanding of CTLs immunology to better leverage cytotoxic functions against cancer.

Potential anti-tumor effects of regulatory T cells in the tumor microenvironment: a review.

Regulatory T cells (Tregs) expressing the transcription factor FoxP3 are essential for maintaining immunological balance and are a significant component of the immunosuppressive tumor microenvironment (TME). Single-cell RNA sequencing (ScRNA-seq) technology has shown that Tregs exhibit significant plasticity and functional diversity in various tumors within the TME. This results in Tregs playing a dual role in the TME, which is not always centered around supporting tumor progression as typically believed. Abundant data confirms the anti-tumor activities of Tregs and their correlation with enhanced patient prognosis in specific types of malignancies. In this review, we summarize the potential anti-tumor actions of Tregs, including suppressing tumor-promoting inflammatory responses and boosting anti-tumor immunity. In addition, this study outlines the spatial and temporal variations in Tregs function to emphasize that their predictive significance in malignancies may change. It is essential to comprehend the functional diversity and potential anti-tumor effects of Tregs to improve tumor therapy strategies.

Long noncoding RNA TMPO-AS1 accelerates glycolysis by regulating the miR-1270/PKM2 axis in colorectal cancer.

BACKGROUND: Long noncoding RNA thymopoietin-antisense RNA 1 (TMPO-AS1) is recognized as a participant in cancer progression. Nevertheless, its biological function in colorectal cancer remains obscure and needs further elucidation. METHODS AND RESULTS: First, we discovered enriched TMPO-AS1 in the tumor tissues that were related to poor prognosis. TMPO-AS1 knockdown enhanced SW480 cell apoptosis but inhibited invasion, proliferation, migration, and glucose metabolism. Further, MiR-1270 is directly bound with TMPO-AS1. MiR-1270 mimics were confirmed to inhibit cell proliferation, invasion, and glucose metabolism in our study. Mechanistically, miR-1270 directly is bound with the 3' untranslated regions (3'UTR) of PKM2 to downregulate PKM2. MiR-1270 inhibitors reversed the TMPO-AS1 knockdown's effect on suppressing the tumor cell proliferation, invasion, and glycolysis, while the knockdown of PKM2 further inverted the function of miR-1270 inhibitors on the TMPO-AS1 knockdown. CONCLUSIONS: This study illustrated that TMPO-AS1 advanced the development and the glycolysis of colorectal cancer by modulating the miR-1270/PKM2 axis, which provided a new insight into the colorectal cancer therapeutic strategy.

Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study.

Background: Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA. Methods: We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed. Results: Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes. Conclusion: CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.

Gamma delta T-cell-based immune checkpoint therapy: attractive candidate for antitumor treatment.

As a nontraditional T-cell subgroup, γδT cells have gained popularity in the field of immunotherapy in recent years. They have extraordinary antitumor potential and prospects for clinical application. Immune checkpoint inhibitors (ICIs), which are efficacious in tumor patients, have become pioneer drugs in the field of tumor immunotherapy since they were incorporated into clinical practice. In addition, γδT cells that have infiltrated into tumor tissues are found to be in a state of exhaustion or anergy, and there is upregulation of many immune checkpoints (ICs) on their surface, suggesting that γδT cells have a similar ability to respond to ICIs as traditional effector T cells. Studies have shown that targeting ICs can reverse the dysfunctional state of γδT cells in the tumor microenvironment (TME) and exert antitumor effects by improving γδT-cell proliferation and activation and enhancing cytotoxicity. Clarification of the functional state of γδT cells in the TME and the mechanisms underlying their interaction with ICs will solidify ICIs combined with γδT cells as a good treatment option.

Adverse reactions associated with immune checkpoint inhibitors and bevacizumab: A pharmacovigilance analysis.

Immune checkpoint inhibitors (ICIs) combined with the anti-angiogenesis drug bevacizumab is one of the future directions of immunotherapy. However, the potential adverse drug reactions (ADRs) caused by combination therapy remain unclear. Current research on ADRs of combination therapy in cancer patients is extremely limited. Our study aims to help determine the safety of combination therapy. We downloaded the ADR reports on combination therapy, from the first quarter of 2012 to the fourth quarter of 2021, from the FDA adverse event reporting system (FAERS) database and conducted a large-scale retrospective study. The ADR signals were monitored by reporting odds ratio (ROR) and analyzing the risk of different ADRs in patients with Pan-cancer. A total of 2094 cases were selected, after excluding duplicate data and the use of chemotherapy drugs. We evaluated the risk of ADR in Pan-cancer patients. Combination therapy was an independent risk factor for adverse drug reactions associated with interstitial lung disease (OR: 8.62; 95% CI: 6.14-12.10, P < .0001), hypertension (OR: 1.35; 95% CI: 1.11-1.65, P < .01) and gastrointestinal bleeding (OR: 3.16; 95% CI: 2.21-4.51, P < .0001). A subgroup analysis revealed that the risk of endocrine system-related ADRs was elevated in patients receiving different combination therapies or with certain tumor types. We retrospectively studied the ADR of combination therapy in Pan-cancer patients and analyzed the distribution characteristics of ADR from the perspectives of treatment strategy and cancer types to provide recommendations for the individualized management of patients receiving combination therapy.

Establishment of a risk classifier to predict the in-hospital death risk of nosocomial fungal infections in cancer patients.

BACKGROUND: Patients with malignancy are at a higher risk of developing nosocomial infections. However, limited studies investigated the clinical features and prognostic factors of nosocomial infections due to fungi in cancer patients. Herein, this study aims to investigate the clinical characteristics of in-hospital fungal infections and develop a nomogram to predict the risk of in-hospital death during fungal infection of hospitalized cancer patients. METHODS: This retrospective observational study enrolled cancer patients who experienced in-hospital fungal infections between September 2013 and September 2021. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of in-hospital mortality. Variables demonstrating significant statistical differences in the multivariate analysis were utilized to construct a nomogram for personalized prediction of in-hospital death risk associated with nosocomial fungal infections. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. RESULTS: A total of 216 participants were included in the study, of which 57 experienced in-hospital death. C.albicans was identified as the most prevalent fungal species (68.0%). Respiratory infection accounted for the highest proportion of fungal infections (59.0%), followed by intra-abdominal infection (8.8%). The multivariate regression analysis revealed that Eastern Cooperative Oncology Group Performance Status (ECOG-PS) 3-4 (odds ratio [OR] = 6.08, 95% confidence interval [CI]: 2.04-18.12), pulmonary metastases (OR = 2.76, 95%CI: 1.11-6.85), thrombocytopenia (OR = 2.58, 95%CI: 1.21-5.47), hypoalbuminemia (OR = 2.44, 95%CI: 1.22-4.90), and mechanical ventilation (OR = 2.64, 95%CI: 1.03-6.73) were independent risk factors of in-hospital death. A nomogram based on the identified risk factors was developed to predict the individual probability of in-hospital mortality. The nomogram demonstrated satisfactory performance in terms of classification ability (area under the curve [AUC]: 0.759), calibration ability, and net clinical benefit. CONCLUSIONS: Fungi-related nosocomial infections are prevalent among cancer patients and are associated with poor prognosis. The constructed nomogram provides an invaluable tool for oncologists, enabling them to make timely and informed clinical decisions that offer substantial net clinical benefit to patients.

Plasmacytoid dendritic cells cross-prime naive CD8 T cells by transferring antigen to conventional dendritic cells through exosomes.

Although plasmacytoid dendritic cells (pDCs) have been shown to play a critical role in generating viral immunity and promoting tolerance to suppress antitumor immunity, whether and how pDCs cross-prime CD8 T cells in vivo remain controversial. Using a pDC-targeted vaccine model to deliver antigens specifically to pDCs, we have demonstrated that pDC-targeted vaccination led to strong cross-priming and durable CD8 T cell immunity. Surprisingly, cross-presenting pDCs required conventional DCs (cDCs) to achieve cross-priming in vivo by transferring antigens to cDCs. Taking advantage of an in vitro system where only pDCs had access to antigens, we further demonstrated that cross-presenting pDCs were unable to efficiently prime CD8 T cells by themselves, but conferred antigen-naive cDCs the capability of cross-priming CD8 T cells by transferring antigens to cDCs. Although both cDC1s and cDC2s exhibited similar efficiency in acquiring antigens from pDCs, cDC1s but not cDC2s were required for cross-priming upon pDC-targeted vaccination, suggesting that cDC1s played a critical role in pDC-mediated cross-priming independent of their function in antigen presentation. Antigen transfer from pDCs to cDCs was mediated by previously unreported pDC-derived exosomes (pDCexos), that were also produced by pDCs under various conditions. Importantly, all these pDCexos primed naive antigen-specific CD8 T cells only in the presence of bystander cDCs, similarly to cross-presenting pDCs, thus identifying pDCexo-mediated antigen transfer to cDCs as a mechanism for pDCs to achieve cross-priming. In summary, our data suggest that pDCs employ a unique mechanism of pDCexo-mediated antigen transfer to cDCs for cross-priming.

Zinc oxide nanoparticles (ZnO-NPs) exhibit immune toxicity to crucian carp (Carassius carassius) by neutrophil extracellular traps (NETs) release and oxidative stress.

Zinc oxide nanoparticles (ZnO-NPs) are widely used in sunscreens, cosmetics, paint, construction materials, and other products. ZnO-NPs released into the environment can harm aquatic creatures and pose a health risk to humans through the food chain. ZnO-NPs are toxic to fish, but there are few reports on its immunotoxicity on crucian carp (Carassius carassius). In this study, ZnO-NPs increased the biochemical indexes of the liver in serum, including aspartate aminotransferase (AST) and alanine aminotransferase (ALT). In histopathological observation, many inflammatory cells were filled in the liver's central vein stimulated by ZnO-NPs. Furthermore, ZnO-NPs could increase malondialdehyde (MDA) level, lessen superoxide dismutase (SOD) level, and elevate the level of neutrophil extracellular traps (NETs). However, deoxyribonuclease I (DNase I) alleviated all biochemical indexes and histopathological changes. Immunofluorescence in vitro confirmed that NETs were composed of citrullinated histone 3, myeloperoxidase, and neutrophil elastase. ZnO-NPs-increased NETs were dependent on reactive oxygen species (ROS) and nicotinamide adenine dinucleotide phosphate (NADPH)-oxidase and were also related to partial processes of glycolysis. Our study confirms that ZnO-NPS has a toxic effect on the liver of crucian carp. DNase I can prevent liver damage caused by ZnO-NPs, which provides a new insight into the immunotoxicity of ZnO-NPs to fish.

Dendritic Cell-Based Vaccines Against Cancer: Challenges, Advances and Future Opportunities.

As the most potent professional antigen presenting cells, dendritic cells (DCs) have the ability to activate both naive CD4 and CD8 T cells. Recognized for their exceptional ability to cross-present exogenous antigens to prime naive antigen-specific CD8 T cells, DCs play a critical role in generating CD8 T cell immunity, as well as mediating CD8 T cell tolerance to tumor antigens. Despite the ability to potentiate host CD8 T cell-mediated anti-tumor immunity, current DC-based cancer vaccines have not yet achieved the promised success clinically with the exception of FDA-approved Provenge. Interestingly, recent studies have shown that type 1 conventional DCs (cDC1s) play a critical role in cross-priming tumor-specific CD8 T cells and determining the anti-tumor efficacy of cancer immunotherapies including immune checkpoint blockade (ICB). Together with promising clinical results in neoantigen-based cancer vaccines, there is a great need for DC-based vaccines to be further developed and refined either as monotherapies or in combination with other immunotherapies. In this review, we will present a brief review of DC development and function, discuss recent progress, and provide a perspective on future directions to realize the promising potential of DC-based cancer vaccines.

Lipid metabolism-related gene prognostic index (LMRGPI) reveals distinct prognosis and treatment patterns for patients with early-stage pulmonary adenocarcinoma.

Background: Lipid metabolism plays a pivotal role in cancer progression and metastasis. This study aimed to investigate the prognostic value of lipid metabolism-related genes (LMRGs) in early-stage lung adenocarcinoma (LUAD) and develop a lipid metabolism-related gene prognostic index (LMRGPI) to predict their overall survival (OS) and treatment response. Methods: A total of 774 early-stage LUAD patients were identified from The Cancer Genome Atlas (TCGA, 403 patients) database and Gene Expression Omnibus (GEO, 371 patients) database. The non-negative Matrix Factorization (NMF) algorithm was used to identify different population subtypes based on LMRGs. The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance. The characteristics of mutation landscape, enriched pathways, tumor microenvironment (TME), and treatment response between different LMRGPI groups were also investigated. Results: We identified two population subtypes based on LMRGs in the TCGA-LUAD cohort, with distinct prognosis, TME, and immune status being observed. LMRGPI was developed based on the expression levels of six LMRGs, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. Higher LMRGPI was correlated with poor OS both in TCGA and GSE68465 cohorts. Two nomograms were established to predict the survival probability of early-stage LUAD, with higher consistencies being observed between the predicted and actual OS. Higher LMRGPI was significantly correlated with more frequent TP53 mutation, higher tumor mutation burden (TMB), and up-regulation of CD274. Besides, patients with higher LMRGPI presented unremarkable responses for gefitinib, erlotinib, cisplatin, and vinorelbine, while they tend to have a favorable response for immune checkpoint inhibitors (ICIs). The opposite results were observed in the low-LMRGPI group. Conclusions: We comprehensively investigated the prognostic value of LMRGs in early-stage LUAD. Given its good prognostic ability, LMRGPI could serve as a promising biomarker to predict the OS and treatment response of these patients.

Chicken heterophils extracellular traps act as early effectors against cyclopiazonic acid dependent upon NADPH oxidase, ROS and glycolysis.

Cyclopiazonic acid (CPA) is a secondary metabolite produced by Aspergillus and Penicillium, which is present in contaminated crops and food, causing severe toxicity to humans and animals. Heterophil extracellular traps (HETs) are a novel host innate immune mechanism of chicken heterophils against pathogen infection. However, whether CPA can cause immunotoxicity of heterophils on HETs release remains unclear. Here, we attempt to detect the effects of CPA on HETs release, and further investigate the molecular mechanisms underlying these processes. We exposed heterophils to 2.5, 5, 10 µM CPA for 90 min. The results showed that CPA induced the release of HETs in heterophils, consisting of DNA-modified citrullinated histone 3 and elastase. The quantitative analysis of HETs content was positively correlated with CPA concentration. CPA also promoted reactive oxygen species production and phosphorylation of ERK1/2 and p38. In addition, CPA-triggered HETs formation was reduced by NADPH oxidase, ERK1/2, and p38 signaling pathway and glycolysis inhibitors, indicating that CPA-induced HETs were related to the production of ROS dependent on NADPH oxidase, ERK1/2, and p38 signaling pathways, as well as glycolysis. Our study describes the underlying mechanism of CPA-induced HETs release, which may provide a further understanding of the immunotoxicology of CPA poisoning.

Construction and Validation of a Novel Nomogram to Predict the Overall Survival of Patients With Combined Small Cell Lung Cancer: A Surveillance, Epidemiology, and End Results Population-Based Study.

INTRODUCTION: Combined small cell lung cancer (C-SCLC) represents a rare subtype of all small cell lung cancer cases, with limited studies investigated its prognostic factors. The aim of this study was to construct a novel nomogram to predict the overall survival (OS) of patients with C-SCLC. METHODS: In this retrospective study, a total of 588 C-SCLC patients were selected from the Surveillance, Epidemiology, and End Results database. The univariate and multivariate Cox analyses were performed to identify optimal prognostic variables and construct the nomogram, with concordance index (C-index), receiver operating characteristic curves, and calibration curves being used to evaluate its discrimination and calibration abilities. Furthermore, decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) were also adopted to assess its clinical utility and predictive ability compared with the classic TNM staging system. RESULTS: Seven independent predictive factors were identified to construct the nomogram, including T stage, N stage, M stage, brain metastasis, liver metastasis, surgery, and chemotherapy. We observed a higher C-index in both the training (.751) and validation cohorts (.736). The nomogram has higher area under the curve in predicting 6-, 12-, 18-, 24-, and 36-month survival probability of patients with C-SCLC. Meanwhile, the calibration curves also revealed high consistencies between the actual and predicted OS. DCA revealed that the nomogram could provide greater clinical net benefits to these patients. We found that the NRI for 6- and 12-month OS were .196 and .225, and the IDI for 6- and 12-month OS were .217 and .156 in the training group, suggesting that the nomogram can predict a more accurate survival probability. Similar results were also observed in the validation cohort. CONCLUSION: We developed and verified a novel nomogram that can help clinicians recognize high-risk patients with C-SCLC and predict their OS.

Assessment of the Clinical Utility of Circulating Tumor Cells at Different Time Points in Predicting Prognosis of Patients With Small Cell Lung Cancer: A Meta-Analysis.

OBJECTIVES: Numerous studies have elucidated that circulating tumor cells (CTCs) have significant prognostic value in various solid tumors. However, the prognostic value of CTCs in small cell lung cancer (SCLC) remains controversial. The current study was performed to investigate the prognostic significance of different time points of CTCs in SCLC. METHODS: PubMed, EMBASE, Web of Science, and Cochrane Library databases were retrieved for eligible studies. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to investigate the association between CTCs level and overall survival (OS) and progression-free survival (PFS) in SCLC. Furthermore, subgroup analyses, sensitivity analysis, Begg's and Egger's tests were also conducted. RESULTS: Sixteen cohort studies with 1103 participants were eligible for this meta-analysis. Our results revealed that higher pretreatment CTCs level was significantly correlated with worse OS in SCLC no matter CellSearch (HR, 2.95; 95%CI, 1.56-5.58; P = .001) or other methods (HR, 2.37; 95%CI, 1.13-4.99; P = .023) was used to detect CTCs. Higher pretreatment CTCs status detected by CellSearch was associated with shorter PFS (HR, 3.75; 95%CI, 2.52-5.57; P < .001), while there was no significant association when other methods were adopted to CTC detection (HR, 2.04; 95%CI, .73-5.68; P = .172). Likewise, we observed that higher post-therapy CTCs level detected by both CellSearch (HR, 2.99; 95%CI, 1.51-5.93; P = .002) and other methods (HR, 4.79; 95%CI, 2.03-11.32; P < .001) was significantly correlated with decreased OS in SCLC. However, higher post-therapy CTCs count detected by CellSearch was not correlated with worse PFS (HR, 1.80; 95%CI, .83-3.90; P = .135). Sensitivity analysis demonstrated that the pooled data were still stable after eliminating studies one by one. However, significant publication bias was observed between pretreatment CTCs level detected by CellSearch and OS of SCLC. CONCLUSION: Dynamic monitoring of CTCs level could be a non-invasive and effective tool to predict the disease progression and prognosis in patients with SCLC.

Tumor Mutation Burden, Immune Cell Infiltration, and Construction of Immune-Related Genes Prognostic Model in Head and Neck Cancer.

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and the prognosis of HNSCC remains bleak. Numerous studies revealed that the tumor mutation burden (TMB) could predict the survival outcomes of a variety of tumors. Objectives: This study aimed to investigate the TMB and immune cell infiltration in these patients and construct an immune-related genes (IRGs) prognostic model. Methods: The expression data of 546 HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. All patients were divided into high- and low- TMB groups, and the relationship between TMB and clinical relevance was further analyzed. The differentially expressed genes (DEGs) were identified using the R software package, limma. Functional enrichment analyses were conducted to identify the significantly enriched pathways between two groups. CIBERSORT algorithm was adopted to calculate the abundance of 22 leukocyte subtypes. The IRGs prognostic model was constructed via the multivariate Cox regression analysis. Results: Missense mutation and single nucleotide variants (SNV) were the most predominant mutation types in HNSCC. TP53, TTN, and FAT1 were the most frequently mutated genes. Patients with high TMB were observed with worse survival outcomes. The functional analysis of TMB associated DEGs showed that the identified DEGs mainly involved in spliceosome, RNA degradation, proteasome, and RNA polymerase pathways. We observed that macrophages, T cells CD8, and T cells CD4 memory were the most commonly infiltrated subtypes of immune cells in HNSCC. Finally, an IRGs prognostic model was constructed, and the AUC of the ROC curve was 0.635. Conclusions: Our results suggest that high TMB is associated with poor prognosis in HNSCC patients. The constructed model has potential prognostic value for the prognosis of these individuals, and it needs to be further validated in large-scale and prospective studies.

Sodium molybdate induces heterophil extracellular traps formation in chicken.

Molybdenum (Mo) is not only an important rare metal that is widely used in industrial production but also an essential trace element for plants and animals. Nevertheless, in Mo polluted areas, excess Mo intake will not only cause gout in humans but also cause diarrhea in livestock and growth inhibition of chickens. Heterophils extracellular traps (HETs) are an important way to clear pathogens in the innate immune system of the chicken. However, the effects of Mo on the innate immune responses of HETs formation in chicken, and the mechanism undergoing this phenomenon remain unknown. In the study, we firstly aim to investigate the effects of sodium molybdate (Na2MoO4) on chicken HETs formation in vitro, and further to explore its related metabolic requirements and molecular mechanisms. Chicken heterophils were cultured with Na2MoO4, and Na2MoO4-induced HETs structures were analyzed by confocal microscopy. Moreover, Na2MoO4-induced HETs were quantified by Quant-iT PicoGreen® dsDNA Assay kits and fluorescence microplate. It has been shown that Na2MoO4 truly triggered HETs-like structures that were composed of DNA decorated with citrullinated histone 3 (citH3) and elastase. The inhibitors of NADPH oxidase, ERK1/2 and p38 MAPK signaling pathway significantly reduced Na2MoO4-induced HETs formation. Further experiments on energy metabolism involving Na2MoO4-induced HETs formation showed that Na2MoO4-induced HETs release was relevant to glucose, and the inhibitors of glycolysis including 3PO, AZD23766 and 3-Bromopyuvic acid, the inhibitors of glucose transport including STF31 and Ritonavir and NSC23766 significantly decreased Na2MoO4-induced HETs formation. In summary, these results demonstrate that Mo does induce chicken HETs formation in vitro, and the formation of HETs is a process relying on glucose transport 1 (GLUT1)，glucose transport 4 (GLUT4), glycolysis, and ROS production depended on the activation of NADPH oxidase, ERK1/2 and p38 signaling pathways, which also reflects the early innate immune responses of chicken against excessive molybdenum intake.

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network's lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.