Identification of risk factors of EGFR-TKIs primary resistance in lung adenocarcinoma patients and construction of a risk predictive model: a case-control study.

Background: Lung cancer is one of the malignancies with the highest incidence and mortality rates. Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are recommended as the first-line treatment for patients with EGFR-mutated lung adenocarcinoma (LUAD). However, some patients with EGFR-sensitive mutations develop primary resistance to EGFR-TKIs. This study aims to analyze the clinical characteristics of LUAD patients with primary resistance to EGFR-TKIs, identify independent risk factors for primary resistance, and establish a risk predictive model to provide reference for clinical decision-making. Methods: We collected data from LUAD patients with EGFR-sensitive mutations (19del/21L858R) who were hospitalized in our institution between 2020 and 2022 and received first-generation EGFR-TKIs with follow-up exceeding 6 months. These patients were categorized into primary resistance and sensitive groups based on treatment outcomes. We compared general clinical data, laboratory tests, and tumor-related characteristics between the two groups, analyzed risk factors for primary resistance to EGFR-TKIs, and constructed a risk predictive model. The model's predictive value was comprehensively assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: Serum neuron-specific enolase (NSE) concentration (P=0.03), serum pro-gastrin-releasing peptide (ProGRP) concentration (P=0.01), and Ki67 expression (P<0.001) were identified as independent risk factors for primary resistance to EGFR-TKIs in LUAD. The combined presence of these three risk factors had the highest predictive value [area under the curve (AUC) =0.975, P<0.001]. We constructed a predictive model for the risk of primary resistance to EGFR-TKIs in LUAD patients, incorporating these three parameters, and represented it through a visually interpretable nomogram. The calibration curve of the nomogram demonstrated its strong predictive ability. Further decision curve analysis indicated the model's clinical utility. Conclusions: Based on a single-center retrospective case-control study, we identified serum NSE concentration, ProGRP concentration, and Ki67 expression as independent risk factors for primary resistance to EGFR-TKIs in LUAD patients. We constructed and validated a risk predictive model based on these findings. This predictive model holds promise for clinical application, aiding in the development of personalized treatment strategies and providing a scientific basis for early identification of primary resistance patients.

Construction and validation of a prognostic model for stemness-related genes in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with poor overall prognosis. Early identification of high-risk patients and individualized treatment can help extend the survival time of patients. This study aimed to construct and validate a prognostic prediction least absolute shrinkage and selection operator (LASSO) model for stemness-related genes in LUAD. Methods: Firstly, LUAD RNA-sequencing data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. The tumor stemness index based on mRNA expression (mRNAsi) was calculated, and the relationship between mRNAsi and the survival prognosis as well as clinical features of LUAD patients was analyzed. Then, the weighted gene co-expression network analysis (WGCNA) method was used to screen for gene modules highly correlated with mRNAsi, and functional annotation [Gene Ontology (GO) analysis] and pathway enrichment analysis [Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis] were performed for the selected stemness-related gene module. Furthermore, prognosis-associated genes were determined from the stemness-related genes through univariate Cox analysis, and a prognostic model was constructed using LASSO analysis. Finally, a series of validations including survival curve analysis, receiver operating characteristic (ROC) curve analysis, and risk analysis were conducted for the prognostic model, and nomogram based on the risk model and various clinicopathological features were constructed. Results: LUAD patients with high mRNAsi had a higher mortality rate than those with low mRNAsi. GO analysis showed that stemness-related genes were mainly involved in mRNA processing and extracellular matrix organization, while KEGG analysis revealed their involvement in cell cycle and PI3K-Akt signaling pathways. A prognostic model based on 12 stemness-related genes was constructed using LASSO regression. Validation of the prognostic model demonstrated its good accuracy in predicting the prognosis of LUAD patients. Conclusions: mRNAsi plays an important role in the occurrence and development of LUAD. This study successfully constructed a prognostic prediction LASSO model for stemness-related genes in LUAD, which can serve as a novel prognostic indicator for LUAD and may be an effective complement to the current Tumor Node Metastasis (TNM) clinical staging of LUAD.

Novel Therapies for ANCA-associated Vasculitis: Apilimod Ameliorated Endothelial Cells Injury through TLR4/NF-κB Pathway and NLRP3 Inflammasome.

BACKGROUND: Antineutrophil cytoplasmic antibody-associated vasculitis (AAV) is a rapidly progressive form of glomerulonephritis for which effective therapeutic drugs are currently lacking, and its underlying mechanism remains unclear. AIMS: This study aimed to investigate new treatment options for AAV through a combination of bioinformatics analysis and cell molecular experiments. METHODS: The research utilized integrated bioinformatics analysis to identify genes with differential expression, conduct enrichment analysis, and pinpoint hub genes associated with AAV. Potential therapeutic compounds for AAV were identified using Connectivity Map and molecular docking techniques. In vitro experiments were then carried out to examine the impact and mechanism of apilimod on endothelial cell injury induced by MPO-ANCA-positive IgG. RESULTS: The findings revealed a set of 374 common genes from differentially expressed genes and key modules of WGCNA, which were notably enriched in immune and inflammatory response processes. A proteinprotein interaction network was established, leading to the identification of 10 hub genes, including TYROBP, PTPRC, ITGAM, KIF20A, CD86, CCL20, GAD1, LILRB2, CD8A, and COL5A2. Analysis from Connectivity Map and molecular docking suggested that apilimod could serve as a potential therapeutic cytokine inhibitor for ANCA-GN based on the hub genes. In vitro experiments demonstrated that apilimod could mitigate tight junction disruption, endothelial cell permeability, LDH release, and endothelial activation induced by MPO-ANCA-positive IgG. Additionally, apilimod treatment led to a significant reduction in the expression of proteins involved in the TLR4/NF-κB and NLRP3 inflammasome-mediated pyroptosis pathways. CONCLUSION: This study sheds light on the potential pathogenesis of AAV and highlights the protective role of apilimod in mitigating MPO-ANCA-IgG-induced vascular endothelial cell injury by modulating the TLR4/ NF-kB and NLRP3 inflammasome-mediated pyroptosis pathway. These findings suggest that apilimod may hold promise as a treatment for AAV and warrant further investigation.

Identification of potential biomarkers and therapeutic targets for antineutrophil cytoplasmic antibody-associated glomerulonephritis.

Exploring key genes for antineutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis (ANCA-GN) is of great significance. Through bioinformatics analysis, 79 immune protein-differentially expressed genes (IP-DEGs) were obtained. Six hub genes (PTPRC, CD86, TLR2, IL1B, CSF-1R, and CCL2) were identified and verified to be increased in ANCA-GN patients. Random forest algorithm and ROC analysis showed that CSF-1R was a potential biomarker. Plasma CSF-1R levels increased significantly in ANCA-GN-active patients compared with remission stage and control. Correlation analysis revealed that CSF-1R levels had positive relationship with serum creatinine and Birmingham scoring, while inversely correlated with eGFR. Multivariate analysis revealed that plasma CSF-1R were an independent poor prognostic variable for end-stage renal disease or death, after adjusting for age and gender (HR = 3.05, 95% CI = 1.45-6.43, p = 0.003). Overall, we revealed that the CSF-1R is related to disease activity and might be a vital gene associated with the pathogenesis of ANCA-GN.

A methylation-related signature for predicting prognosis and sensitivity to first-line therapies in gastric cancer.

Background: Methylation modification patterns play a crucial role in human cancer progression, especially in gastrointestinal cancers. We aimed to use methylation regulators to classify patients with gastric adenocarcinoma and build a model to predict prognosis, promoting the application of precision medicine. Methods: We obtained RNA sequencing data and clinical data from The Cancer Genome Atlas (TCGA) database (n=335) and Gene Expression Omnibus (GEO) database (n=865). Unsupervised consensus clustering was used to identify subtypes of gastric adenocarcinoma. We performed functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular feature analysis to determine the clinical application for different subtypes. The univariate Cox regression analysis and the LASSO regression analysis were subsequently used to identify prognosis-related methylation regulators and construct a risk model. Results: Through unsupervised consensus clustering, patients were divided into two subtypes (cluster A and cluster B) with different clinical outcomes. Cluster B included patients with a better prognosis outcome and who were more likely to respond to immunotherapy. We then successfully built a predictive model and found five methylation-related genes (CHAF1A, CPNE8, PHLDA3, SPARC, and EHF) potentially significant to the prognosis of patients. The 1-, 3-, and 5-year areas under the curve of the risk model were 0.712, 0.696, and 0.759, respectively. The risk score was an independent prognostic factor and had the highest concordance index among common clinical indicators. Meanwhile, the tumor microenvironment, sensitivity of chemotherapeutic drugs, molecular features, and oncogenic dedifferentiation differed significantly across the risk groups and subtypes. Conclusions: We classified patients with gastric adenocarcinoma based on methylation regulators, which has positive implications for first-line clinical treatment. The prognostic model could predict the prognosis of patients and help to promote the development of precision medicine.

Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies.

Background: Molecular classification of lung adenocarcinoma (LUAD) based on transcriptomic features has been widely studied. The complementarity of data obtained from multilayer molecular biology could help the LUAD classification via combining multi-omics information. Methods: We successfully divided samples from the The Cancer Genome Atlas (TCGA) (n=437) into four subtypes (CS1, CS2, CS3 and CS4) by 10 comprehensive multi-omics clustering methods in the "movics" R package. Meanwhile, external validation sets from different sequencing technologies proved the robustness of the grouping model. The relationship between subtypes, prognosis, molecular features, tumor microenvironment and response to first-line therapy was further analyzed. Next we used univariate Cox regression analysis and Lasso regression analysis to explore the application of biomarkers in clinical prognosis and constructed a prognostic model. Results: CS1 showed the worst overall survival (OS) among all four clusters, possibly related to its poor immune infiltration, higher tumor mutation and worse chromosomal stability. Patients in different subtypes differed significantly in cancer stem cell characteristics, activation of cancer-related pathways, sensitivity to chemotherapy and immunotherapy. The prognostic model showed good predictive performance. The 1-, 2- and 3-year areas under the curve of risk score were 0.779, 0.742 and 0.678, respectively. Seven genes (DKK1, TSPAN7, ID1, DLGAP5, HHIPL2, CD40 and SEMA3C) used to build the model may be potential therapeutic targets for LUAD. Conclusions: Four LUAD subtypes with different molecular characteristics and clinical implications were identified successfully through bioinformatic analysis. Our results may contribute to precision medicine and inform the development of rational clinical strategies for targeted and immune therapies.

Electron Donor-Acceptor Complex-Initiated Photochemical Cyanation for the Preparation of α-Amino Nitriles.

An electron donor-acceptor complex-initiated α-cyanation of tertiary amines has been described. The reaction protocol provides a novel method to synthesize various α-amino nitriles under mild conditions. The reaction can proceed smoothly without the presence of photocatalysts and transition metal catalysts, and either oxidants are unnecessary or O2 is the only oxidant. The practicality of this method is showcased not only by the late-stage functionalization of natural alkaloid derivatives and pharmaceutical intermediate, but also by the applicability of a stop-flow microtubing reactor.