Background: Lung cancer has the highest mortality rate among all cancers worldwide. Alkaliptosis is characterized by a pH-dependent form of regulated cell death. In this study, we constructed a model related to alkaliptosis-associated long non-coding RNAs (lncRNAs) and developed a prognosis-related framework, followed by the identification of potential therapeutic drugs.Methods: The TCGA database was utilized to obtain RNA-seq-based transcriptome profiling data, clinical information, and mutation data. We conducted multivariate Cox regression analysis to identify alkaliptosis-related lncRNAs. Subsequently, we employed the training group to construct the prognostic model and utilized the testing group to validate the model’s accuracy. Calibration curves were generated to illustrate the discrepancies between predicted and observed outcomes. Principal Component Analysis (PCA) was performed to investigate the distribution of LUAD patients across high- and low-risk groups. Additionally, Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were conducted. Immune cell infiltration and Tumor Mutational Burden (TMB) analyses were carried out using the CIBERSORT and maftools algorithms. Finally, the “oncoPredict” package was employed to predict immunotherapy sensitivity and to further forecast potential anti-tumor immune drugs. qPCR was used for experimental verification.Results: We identified 155 alkaliptosis-related lncRNAs and determined that 5 of these lncRNAs serve as independent prognostic factors. The progression-free survival (PFS) and overall survival (OS) rates of the low-risk group were significantly higher than those of the high-risk group. The risk signature functions as a prognostic factor that is independent of other variables. Different stages (I–II and III–IV) effectively predict the survival rates of lung adenocarcinoma (LUAD) patients, and these lncRNAs can reliably forecast these signatures. GSEA revealed that processes related to chromosome segregation and immune response activation were significantly enriched in both the high- and low-risk groups. The high-risk group exhibited a lower fraction of plasma cells and a higher proportion of activated CD4 memory T cells. Additionally, the OS of the low TMB group was significantly lower compared to the high TMB group. Furthermore, drug sensitivity was significantly greater in the high-risk group than in the low-risk group. These lncRNAs may serve as biomarkers for treating LUAD patients.Conclusion: In summary, the construction of an alkaliptosis-related lncRNA prognostic model and drug sensitivity analysis in LUAD patients provides new insights into the clinical diagnosis and treatment of advanced LUAD patients.

Surface-Enhanced Raman Scattering technology has shown broad application potential in tumor diagnosis due to its excellent sensitivity and specificity. With the increasing demand for early tumor detection and precision medicine, SERS has been widely applied in the detection of tumor markers, particularly in liquid biopsy. This review systematically summarizes the recent advances of SERS in detecting key liquid biopsy biomarkers (exosomes, CTCs, ctDNA), critically analyzes its advantages in sensitivity and multiplexing, and discusses the main challenges for clinical translation, including signal interference in complex biological environments and the need for improved quantitative accuracy. Future perspectives integrating AI and multimodal strategies are also outlined to guide the development of precision oncology tools.

Background: The gut microbiome is closely associated with malignant tumors; however the specific mechanisms by which it contributes to the development of lung adenocarcinoma remain unclear. In this study, we performed a two-sample bidirectional Mendelian randomization (MR) analysis to assess the causal relationship between the gut microbiome and lung adenocarcinoma. By identifying single nucleotide polymorphism markers linked to gut microbiome species, we aimed to discover potential biomarkers for lung adenocarcinoma. These findings may offer new insights into the role of the gut microbiome in the prevention and treatment of lung adenocarcinoma. Methods: We used genome-wide association study (GWAS) summary statistics to assess the association between the gut microbiome and lung adenocarcinoma through two-sample MR analysis. Sensitivity analyses were performed to confirm the robustness of the findings. Reverse MR analysis and GWAS data integration were employed to identify potential genetic and therapeutic targets. Bioinformatics analysis and quantitative Real-Time PCR (qRT-PCR) were utilized to validate gene expression and explore the underlying mechanisms of key genes. Results: Our analysis identified two bacterial taxa, Prevotella9 and Parabacteroides, as being causally associated with lung adenocarcinoma, both showing positive causal relationships. Sensitivity analyses confirmed the robustness of these associations. The reverse MR analysis revealed no evidence of reverse causality. GWAS data identified 15 genes (DNAH1, PDE10A, DOCK2, INSYN2B, DNAI3, SUOX, LINC01505, SULT4A1, NT5ELP, LINC02895, calcium/calmodulin dependent protein kinase 1D (CAMK1D), ENSG00000253557, BCAS3, C18orf63, MYO18B) that passed the summary-data-based MR test. The transcriptomic data revealed that five genes (CAMK1D, BCAS3, DNAH1, PDE10A, and C18orf63) were differentially expressed between lung adenocarcinoma patients and healthy individuals. Through qRT-PCR validation, the CAMK1D gene was markedly upregulated in lung adenocarcinoma cell lines, whereas BCAS3, DNAH1, PDE10A, and C18orf63 genes exhibit ed substantially reduced expression. Conclusion: Our study identified specific gut microbial taxa as risk factors for lung adenocarcinoma and proposes CAMK1D as a microbiota-related candidate biomarker and potential therapeutic target that may inform personalized treatment and drug development strategies in the future.

Lung cancer is the cancer with the highest mortality rate worldwide. PANoptosis is characterized by inflammatory lytic cell death facilitated by caspases and RIPKs. We determined the construction of PANoptosis-related lncRNA, constructed a prognosis-related model, and further screened potential therapeutic drugs. The TCGA database was used to obtain the RNA-seq-based transcriptome profiling data, clinical information, and mutation data. We used multivariable Cox regression analysis to obtain PANoptosis-related lncRNAs. We then used the training group to build the prognostic model and used the testing group to verify the accuracy of the model. Calibration curves showed the difference between the predicted and observed outcomes. PCA analysis was used to explore the distribution of LUAD patients with high- and low-risk groups. The GO and GSEA enrichment analyses were performed. Immune cell infiltration and TMB analysis were performed using CIBERSORT and maftools algorithm. The TIDE algorithm was used to predict immunotherapy sensitivity and further predicted anti-tumor immune drugs. qPCR was used for experimental verification. We identified 163 PANoptosis-related lncRNAs and identified 6 lncRNAs as independent prognostic factors. The PFS and OS of the low-risk group were significantly higher than those of the high-risk group. The risk signature is a prognostic factor, independent of other factors. Different stages (stages I-II and III-IV) could well predict the survival rate of LUAD patients and these lncRNAs can reliably stratify patient prognosis. GSEA analysis showed that chromosome segregation and activation of immune response were significantly enriched in the high- and low-risk groups. The high-risk group showed a lower fraction of T cells CD4 memory resting and a higher proportion of NK cells resting. The OS of the low TMB group was significantly lower than the high TMB group. Furthermore, the drug sensitivity of the high-risk group is significantly higher than the low-risk group. And the high-risk lncRNAs may serve as therapeutic targets. In summary, the 6 PANoptosis-related lncRNAs can well predict the prognosis of LUAD patients, which may provide new insight for survival prediction and clinical immunotherapy of LUAD patients.

Background: Lung cancer continues to maintain a high mortality rate, with lung adenocarcinoma (LUAD) being its most common type. As an emerging environmental pollutant, 6PPD-quinone is attracting increasing attention. Through network toxicology, we constructed a prognostic model and validated it with molecular docking and dynamics simulations, revealing its underlying molecular mechanisms. Methods: RNA-seq and clinical data of LUAD were obtained from the TCGA database, and differential expression analysis was performed using DESeq2. Putative targets of 6PPD-quinone were retrieved from DIGEP-Pred 2.0 and SEA databases, while LUAD-associated genes were collected from GeneCards, CTD, and OMIM. Overlapping genes with DEGs were identified as potential targets. Protein–protein interaction networks were constructed via STRING and visualized with Cytoscape. The hub genes were screened using four algorithms (Degree, EPC, MCC, MNC). GO and KEGG enrichment analyses were conducted on both potential and hub genes. A prognostic model was built based on Cox regression, with risk scores calculated for survival and independent prognostic analyses. SHAP analysis was applied to interpret feature contributions. Molecular docking and molecular dynamics simulations assessed binding stability between 6PPD-quinone and key genes, and qPCR validated expression changes in BEAS-2B and A549 cells with or without 6PPD-quinone exposure. Results: 6PPD-quinone-linked LUAD were used to identify 31 potential targets and screen 18 network hub genes. Through a seven-gene prognostic signature (GPI, FAM83D, KRT17, CTLA4, MAP3K8, SIX1, ABCC2) construction, clear stratification into high- and low-risk groups was achieved with significantly different survival rates (1-year AUC = 0.711). SHAP analysis highlighted GPI, FAM83D, KRT17, and ABCC2 as major pathogenic contributors. Molecular docking demonstrated stable binding between 6PPD-quinone and GPI, FAM83D, and ABCC2 (binding energies < −5 kcal/mol). Furthermore, MD simulations confirmed the stability of the ABCC2-ligand complex. qPCR validation confirmed that exposure to 6PPD-quinone markedly enhanced the expression of these genes in A549 cells. Conclusion: This study identifies GPI, FAM83D, and ABCC2 as potential pathogenic genes linking 6PPD-quinone exposure to LUAD, possibly through genetic dysregulation. The integrative network toxicology and SHAP-based approach provide a robust framework for uncovering environmental carcinogenesis mechanisms, offering new insights into the health risks of tire-derived pollutants.

Parasitic eosinophilic lung disease is characterized by eosinophil infiltration and dysregulated immune responses, posing significant challenges due to its complex immunoregulatory mechanisms which remain incompletely understood. This review explores the emerging role of IgE immune complexes (IgE ICs) in modulating eosinophilic immune responses, proposing a hypothetical signaling axis involving the NLRC4 inflammasome that may influence disease progression. We synthesize preliminary evidence from cellular models and discuss the potential implications of this pathway for parasitic eosinophilic lung disease. Integrating recent findings, we explore how IgE ICs interact with Toll-like receptor 2 (TLR2) and Fc epsilon receptor II (FcϵRII) to regulate eosinophil inflammatory responses, degranulation, and the expression of associated proteins. These interactions reveal a dual regulatory function of immune complexes in parasitic infections and related pulmonary disorders. By critically examining the available evidence for crosstalk between IgE ICs and the NLRC4 inflammasome, this review aims to provide a conceptual framework and hypothesis-generating perspectives for developing targeted immunotherapeutic strategies for parasitic eosinophilic lung disease. Importantly, we acknowledge that the mechanistic evidence for this axis is primarily derived from in vitro studies and requires validation in physiologically relevant systems.