Search Results (282)

RNA-binding proteins (RBPs), particularly IGF2BP3, play critical but underexplored roles in lung adenocarcinoma (LUAD). This study investigated IGF2BP3′s clinical and functional significance using single-cell/RNA sequencing, validated by qPCR, Western blot, and immunohistochemistry. The results show IGF2BP3 was significantly upregulated in LUAD tissues and associated with advanced-stage, larger tumors, lymph node metastasis, and poor prognosis. A prognostic nomogram confirmed its independent predictive value. Functionally, IGF2BP3 knockdown suppressed proliferation, and induced G2/M arrest and apoptosis. GSEA linked high IGF2BP3 to cell cycle activation and low expression to metabolic pathways. Notably, high IGF2BP3 correlated with immune evasion markers (downregulated CD4+ effector T cells, upregulated Th2 cells), while TIDE analysis suggested a better immunotherapy response in low-expressing patients. Drug screening identified BI-2536 as a potential therapy for low-IGF2BP3 cases, supported by strong molecular docking affinity (−7.55 kcal/mol). These findings establish IGF2BP3 as a key driver of LUAD progression and a promising target for immunotherapy and precision medicine. Full article

Background/Objectives: Brain metastasis (BM) is a common and often early manifestation in lung adenocarcinoma (LUAD), yet its tumor microenvironment remains poorly defined at the time of initial diagnosis. This study aims to characterize early immune microenvironmental alterations in synchronous BM using spatial proteomic profiling. Methods: We performed digital spatial proteomic profiling using the NanoString GeoMx platform on formalin-fixed paraffin-embedded tissues from five treatment-naïve LUAD patients in whom BM was the initial presenting lesion. Paired primary lung and brain metastatic samples were analyzed across tumor and stromal compartments using 68 immune- and tumor-related protein markers. Results: Spatial profiling revealed distinct expression patterns between primary tumors and brain metastases. Immune regulatory proteins—including IDO-1, PD-1, PD-L1, STAT3, PTEN, and CD44—were significantly reduced in brain metastases (p < 0.01), whereas pS6, a marker of activation-induced T-cell death, was significantly upregulated (p < 0.01). These alterations were observed in both tumor and stromal regions, suggesting a more immunosuppressive and apoptotic microenvironment in brain lesions. Conclusions: This study provides one of the first spatially resolved proteomic characterizations of synchronous BM at initial LUAD diagnosis. Our findings highlight early immune escape mechanisms and suggest the need for site-specific immunotherapeutic strategies in patients with brain metastasis. Full article

Background/Objectives: CD318 (also known as CDCP1) is a transmembrane protein that is overexpressed in many cancers and contributes to tumor progression, invasion, and metastasis by activating SRC family kinases through phosphorylation. Emerging evidence also suggests that CD318 plays a role in modulating the tumor immune microenvironment, although its precise mechanism in tumor progression is still not well understood. Methods: To investigate this, we analyzed the expression and immune-related functions of CD318 using the publicly available data from The Cancer Genome Atlas (TCGA) across colorectal adenocarcinoma (COAD), cervical squamous cell carcinoma (CESC), lung adenocarcinoma (LUAD), and pancreatic adenocarcinoma (PAAD). Results: All four cancers exhibited a high level of CD318 expression. Notably, in CESC, LUAD, and PAAD, plasmin-mediated cleavage of CD318 leads to phosphorylation of SRC and protein kinase C delta (PKCδ), which activates HIF1α and/or p38 MAPK. These downstream effectors translocate to the nucleus and promote the transcriptional upregulation of TGFβ1, fostering an immunosuppressive tumor microenvironment through Treg cell recruitment. In contrast, this signaling cascade appears to be absent in COAD. Instead, our analysis indicate that intact CD318 in COAD interacts with the surface receptors CD96 and CD160, which are found on CD8⁺ T cells and NK cells. Conclusions: This interaction enhances cytotoxic immune responses in COAD by promoting CD8⁺ T cell and NK cell activity, offering a possible explanation for the favorable prognosis associated with high CD318 expression in COAD, compared to the poorer outcomes observed in CESC, LUAD, and PAAD. Full article

Background: To improve the prognosis of patients with lung adenocarcinoma (LUAD), revolutionary treatments for metastatic lesions are essential. Methods: To identify genes closely involved in LUAD-cell-derived metastasis, we used RNA sequencing to generate microRNA (miRNA) expression signatures of brain metastatic lesions. Once tumor-suppressive miRNAs are identified, it will be possible to explore the numerous tumor-promoting genes that are regulated by miRNAs. Results: By comparison with a previously created LUAD signature, we identified several miRNAs whose expression was significantly suppressed in brain metastases. We focused on both strands of pre-miR-195 (miR-195-5p and miR-195-3p), which were significantly downregulated in brain metastatic tissues, and confirmed by ectopic expression assays that both strands of pre-miR-195 attenuated the aggressive phenotypes (cell proliferation, migration, and invasion) of LUAD cells. These data suggest that both strands of pre-miR-195 have tumor-suppressive functions in LUAD cells. Next, we explored the target molecules that each miRNA strand regulates in LUAD cells. We identified 159 target genes regulated by miR-195-5p and miR-195-3p, of which 12 genes (ANLN, CDC6, CDCA2, CDK1, CEP55, CHEK1, CLSPN, GINS1, KIF23, MAD2L1, OIP5, and TIMELESS) affect cell cycle/cell division and the prognosis of LUAD patients. Finally, we focused on two genes, ANLN (miR-195-5p target) and MAD2L1 (miR-195-3p target), and demonstrated their oncogenic functions and the molecular pathways they regulate in LUAD cells. Conclusions: The miRNA signature derived from lung cancer brain metastasis will be a landmark in the field, and analysis of this miRNA signature will accelerate the identification of genes involved in lung cancer brain metastasis. Full article

Background/Objectives: The tumor microenvironment (TME) plays a critical role in cancer progression by shaping immune responses and influencing patient outcomes. We hypothesized that the relative proximity of specific immune cell pairs to cancer cells within the TME could help predict their pro- or anti-tumor functions and reflect clinically relevant immune dynamics. Methods: We analyzed imaging mass cytometry (IMC) data from lung adenocarcinoma (LUAD) and triple-negative breast cancer (TNBC) cohorts. For each immune cell pair, we calculated a relative distance (RD) score, which quantifies the spatial difference in proximity to cancer cells. We assessed the prognostic and predictive significance of these RD-scores by comparing them with conventional features such as cell fractions, densities, and individual cell distances. To account for variations in cell abundance, we also derived normalized RD-scores (NRD-scores). Results: RD-scores were more strongly associated with overall patient survival than standard immunological metrics. Among all immune cell pairs, the RD-score comparing the proximity of B cells to that of intermediate monocytes showed the most significant association with improved survival. In TNBC, RD-scores also improved the distinction between responders and non-responders to immunochemotherapy and chemotherapy. Normalized RD-scores reinforced these findings by minimizing the influence of cell density and further highlighting the importance of immune cell spatial relationships. Conclusions: RD-scores offer a spatially informed biomarker that outperforms traditional metrics in predicting survival and treatment response. This approach provides a new perspective on immune cell behavior in the TME and has potential utility in guiding personalized cancer therapies and patient stratification. Full article

Ubiquitination is a dynamic and reversible post-translational modification mediated by ubiquitination regulators (UBRs), which plays an essential role in protein stability, cell differentiation and immunity. Dysregulation of UBRs can lead to destabilization of biological processes and may induce serious human diseases, including cancer. Many UBRs, such as E3 ubiquitin ligases and deubiquitinases (DUBs), have been identified as potential drug targets for cancer therapy. However, the potential clinical value of UBRs in lung adenocarcinoma (LUAD) remains to be elucidated. Here, we identified 17 hub UBRs from high-confidence protein–protein interaction networks of UBRs correlated with cancer hallmark-related pathways using four topological algorithms. The expression of hub UBRs is affected by copy number variation and post-transcriptional regulation, and their high expression is often detrimental to patient survival. Based on the expression profiles of hub UBRs, patients can be classified into two ubiquitination subtypes with different characteristics. These subtypes exhibit significant differences across multiple dimensions, including survival, expression level, mutation burden, female predominance, infiltration level, immune profile, and drug response. In addition, we established a scoring system for evaluating the ubiquitination status of individual LUAD patients, called the ubiquitination-related risk (UB_risk) score, and found that patients with low scores are more likely to gain advantages from immunotherapy. The results of this study emphasize the critical role of ubiquitination in the classification, tumor microenvironment and immunotherapy of LUAD. The construction of the UB_risk scoring system lays a research foundation for evaluating the ubiquitination status of individual LUAD patients and formulating precise treatment strategies from the ubiquitination level. Full article

Background: Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality despite advances in treatments, necessitating more effective therapeutic strategies. Single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to dissect the cellular complexity of cancers, which is often obscured in conventional bulk transcriptomic experiments. Methods: In this study, we performed an integrative analysis of scRNA-seq data from multiple LUAD patient cohorts to investigate cell-type-specific transcriptomic changes across disease stages. Clustering, lineage trajectory analysis, and transcriptional regulatory network reconstruction were employed to identify stage-specific gene markers and their upstream regulators. Additionally, we constructed intercellular communication networks to evaluate signaling changes within the tumor microenvironment (TME) during LUAD progression. Results: Our analysis revealed that epithelial cells from stage IV tumors exhibited a distinct transcriptional profile compared to earlier stages, a separation not observed in immune or stromal cell populations. We identified a panel of gene markers that differentiated epithelial cells across disease stages and effectively stratified patients into subgroups with distinct survival outcomes and TME compositions. Regulatory network analysis uncovered key transcription factors, including ATF3, ATF4, HSF1, KLF4, and NFIC, as potential upstream regulators of these stage-specific genes. Moreover, cell–cell communication analysis revealed a significant increase in signaling originating from epithelial cells and a concomitant decrease in immune-derived signals in late-stage LUAD. We identified several signaling pathways enriched in stage-specific crosstalk, including Wnt, PTN, and PDGF pathways, which may play critical roles in LUAD progression. Conclusions: This study provides a comprehensive single-cell resolution map of LUAD progression, highlighting epithelial-driven regulatory programs and dynamic intercellular communication within the TME. Our findings uncover novel molecular markers and regulatory mechanisms with potential prognostic and therapeutic value for more precise treatment. Full article

Background: Zinc finger proteins (ZNFs), functioning as pervasive transcriptional modulators, serve as pivotal mediators of tumorigenesis and malignant advancement. However, the mechanistic contributions of these epigenetic orchestrators to lung adenocarcinoma pathogenesis remain incompletely characterized. Methods: To elucidate zinc finger proteins’ biological significance in lung adenocarcinoma (LUAD) pathogenesis, we first extracted relevant transcriptional data from TCGA. After preliminary screening with univariate Cox regression, a LASSO algorithm was applied to optimize the risk score model, incorporating key zinc finger protein markers. For independent validation, we accessed GEO dataset GSE68465, applying identical analytical protocols to confirm model generalizability. We performed multivariable Cox regression to identify independent predictors of clinical outcomes after adjusting for confounding variables. Cell-based validation included (1) comparative analysis of zinc finger protein expression across LUAD/normal cell models and (2) technical verification using standardized qRT-PCR protocols. Results: Following rigorous bioinformatics screening comprising differential expression and survival analysis, the final 21-zinc finger protein cohort was selected for risk score algorithm development aimed at clinical outcome prediction. Stratification based on computed risk scores revealed markedly superior survival outcomes in the low-risk cohort compared to high-risk patients. Comparative analysis revealed overall concordance in the transcriptional profiles of eight ZNFs (|coef| > 0.1) across experimental cell systems and TCGA datasets. Conclusions: Collectively, the prognostic framework incorporating zinc finger proteins demonstrates biomarker utility in lung adenocarcinoma survival prediction, while offering novel avenues for molecular target discovery in therapeutic strategies against this malignancy. Full article

Lung adenocarcinoma (LUAD) is characterized by substantial genetic heterogeneity, making it challenging to identify reliable biomarkers for diagnosis and treatment. Tumor mutational burden (TMB) is widely recognized as a predictive biomarker due to its association with immune response and treatment efficacy. In this study, we take a different approach by treating TMB as a response variable to uncover its genetic drivers using multiomics data. We conducted a thorough evaluation of recent feature selection methods through extensive simulations and identified three top-performing approaches: projection correlation screening (PC-Screen), distance correlation sure independence screening (DC-SIS), and Wasserstein distance-based screening (WD-Screen). Unlike traditional approaches that rely on simple statistical tests or dataset splitting for validation, we adopt a method-based validation strategy, selecting top-ranked features from each method and identifying consistently selected genes across all three. Using The Cancer Genome Atlas (TCGA) dataset, we integrated copy number alteration (CNA), mRNA expression, and DNA methylation data as predictors and applied our selected methods. In the two-platform analysis (mRNA + CNA), we identified 13 key genes, including both previously reported LUAD-associated genes (CCNG1, CKAP2L, HSD17B4, SHROOM1, TIGD6, and TMEM173) and novel candidates (DTWD2, FLJ33630, NME5, NUDT12, PCBD2, REEP5, and SLC22A5). Expanding to a three-platform analysis (mRNA + CNA + methylation) further refined our findings, with PCBD2 and TMEM173 emerging as the robust candidates. These results highlight the complexity of multiomics integration and the need for advanced feature selection techniques to uncover biologically meaningful patterns. Our multiomics strategy and robust selection approach provide insights into the genetic determinants of TMB, offering potential biomarkers for targeted LUAD therapies and demonstrating the power of Wasserstein distance-based feature selection in complex genomic analysis. Full article

Lactylation and PANoptosis are emerging modes of tumor progression regulation; however, their interplay and effect on the prognosis for lung adenocarcinoma (LUAD) remain unclear. This research analyzed both bulk and single-cell transcriptomic profiles of LUAD and identified 506 potential markers related to lactylation and PANoptosis. Employing 117 machine learning approaches and 5 LUAD datasets, lactylation and PANoptosis-related signatures (LAPRS) and further predictive nomograms were constructed with 85 prognostic genes. The performance of LAPRS was validated with multifaceted validation, including Kaplan–Meier analysis, time-dependent ROC curves and comparison with 55 existing LUAD models. LAPRS enabled the stratification of LUAD patients into high- and low-risk subgroups. Through additional investigation, high-risk individuals showed elevated genomic alterations, reduced immune infiltration, and poorer immunotherapy response, while low-risk individuals showed better drug sensitivity and a higher tumor mutation burden. Further analysis via 18 models and experimental validation revealed APOL1 as a poor prognostic factor, potentially interacting with the lactylation-related gene VIM through TNF signaling. This research clarifies the mechanistic roles of lactylation and PANoptosis in LUAD and proposes APOL1 as a novel prognostic marker, offering insights for therapeutic stratification. Full article

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer and is frequently diagnosed at advanced stages with metastasis, contributing to its poor prognosis. Identifying key metastasis-related biomarkers is critical for improving early diagnosis and therapeutic targeting. Methods: We analyzed four GEO microarray datasets (GSE32863, GSE27262, GSE40275, and GSE33356) and TCGA data to identify differentially expressed genes (DEGs) in LUAD. Functional enrichment of DEGs was analyzed using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and a Cancer Hallmark Enrichment Plot. Hub gene analysis was conducted using Cytoscape. Hub genes were evaluated for their expression, prognostic significance (via the Kaplan–Meier plotter), and clinical correlation using additional platforms (TCGA, Lung Cancer Explorer, TNMplot, and the Human Protein Atlas). Results: A total of 333 consistently dysregulated DEGs were identified, enriched in pathways related to metastasis, including angiogenesis, immune escape, and ECM interaction. Ten hub genes (AURKA, TOP2A, CCNB2, CENPF, MCM4, TPX2, KIF20A, ASPM, MELK, and NEK2) were identified through network analysis. Among these, MCM4 showed strong upregulation in LUAD and was significantly associated with poor overall survival. Notably, MCM4 expression also correlated with post-progression survival and markers of invasiveness. Immunohistochemistry and transcriptomic analyses confirmed MCM4 overexpression at both mRNA and protein levels. Additionally, MCM4 expression was positively correlated with various matrix metalloproteinases, supporting its role in promoting tumor invasiveness. Conclusions: MCM4 is a novel potential biomarker for LUAD metastasis and prognosis. Its consistent upregulation, association with metastatic markers, and clinical significance suggest it may serve as a candidate target for diagnostic use or therapeutic intervention in advanced LUAD. Full article

The coronavirus pathogenesis pathway, which consists of severe acute respiratory syndrome (SARS) coronavirus infection and signaling pathways, including the interferon pathway, the transforming growth factor beta pathway, the mitogen-activated protein kinase pathway, the apoptosis pathway, and the inflammation pathway, is activated upon coronaviral infection. An artificial intelligence approach based on machine learning was utilized to develop models with images of the coronavirus pathogenesis pathway to predict the activation states. Data on coronaviral infection held in a database were analyzed with Ingenuity Pathway Analysis (IPA), a network pathway analysis tool. Data related to SARS coronavirus 2 (SARS-CoV-2) were extracted from more than 100,000 analyses and datasets in the IPA database. A total of 27 analyses, including nine analyses of SARS-CoV-2-infected human-induced pluripotent stem cells (iPSCs) and iPSC-derived cardiomyocytes and fibroblasts, and a total of 22 analyses of SARS-CoV-2-infected lung adenocarcinoma (LUAD), were identified as being related to “human” and “SARS coronavirus 2” in the database. The coronavirus pathogenesis pathway was activated in SARS-CoV-2-infected iPSC-derived cells and LUAD cells. A prediction model was developed in Python 3.11 using images of the coronavirus pathogenesis pathway under different conditions. The prediction model of activation states of the coronavirus pathogenesis pathway may aid in treatment identification. Full article

Background: Organoid cultures have received much attention in recent years due to the promise of patient-derived organoid cultures for exploration of personalized cancer treatment strategies. Organoid cultures have been established from a variety of malignancies; however, lack of a thorough histopathological analysis has limited the acceptance of organoid models as translational tools. Methods: Here, we aimed to establish patient-derived tumor-organoid (PDTO) models from human non-small-cell lung cancer (NSCLC) resection specimens and provide a thorough histopathological evaluation of the cultures. Results: We show that we were able to establish organoid cultures of lung adenocarcinomas (LUADs) and lung squamous cell carcinomas (LUSCs) successfully, and that the organoid cultures of different subtypes of NSCLC preserved the histoarchitecture and growth pattern of the tumors they derive from. Immunohistochemistry and AB-PAS staining confirmed the subtype-specific protein expression pattern and preserved mucin production in LUAD organoids. The genetic abnormalities of the tumors assessed by immunohistochemistry (IHC-P) were preserved in the organoid cultures. Conclusions: Our thorough study reveals conserved PDTO histopathology, supports further exploration, and encourages using PDTO models in translational research projects. PDTO models hold remarkable promise as patient-specific models and may be applied to predict therapy response in cases where molecular–pathological analyses pose significant management dilemmas, and they also may provide a platform for exploring the molecular mechanisms of therapy resistance in a biologically relevant model system. Full article

Lung cancer is responsible for 2.21 million annual cancer cases and is the leading worldwide cause of cancer-related deaths. Specifically, lung adenocarcinoma (LUAD) is the most prevalent lung cancer subtype resulting from genetic causes; LUAD has a 15% patient survival rate due to it commonly being detected in its advanced stages. This study aimed to identify a novel biomarker signature of early-stage LUAD utilizing gene expression analysis of human lung tissue samples. Using 22 pairs of LUAD and matched normal lung microarrays, 229 differentially expressed genes were identified. These genes were networked for their protein–protein interactions, and 44 hub genes were determined from protein essentiality. Survival analysis of 478 LUAD patient samples identified four statistically significant candidates. These candidate genes’ expression profiles were validated from GTEx and TCGA (347 normal, 483 LUAD samples); immunohistochemistry validated the subsequent protein presence. Through intensive bioinformatic identification and multiple validations of the four-biomarker gene signature, AGER, MGP, and PECAM1 were identified as downregulated in LUAD; SLC2A1 was identified as upregulated in LUAD. These four biologically significant genes are involved in tumorigenesis and poor LUAD prognosis, meriting their use as a clinical biomarker signature and therapeutic targets for early-stage LUAD. Full article

Background: A micropapillary pattern (MP) and solid pattern (SP) in lung adenocarcinoma (LUAD), a major subtype of non-small-cell lung cancer (NSCLC), are associated with a poor prognosis and necessitate accurate preoperative identification. This study aimed to develop and validate a predictive model combining clinical and radiomics features for differentiating a high-risk MP/SP in LUAD. Methods: This retrospective study analyzed 180 surgically confirmed NSCLC patients (Stages I–IIIA), randomly divided into training (70%, n = 126) and validation (30%, n = 54) cohorts. Three prediction models were constructed: (1) a clinical model based on independent clinical and CT morphological features (e.g., nodule size, lobulation, spiculation, pleural indentation, and vascular abnormalities), (2) a radiomics model utilizing LASSO-selected features extracted using 3D Slicer, and (3) a comprehensive model integrating both clinical and radiomics data. Results: The clinical model yielded AUCs of 0.7975 (training) and 0.8462 (validation). The radiomics model showed superior performance with AUCs of 0.8896 and 0.8901, respectively. The comprehensive model achieved the highest diagnostic accuracy, with training and validation AUCs of 0.9186 and 0.9396, respectively (DeLong test, p < 0.05). Decision curve analysis demonstrated the enhanced clinical utility of the combined approach. Conclusions: Integrating clinical and radiomics features significantly improves the preoperative identification of aggressive NSCLC patterns. The comprehensive model offers a promising tool for guiding surgical and adjuvant therapy decisions. Full article