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PubMed2026

PYCR1 promotes glutamine metabolism and the progression of lung adenocarcinoma by regulating the expression of OPLAH.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Glutamine plays a critical role in the progression of LUAD. However, the function of pyrroline-5-carboxylate reductase 1 (PYCR1) and its regulatory role in glutamine metabolism remain unclear. Transcriptomic and clinical data for LUAD were obtained from The Cancer Genome Atlas (TCGA) and validated using Gene Expression Omnibus (GEO) datasets (GSE19188, GSE13213). Glutamine metabolism-related genes were analyzed for differential expression and prognostic significance. Functional enrichment was performed via gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses. Single-cell RNA-seq data (GSE117570) were processed using Seurat, and cell-cell communication was inferred with CellChat. In vitro, lentiviral overexpression, Western blotting, EdU, CCK-8, and glutamine uptake assays were conducted. An orthotopic xenograft model was established in nude mice to assess tumor growth in vivo. Six glutamine-metabolism-related genes were found significantly overexpressed in LUAD tissues and associated with poor overall survival. Single-cell sequencing revealed predominant PYCR1 expression in malignant cells. Functional assays demonstrated that PYCR1 overexpression enhanced glutamine uptake, proliferation, and inhibited apoptosis in LUAD cells, effects mediated via suppression of the P53 pathway. PYCR1 promoted tumor growth in a xenograft model and was found to transcriptionally upregulate 5-oxoprolinase (OPLAH), which augmented its oncogenic effects. Our findings identify the PYCR1/OPLAH axis as a key driver of LUAD progression via p53 signaling, revealing a promising therapeutic target.

Pyrroline Carboxylate Reductases↗

PubMed2025

Cancer-associated fibroblast-derived SOD3 enhances lymphangiogenesis to drive metastasis in lung adenocarcinoma.

Despite advancements in diagnostic and therapeutic strategies, lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality due to its aggressive metastatic potential. Extracellular superoxide dismutase (SOD3) is an antioxidant enzyme that regulates oxidative stress and is regarded as a tumor suppressor. However, studies have demonstrated that SOD3 can either promote or inhibit cell proliferation and survival in various cancers, and its molecular mechanisms within the tumor microenvironment are poorly understood. In this study, we report a breakthrough in uncovering the role of SOD3 derived from cancer-associated fibroblasts (CAFs) in LUAD. Using LUAD xenograft models co-implanted with SOD3-overexpressing CAFs (CAFSOD3), we observe an aggressive tumor phenotype characterized by increased lymphangiogenesis and lymphatic vessel invasion (LVI) of the tumor. Additionally, LUAD patients with elevated SOD3 levels exhibit a higher incidence of LVI and metastasis. Notably, RNA sequencing of CAFSOD3 reveals that SOD3-mediated VEGF-dependent tumor progression and lymphangiogenesis are up-regulated. Furthermore, single-cell transcriptomic analysis of LUAD clinical samples confirms a strong correlation between SOD3 expression in fibroblasts and characteristics of tumor exacerbation, such as lymphangiogenesis and metastasis. These findings underscore new insights into the role of CAF-derived SOD3 in LUAD progression and highlight its potential as a biomarker and therapeutic target.

Lymphangiogenesis↗

PubMed2025

Distinct Clinicogenomic Features and Immunotherapy Associations in Pulmonary Sarcomatoid Carcinoma: A Multicenter Retrospective Study.

INTRODUCTION: Pulmonary sarcomatoid carcinoma (PSC) is a rare NSCLC subtype with poor prognosis. Outcomes to immune checkpoint inhibitors (ICIs) and genomic features in PSC remain underexplored compared with other NSCLC subtypes. METHODS: Patients from three institutions and the National Cancer Database (NCDB) with metastatic NSCLC treated with ICI alone or with chemotherapy were identified. Clinicogenomics and treatment outcomes were compared across PSC, lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC). RESULTS: We analyzed 4841 patients including 165 PSC cases treated with ICI-based therapy from three institutions and 201 PSC from NCDB. In MDACC, 65 (4.3%) were PSC, 1138 (75.1%) LUAD, and 312 (20.6%) LUSC. Patients with PSC were older and more likely to present with metastatic disease. In both the MDACC and NCDB cohorts, ICIs resulted in better outcomes for patients with PSC compared with chemotherapy. In these patients, there was no difference in outcome between ICI-monotherapy and ICI-chemotherapy. Across the three institutional cohorts, 37% to 43% of patients with PSC who received ICIs were responders, compared with 26% to 29% in LUAD and 22% to 46% in LUSC (p < 0.05). Improved ICI outcomes in PSC appeared driven by high PD-L1 (≥50% in 73%-77% cases). Among patients with high PD-L1, response rates were similar across histologic subtypes. Conversely, TMB was similar in PSC compared with LUAD or LUSC and was not associated with ICI outcomes. Across cohorts, PSC tumors were enriched for TP53, NF1, NF2, and NRAS, with relative depletion of STK11 and KEAP1 compared with LUAD. Case observation revealed relatively better outcomes to ICI than targeted therapies in patients with PSC with MET exon 14 skipping or KRAS G12C. CONCLUSION: PSC exhibits improved outcomes to ICI relative to other therapies, potentially driven by high PD-L1 expression. Genomic analysis highlights a distinct genomic landscape of PSC when compared with LUAD.

Humans↗

PubMed2025

Implications of EGFR expression on EGFR signaling dependency and adaptive immunity against EGFR-mutated lung adenocarcinoma.

BACKGROUND: In EGFR-mutated lung adenocarcinoma (EGFRm LUAD), EGFR mutations do not necessarily result in increased EGFR expression (EGFR-exp), which differs among patients. However, the factors influencing EGFR-exp and the impact of EGFR-exp on tumor characteristics in patients with EGFRm LUAD remain unclear. PATIENTS AND METHODS: Whole-exome and RNA sequencing were performed for patients with early- and advanced-stage EGFRm LUAD. The patients were classified into low or high EGFR-exp groups based on the median transcripts per million. We retrospectively examined the association between EGFR-exp, genomic characteristics, downstream EGFR signaling activity, tumor microenvironment (TME) status, and clinical outcomes. RESULTS: This study included 450 and 45 patients in the early- and advanced-stage cohorts, respectively. In both cohorts, the EGFR-exp low group exhibited a lower incidence of TP53 co-mutations and EGFR amplification and a higher incidence of EGFR subclonal mutations than the EGFR-exp high group. Furthermore, downstream EGFR signaling pathways, such as the MAPK signaling, were less activated in the EGFR-exp low group. However, this group showed significantly enriched adaptive immune response pathways (Q < 0.0001) and an immune-inflamed TME. Additionally, a low EGFR-exp was a significantly favorable factor for postoperative relapse (odds ratio [OR], 0.6; P = 0.04). However, in the advanced-stage cohort, a low EGFR-exp was a significant risk factor for non-responders to osimertinib (OR, 17.5; P = 0.03). CONCLUSIONS: In EGFRm LUAD, significant associations were observed between EGFR-exp levels and both EGFR signaling pathways and adaptive immune status, which in turn influence clinical outcomes. This large-scale multi-omics analysis highlights the heterogeneity among patients with EGFRm LUAD and emphasizes the need to assess EGFR-exp levels alongside mutation status for optimal treatment strategies in EGFRm LUAD.

Humans↗

PubMed2020

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

BACKGROUND: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. METHOD: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). RESULTS: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. CONCLUSION: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Adenocarcinoma of Lung↗

PubMed2026

Molecular differences between poorly and well/moderately differentiated lung adenocarcinoma and their clinical implications.

BACKGROUND: Diagnostic and therapeutic techniques for lung adenocarcinoma (LUAD) have advanced rapidly. However, the morphology-based assessment of tumor differentiation commonly used in clinical practice has several limitations, including strong subjectivity, inability to reflect tumor heterogeneity, and limited prognostic predictive value. This study aimed to identify key genetic mutations associated with tumor differentiation features and explored their potential clinical impact of these molecular features on tumor prognosis and therapeutic response. METHODS: In this study, 196 LUAD tissue samples collected from Fujian Cancer Hospital between 2021 and 2023 were analyzed using integrated high-throughput sequencing and comprehensive bioinformatics approaches. Molecular differences between poorly differentiated tumors and moderately/well-differentiated tumors were characterized. The effects of these molecular alterations on tumor behavior and therapeutic response were examined, with the aim of exploring biomarkers associated with poor prognosis and treatment in LUAD. RESULTS: Our findings showed a significant quantitative difference in tumor mutation burden, EGFR co-mutations, patterns of co-occurrence resulting in distinct clinical outcomes. Among these alterations, mutations in LRP1B and TP53, as well as EGFR amplification, MET amplification, JAK2 deletion and CDKN2B deletion were significantly enriched in the poorly differentiated group, whereas EGFR mutations were significantly enriched in the moderately/well-differentiated group. We also identified MET amplification and LRP1B mutation as independent poor prognostic factors in LUAD. Moreover, a subset of poorly differentiated group exhibited DNA double-strand breaks possibly due to homologous recombination deficiency (HRD), along with frequent alterations of immune evasion-related genes. CONCLUSIONS: These findings provide novel insights into the molecular basis of LUAD and the development of novel targeted differentiation-related therapies and precision genome-guided treatments.

Lung adenocarcinoma (LUAD)↗

PubMed2026

Development and internal validation of a six-gene prognostic model based on galactose metabolism for overall survival in lung adenocarcinoma.

BACKGROUND: Lung cancer remains a leading cause of cancer incidence and mortality globally. Metabolic reprogramming promotes tumor progression and shapes an immunosuppressive tumor microenvironment. Galactose metabolism is involved in multiple malignancies, but its prognostic value in lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop and internally validate a galactose metabolism-related multigene prognostic model for LUAD. METHODS: A retrospective prognostic model development and internal validation study was performed using RNA sequencing (RNA-seq) and clinical data from 585 LUAD patients in The Cancer Genome Atlas (TCGA). Differential expression, functional enrichment, univariate and multivariate Cox regression were applied to construct a prognostic gene signature. Internal validation was performed using bootstrap resampling. Model performance was evaluated by time-dependent receiver operating characteristic (ROC), C-index, calibration, and Kaplan-Meier analysis. Associations between the model and immune infiltration, immunotherapy responsiveness, and tumor stemness were also analyzed. RESULTS: A six-gene prognostic model (GALT, GANC, PGM1, GALM, B4GALT1, PGM2) was developed. The model showed good discrimination with 1-, 3-, and 5-year area under the curve (AUC) values of 0.719, 0.693, and 0.684, respectively. The low-risk group exhibited significantly longer survival, increased antitumor immune infiltration (CD8+ T cells, M1 macrophages, activated CD4+ memory T cells), higher expression of T cell proliferation-related genes, lower immune checkpoint expression, better predicted immunotherapy response, and lower tumor stemness compared with the high-risk group. CONCLUSIONS: We developed and internally validated a six-gene prognostic model for LUAD based on galactose metabolism. The model shows moderate prognostic performance and is associated with antitumor immunity and tumor stemness. It may be used for prognostic risk stratification and to guide personalized immunotherapy in LUAD.

Galactose metabolism↗

PubMed2026

Stratifying lung adenocarcinoma: a novel prognostic model based on mitochondrial outer membrane permeabilization activity.

UNLABELLED: Mitochondrial outer membrane permeabilization (MOMP) is a core apoptotic regulatory event that dictates mitochondrial integrity, where full activation drives cell death and sublethal dysregulation contributes to tumor genomic instability. We used the Cancer Genome Atlas lung adenocarcinoma cohort (TCGA-LUAD) as the training cohort and the Gene Expression Omnibus dataset GSE42127 as the validation cohort to identify prognostic genes related to MOMP activity in lung adenocarcinoma (LUAD) and to evaluate their potential biological significance. By intersecting MOMP-related genes with differentially expressed genes, combined with survival analysis, Mendelian randomization analysis, and 101 machine-learning algorithm combinations, seven prognostic genes, namely BIRC5, PSMD11, TNFRSF13C, YWHAZ, YWHAG, CYCS, and LTB, were identified. Next, an optimal prognostic model was constructed based on the gradient boosting machine (GBM) algorithm. Based on the risk score, LUAD patients were stratified into high- and low-risk groups, and patients in the high-risk group exhibited poorer overall survival in both the training and validation cohorts. Furthermore, a nomogram integrating the risk score and clinicopathological factors was developed and showed favorable predictive performance for 1-, 3-, and 5-year survival. Meanwhile, functional and immune analyses revealed that the high-risk group was enriched in DNA replication-related pathways and demonstrated a higher tumor mutation burden (TMB). Correlation analysis indicated that TNFRSF13C was positively correlated with activated B cells, whereas BIRC5 was negatively correlated with eosinophils, suggesting that MOMP-related genes might be involved in remodeling the immune microenvironment of LUAD. Drug sensitivity analysis showed differences in predicted half-maximal inhibitory concentration (IC50) values between the risk groups, suggesting the potential value of this model in assisting therapeutic stratification. Single-cell RNA sequencing (scRNA-seq) further identified T lymphocytes as a key cell type, with numerous prognostic genes exhibiting differential expression in T cells or dynamic changes during differentiation. We suggest that the MOMP-related signature established in this study may provide a reference for prognostic stratification in LUAD and offers candidate prognostic genes for subsequent experimental and clinical validation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13205-026-05058-6.

Lung adenocarcinoma↗

PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗

PubMed2025

Stratifying Lung Adenocarcinoma Risk with Multi-ancestry Polygenic Risk Scores in East Asian Never-Smokers.

BACKGROUND: Lung adenocarcinoma (LUAD) in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve LUAD risk prediction. METHODS: PRSs were developed using genome-wide association study summary statistics from East Asian (8,002 cases; 20,782 controls) and European (2,058 cases; 5,575 controls) populations. Single-ancestry models included PRS-25, PRS-CT, and LDpred2; multi-ancestry models included LDpred2+PRS-EUR128, PRS-CSx, and CT-SLEB. Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium (FLCCA) and externally validated in the Nanjing Lung Cancer Cohort (NJLCC). We assessed predictive accuracy via AUC, with 10-year and (age 30-80) absolute risks estimates. RESULTS: The best multi-ancestry PRS, using East Asian and European data via CT-SLEB (clumping and thresholding, super learning, empirical Bayes), outperformed the best East Asian-only PRS (LDpred2; AUC=0.629, 95% CI:0.618,0.641), achieving an AUC of 0.640 (95% CI:0.629,0.653) and odds ratio of 1.71 (95% CI:1.61,1.82) per SD increase. NJLCC Validation confirmed robust performance (AUC =0.649, 95% CI: 0.623, 0.676). The top 20% PRS group had a 3.92-fold higher LUAD risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year LUAD risk at age 50 (0.42%) by age 41, nine years earlier. CONCLUSIONS: Multi-ancestry PRS approaches enhance LUAD risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

East Asian never smokers↗

PubMed2026

Downregulated lysyl oxidase in plasma extracellular vesicles: a biomarker linked to brain metastasis risk in lung adenocarcinoma.

BACKGROUND: Brain metastasis (BrM) is a leading cause of mortality in patients with lung adenocarcinoma (LUAD). Extracellular vesicles (EVs), which carry bioactive molecules, play a critical role in tumor microenvironment remodeling and exhibit metastatic organotropism, holding promise as liquid biopsy biomarkers. This study aims to identify plasma EV-derived proteins associated with LUAD-BrM. METHODS: A multi-omics framework was applied. Plasma EVs from 59 stage IV LUAD patients (30 BrM vs 29 non-BrM) were profiled using data-independent acquisition mass spectrometry proteomics. Candidate proteins were screened via bioinformatics and machine learning (LASSO/RF/SVM). Initial validation included tissue proteomics (n = 13), single-cell transcriptomics (TISCH2), and Western blot analysis of a subset of the discovery samples. Functional experiments were conducted in vitro. The lead candidate was ultimately validated in an independent plasma cohort (n = 158) through ELISA. RESULTS: Proteomic analysis implicated collagen-containing extracellular matrix (ECM) pathways. Lysyl oxidase (LOX), a key ECM cross-linking enzyme, was identified as a lead candidate. LOX and its family member LOXL1 were consistently downregulated in BrM tissues and plasma EVs. Single-cell analysis revealed decreased LOX expression specifically in BrM-associated fibroblasts, which showed suppressed ECM-related pathways. In vitro experiments supported a PI3K/AKT-LOX-ECM regulatory axis. Plasma EV-derived LOX demonstrated strong diagnostic performance in the independent cohort, with an AUC of 0.786 (95% CI 0.713iated fi. CONCLUSIONS: Our study establishes plasma EV-derived LOX as a promising non-invasive biomarker for LUAD-BrM through a comprehensive multi-omics validation strategy. We propose a model wherein downregulation of LOX, potentially driven by PI3K/AKT signaling in tumor-associated fibroblasts, contributes to ECM degradation and may promote brain-tropic metastasis. This finding offers new insights for risk stratification and timely intervention in LUAD patients.

Humans↗

PubMed2026

Multi-level Transcriptomic and Machine-learning Analyses Identify MZT1 as a Proliferation-associated Prognostic Marker in Lung Adenocarcinoma.

BACKGROUND/AIM: Lung adenocarcinoma (LUAD) exhibits substantial molecular heterogeneity and variable clinical outcomes, highlighting the need for biomarkers that reflect core tumor biological processes. Centrosome-associated proteins regulate mitotic fidelity and genome stability, yet their roles in LUAD remain incompletely defined. In this study, we systematically characterized mitotic spindle organizing protein 1 (MOZART1; MZT1) and related family members in LUAD. MATERIALS AND METHODS: We performed integrated analyses combining bulk transcriptomic datasets, survival modeling, gene set enrichment, immune deconvolution, machine-learning based prognostic modeling, and single-cell RNA sequencing. Expression patterns and clinical associations of MZT family genes were evaluated across pan-cancer and LUAD cohorts. RESULTS: MZT family genes were consistently upregulated in tumor tissues, with MZT1 showing the most robust expression pattern. Elevated MZT1 expression was significantly associated with reduced overall survival. Functional analyses revealed coordinated activation of proliferative and genome maintenance pathways, including G2/M checkpoint regulation, E2F and MYC signaling, and DNA repair. A multivariable analysis indicated that the prognostic association of MZT1 was reduced after adjusting for canonical proliferation markers, suggesting partial overlap with established proliferation signals. The LASSO-based Cox model demonstrated stable time-dependent predictive performance at 1-, 3-, and 5-year survival. Immune analyses indicated associations between MZT1 expression and tumor microenvironmental features. Single-cell analysis showed that MZT1 expression was predominantly enriched in malignant epithelial cells and associated with proliferative cellular states. Protein-level validation supported concordance with transcriptomic findings. CONCLUSION: MZT1 is a proliferation-associated marker that integrates clinical risk, transcriptional programs, cellular heterogeneity, and predictive modeling in LUAD, providing a potential framework for biomarker development and risk stratification.

Humans↗

PubMed2026

CCT2 defines a highly cisplatin-resistant and poor-prognosis subtype of lung adenocarcinoma.

Cisplatin-based chemotherapy is a standard treatment for lung adenocarcinoma (LUAD), yet acquired cisplatin resistance remains a marked cause of treatment failure. The molecular mechanisms driving cisplatin resistance in LUAD have not been fully elucidated. The present study integrated bulk transcriptomic data, genomic mutation profiles and single-cell RNA sequencing data to systematically investigate cisplatin resistance in LUAD. Resistance-associated genes were identified through differential expression, survival analysis and database integration. Unsupervised clustering was used to define cisplatin resistance-associated subtypes. Functional characteristics were explored using pathway enrichment, immune infiltration, tumor mutation burden and weighted gene co-expression network analysis. A machine learning framework incorporating 101 algorithms was applied to identify key genes and construct a prognostic model. Single-cell analyses and in vitro experiments were performed to validate the biological role of the core gene. Molecular docking and molecular dynamics simulations were conducted to identify potential therapeutic compounds. A total of two molecular subtypes with distinct cisplatin resistance levels and prognostic outcomes were identified. The high-resistance subtype exhibited enhanced cell cycle activity, DNA repair signaling and immune heterogeneity. Machine learning analysis revealed a five-gene signature, with chaperonin-containing TCP1 subunit 2 (CCT2) emerging as a key regulator of cisplatin resistance. Single-cell analyses showed that CCT2 was predominantly enriched in resistant epithelial cell subpopulations. Functional experiments demonstrated that CCT2 knockdown significantly inhibited cell proliferation and enhanced cisplatin sensitivity in LUAD cell lines. A number of candidate compounds targeting CCT2 exhibited stable binding in silico. The present findings identified CCT2 as a key mediator of cisplatin resistance in LUAD and provided potential therapeutic strategies to overcome chemotherapy resistance.

chaperonin-containing TCP-1 subunit 2↗

PubMed2026

Immune-Like Malignant Epithelial Programs Shape Tumor-Immune Interactions and Inform Prognostic Stratification in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is characterized by marked cellular heterogeneity, yet how malignant epithelial states contribute to immune regulation and clinical outcomes remains incompletely defined. We integrated single-cell RNA-sequencing data to map the cellular landscape of LUAD and identify malignant epithelial cells based on inferred copy-number alterations. Epithelial states were further examined through trajectory inference, transcription factor analysis, and cell-cell communication profiling. Single-cell-derived genes were subsequently integrated with TCGA and independent GEO cohorts to construct and validate a machine learning-based prognostic signature. Malignant epithelial cells displayed distinct functional programs, including an immune-like state associated with genomic instability, immune-related transcriptional activity, tumor-immune communication, and patient outcomes. The resulting immune-like malignant epithelial cell signature (IMEC-Sig) consistently stratified survival across multiple cohorts. Low IMEC-Sig scores were accompanied by greater immune infiltration, higher immune checkpoint expression, and increased immunophenoscore, whereas high scores were linked to a comparatively immunosuppressive phenotype. Pan-cancer analyses further identified KRT8 as a gene associated with unfavorable prognosis, and functional experiments showed that KRT8 silencing suppressed proliferation, migration, invasion, and colony formation in LUAD cells. Together, these findings connect malignant epithelial heterogeneity with the immune context and clinical outcomes, support IMEC-Sig as a biologically informed prognostic tool, and nominate KRT8 as a potential therapeutic target in LUAD.

Humans↗

PubMed2026

Pathomics-based machine learning models for predicting METTL5 expression and prognosis in lung adenocarcinoma.

BACKGROUND: METTL5, an N6-methyladenosine (m6A) RNA methyltransferase, has been implicated in tumor progression, but its prognostic value and non-invasive prediction in lung adenocarcinoma (LUAD) remain unclear. This study aimed to develop a pathomics-based machine learning model to predict METTL5 expression from histopathological images and evaluate its prognostic significance in LUAD. METHODS: A total of 327 LUAD patients from The Cancer Genome Atlas (TCGA) with matched hematoxylin and eosin (H&E) slides, transcriptomic, and clinical data were included and randomly divided into training and validation sets (7:3). Quantitative histopathological features were extracted using PyRadiomics. Feature selection was performed via maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE), followed by construction of a Gradient Boosting Machine (GBM) model. A pathomics score (PS) was generated to assess prognostic relevance. Survival analyses, gene set variation analysis (GSVA), tumor mutational burden (TMB), immune infiltration analysis, and in vitro functional assays were conducted. RESULTS: METTL5 overexpression was independently associated with poor overall survival [hazard ratio (HR) =1.637, P=0.007]. The model achieved good predictive performance [area under the curve (AUC) =0.847 in the training set and 0.752 in the validation set]. High PS was significantly associated with worse survival and remained an independent prognostic factor (HR =1.563, P=0.03). Elevated PS correlated with altered metabolic pathways, increased TMB, and immune microenvironment changes. METTL5 knockdown reduced proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) in A549 cells. CONCLUSIONS: The pathomics-based model accurately predicts METTL5 expression and provides prognostic stratification in LUAD, supporting its potential as a practical imaging-derived biomarker.

Methyltransferase-like 5↗

PubMed2026

Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.

BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.

Golgi apparatus↗

PubMed2026

Targeting ALDH2 with Alda-1 to reverse cisplatin resistance in lung adenocarcinoma.

BACKGROUND: Cisplatin resistance remains a major obstacle in lung adenocarcinoma (LUAD) treatment. The role of Aldehyde dehydrogenase 2 (ALDH2), a detoxifying enzyme, in LUAD prognosis and chemoresistance is poorly understood. METHODS: We analyzed ALDH2's prognostic value using clinical cohorts, TCGA, and proteomic data. Cisplatin-resistant cell lines and xenograft models were used to assess the effect of the ALDH2 agonist Alda-1. Molecular mechanisms were investigated via gain/loss-of-function studies. RESULTS: High ALDH2 expression was significantly associated with improved survival in univariate analysis and correlated with a favorable genomic instability profile in LUAD. Pharmacological activation of ALDH2 with Alda-1 restored cisplatin sensitivity in resistant cells and potently enhanced cisplatin's efficacy in vivo. Mechanistically, ALDH2 activation upregulated PKC-ζ, leading to downregulation of the drug efflux pump MDR1. Proteomic analysis further linked low ALDH2 expression to a pro-chemoresistance signature. CONCLUSION: ALDH2 represents a potential prognostic biomarker associated with favorable outcomes in LUAD, particularly in patients receiving chemotherapy. Its activation via Alda-1 overcomes cisplatin resistance by targeting the PKC-ζ/MDR1 axis, presenting a novel therapeutic strategy.

Cisplatin↗

PubMed2026

Integrated multi-omic profiling enables recurrence risk stratification beyond pathological stage in resected EGFR-mutant lung adenocarcinoma.

BACKGROUND: Early-stage EGFR-mutant lung adenocarcinoma (LUAD) demonstrates heterogeneous outcomes after curative surgery, yet adjuvant treatment decisions are guided by pathological stage alone. Following the ADAURA trial, adjuvant osimertinib is the standard of care for resected stage IB-IIIA EGFR-mutant LUAD; however, real-world data demonstrate that up to 40% of patients remain disease-free at five years without adjuvant osimertinib, underscoring the need for improved risk stratification. PATIENTS AND METHODS: We performed integrated clinical, genomic and transcriptomic profiling of 400 patients with resected stage IA-IIIA EGFR-mutant LUAD. EGFR-mutant recurrence risk models integrating clinical, genomic and transcriptomic data were developed and validated across one internal and three external cohorts. RESULTS: Genomic instability, including TP53 co-mutations, copy number alterations and APOBEC-associated mutational signatures, increased with pathological stage. RBM10 co-mutations were enriched in tumours with L858R mutations and correlated with upregulation of WNT signalling and epithelial-mesenchymal transition. Transcriptomic features outperformed clinical or genomic variables alone in predicting recurrence risk, and a multi-omic model demonstrated superior and reproducible performance, achieving a median concordance index of 75.4% across four independent validation cohorts. The multi-omic model stratified recurrence risk within individual pathological stages, including stage I disease, and identified patients most likely to benefit from adjuvant EGFR TKI. CONCLUSIONS: These findings define the molecular heterogeneity of early-stage EGFR-mutant LUAD and support multi-omic risk stratification to inform adjuvant EGFR TKI decisions beyond pathological stage. Prospective validation in larger cohorts will be required to confirm these findings.

Journal Article↗