Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma

The C5a/C5aR1 Axis Promotes Migration of Tolerogenic Dendritic Cells to Lymph Nodes, Impairing the Anticancer Immune Response

The precise mechanisms by which the complement system contributes to the establishment of an immunosuppressive tumor microenvironment and promotes tumor progression remain unclear. In this study, we investigated the expression and function of complement C5a receptor 1 (C5aR1) in human and mouse cancer-associated dendritic cells (DC). First, we observed an overexpression of C5aR1 in tumor-infiltrating DCs, compared with DCs from the blood or spleen. C5aR1 expression was restricted to type 2 conventional DCs and monocyte-derived DCs, which displayed a tolerogenic phenotype capable of inhibiting T-cell activation and promoting tumor growth. C5aR1 engagement in DCs drove their migration from tumors to tumor-draining lymph nodes, where C5a levels were higher. We used this knowledge to optimize an anticancer therapy aimed at enhancing DC activity. In three syngeneic tumor models, C5aR1 inhibition significantly enhanced the efficacy of poly I:C, a Toll-like receptor 3 agonist, in combination with PD-1/PD-L1 blockade. The contribution of C5aR1 inhibition to the antitumor activity of the combination treatment relied on type 1 conventional DCs and antigen-specific CD8+ T cells, required lymphocyte egress from secondary lymphoid organs, and was associated with an increase in IFNγ signaling. In conclusion, our study highlights the importance of the C5a/C5aR1 axis in the biology of cancer-associated DCs and provides compelling evidence for the therapeutic potential of modulating the complement system to enhance DC-mediated immune responses against tumors.

Single cell transcriptomic analysis reveals tumor immune infiltration by macrophage cells gene signature in lung adenocarcinoma

BACKGROUND

Tumor-associated macrophages (TAMs) play pivotal roles in innate immunity and contribute to the advancement of lung cancer. We aimed to identify novel TAM-related biomarkers and significance of macrophage infiltration in lung adenocarcinoma (LUAD) through an integrative analysis of single-cell RNA-sequencing (scRNA-seq) data. To describe the cell atlas and construct a novel prognostic signature in LUAD.

METHODS

The gene signature linked to TAMs was identified utilizing Scanpy from the scRNA-seq dataset GSE131907. Subsequent analysis involved evaluating the expression levels of these genes, their potential molecular mechanisms, and prognostic significance in LUAD using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We also constructed a risk score models through LASSO Cox regression for these genes. The underlying mechanism was further elucidated through the application of GSEA, ESTIMATE, TIDE, and other bioinformatic algorithms.

RESULTS

Single-cell atlas was described by analyze 29 scRNA-seq samples from 19 LUAD patients. The TAMs-related gene signature (TGS) was identified as an independent prognostic factor by LASSO Cox regression analysis using differential expression genes (DEGs) derived from pro- and anti-inflammatory macrophage cells. Risk score model including nine TAMs-related genes (FOSL1, ZNF697, ADM, UBE2S, TICAM1, S100P, BIRC3, TLE1, and DEFB1) were obtained for prognosis construction. Moreover, the risk model underwent additional validation in four external GEO cohorts: GSE31210, GSE72094, GSE26939, and GSE30219. Interestingly, TGS-high tumors revealed enrichments in TGF-β signaling and hypoxia pathways, which shown low immune infiltration and immunosuppression by ESTIMATE and TIDE algorithm. The TGS-high risk group exhibited lower richness and diversity in the T-cell receptor (TCR) repertoire.

CONCLUSION

This study introduces a novel TGS score developed through LASSO Cox regression analysis, utilizing DEGs in pro- and anti-inflammatory macrophage cells. High TGS tumors exhibited enrichment in TGF-β signaling and hypoxia pathways, suggesting their potential utility in predicting prognosis and immune responses in patients with LUAD. These results offer promising implications for the development of therapeutic strategies for LUAD.

Identification of blood-derived exosomal tumor RNA signatures as noninvasive diagnostic biomarkers for multi-cancer: a multi-phase, multi-center study

BACKGROUND

Cancer remains a leading global cause of mortality, making early detection crucial for improving survival outcomes. The study aims to develop a machine learning-enabled blood-derived exosomal RNA profiling platform for multi-cancer detection and localization.

METHODS

In this multi-phase, multi-center study, we analyzed RNA from exosomes derived from peripheral blood plasma in 818 participants across eight cancer types during the discovery phase. Machine learning techniques were applied to identify potential pan-cancer biomarkers. During the screening and model validation phases, the sample size was progressively expanded to 1,385 participants in two steps, while the candidate biomarkers were refined into a set of 12 exosomal tumor RNA signatures (ETR.sig). In the subsequent model construction phase, diagnostic models were developed using the expanded cohort and ETR.sig. Statistical analyses included the calculation of receiver operating characteristic (ROC) curves and AUC values to assess the models' ability to distinguish cancer cases from controls and determine tumor origins. To further validate and explore the biological relevance of the identified biomarkers, we integrated tissue RNA-seq, single-cell data, and clinical information.

RESULTS

Machine learning analysis initially identified 33 candidate biomarkers, which were narrowed down to 20 ETR.sig in the screening phase and 12 ETR.sig in the validation phase. In the model construction phase, a diagnostic model based on ETR.sig, built using the Random Forest (RF) algorithm, showed excellent performance with an AUC of 0.915 for distinguishing pan-cancer from controls. The multi-class classification model also demonstrated strong classification power, with macro-average and micro-average AUCs of 0.983 and 0.985, respectively, for differentiating between eight cancer types. Additionally, tumor origin classification using the RF-based diagnostic models achieved high AUC values: BRCA 0.976, COAD 0.98, KIRC 0.947, LIHC 0.967, LUAD 0.853, OV 0.972, PAAD 0.977, and PRAD 0.898. Integration of tissue RNA-seq, single-cell data, and clinical information revealed key associations between ETR.sig-related genes and tumor development.

CONCLUSIONS

The study demonstrates the robust potential of exosomal RNA as a minimally invasive biomarker resource for cancer detection. The developed ETR.sig platform offers a promising tool for precision oncology and broad-spectrum cancer screening, integrating advanced computational models with nanoscale vesicle biology for accurate and rapid diagnosis.

RNA aptamer-mediated RNA nanotechnology for potential treatment of cardiopulmonary diseases

Ribonucleic acid (RNA) aptamers are single-stranded RNAs that bind to target proteins or other molecules with high specificity and affinity, modulating biological functions through distinct mechanisms. These aptamers can act n as antagonists to block pathological interactions, agonists to activate signaling pathways, or delivery vehicles for therapeutic cargos such as siRNAs and miRNAs. The advances in RNA nanotechnology further enhances the versatility of RNA aptamers, offering scalable platforms for engineering. In this review, we have summarized recent developments in RNA aptamer-mediated RNA nanotechnology and provide an overview of its potential in treating cardiovascular and respiratory disorders, including atherosclerosis, acute coronary syndromes, heart failure, lung cancer, pulmonary hypertension, asthma, chronic obstructive pulmonary disease (COPD), acute lung injury, viral respiratory infections, and pulmonary fibrosis. By integrating aptamer technologies with innovative delivery systems, RNA aptamers hold the potential to revolutionize the treatment landscape for cardiopulmonary diseases.

Deciphering key roles of B cells in prognostication and tailored therapeutic strategies for lung adenocarcinoma: a multi-omics and machine learning approach towards predictive, preventive, and personalized treatment strategies

Background

Lung adenocarcinoma (LUAD) remains a significant global health challenge, with an urgent need for innovative predictive, preventive, and personalized medicine (PPPM) strategies to improve patient outcomes. This study leveraged multi-omics and machine learning approaches to uncover the prognostic roles of B cells in LUAD, thereby reinforcing the PPPM approach.

Methods

We integrated multi-omics data, including bulk RNA, ATAC-seq, single-cell RNA, and spatial transcriptomics sequencing, to characterize the B cell landscape in LUAD within the PPPM framework. Subsequently, we developed an integrative machine learning program that generated the Scissor+ related B cell score (SRBS). This score was validated in the training and validation sets, and its prognostic value was assessed along with clinical features to develop predictive nomograms. This study further assessed the role of SRBS and SRBS genes in response to immunotherapy and identified personalized drug targets for distinct risk subgroups, with gene expression verified experimentally to ensure tailored medical interventions.

Results

Our analysis identified 79 Scissor+ B cell genes linked to LUAD prognosis, supporting the predictive aspect of PPPM. The SRBS model, which utilizes multiple machine learning algorithms, performed excellently in predicting prognosis and clinical transformation, embodying the preventive and personalized aspects of PPPM. Multifactorial analysis confirmed that SRBS was an independent prognostic factor. We observed varying biological functions and immune cell infiltration in the tumor immune microenvironment (TIME) between the high- and low-SRBS groups, underscoring personalized treatment approaches. Notably, patients with elevated SRBS may exhibit resistance to immunotherapy but show increased sensitivity to chemotherapy and targeted therapies. Additionally, we found that LDHA, as an SRBS gene with significant clinical implications, may regulate the sensitivity of LUAD cells to cisplatin.

Conclusion

This study presents a B cell-associated gene signature that serves as a prognostic marker to facilitate personalized treatment for patients with LUAD, adhering to the principles of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00390-4.

Metabolic reprogramming of neutrophils in the tumor microenvironment: Emerging therapeutic targets

Neutrophils are pivotal in the immune system and have been recognized as significant contributors to cancer development and progression. These cells undergo metabolic reprogramming in response to various stimulus, including infections, diseases, and the tumor microenvironment (TME). Under normal conditions, neutrophils primarily rely on aerobic glucose metabolism for energy production. However, within the TME featured by hypoxic and nutrient-deprived conditions, they shift to altered anaerobic glycolysis, lipid metabolism, mitochondrial metabolism and amino acid metabolism to perform their immunosuppressive functions and facilitate tumor progression. Targeting neutrophils within the TME is a promising therapeutic approach. Yet, focusing on their metabolic pathways presents a novel strategy to enhance cancer immunotherapy. This review synthesizes the current understanding of neutrophil metabolic reprogramming in the TME, with an emphasis on the underlying molecular mechanisms and signaling pathways. Studying neutrophil metabolism in the TME poses challenges, such as their short lifespan and the metabolic complexity of the environment, necessitating the development of advanced research methodologies. This review also discusses emerging solutions to these challenges. In conclusion, given their integral role in the TME, targeting the metabolic pathways of neutrophils could offer a promising avenue for cancer therapy.

Emerging clinical applications of single-cell RNA sequencing in oncology

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of complex tissues both in health and in disease. Over the past decade, scRNA-seq has been applied to tumour samples obtained from patients with cancer in hundreds of studies, thereby advancing the view that each tumour is a complex ecosystem and uncovering the diverse states of both cancer cells and the tumour microenvironment. Such studies have primarily investigated and provided insights into the basic biology of cancer, although considerable research interest exists in leveraging these findings towards clinical applications. In this Review, we summarize the available data from scRNA-seq studies investigating samples from patients with cancer with a particular focus on findings that are of potential clinical relevance. We highlight four main research objectives of scRNA-seq studies and describe some of the most relevant findings towards such goals. We also describe the limitations of scRNA-seq, as well as future approaches in this field that are anticipated to further advance clinical applicability.

Automated Laser-Assisted Single-Cell Sorting for Cell Functional and RNA Sequencing

Accurate and efficient sorting of single target cells is crucial for downstream single-cell analysis, such as RNA sequencing, to uncover cellular heterogeneity and functional characteristics. However, conventional single-cell sorting techniques, such as manual micromanipulation or fluorescence-activated cell sorting, do not match current demands and are limited by low throughput, low sorting efficiency and precision, or limited cell viability. Here, we report an automated, highly efficient single-cell sorter, integrating laser-induced forward transfer (LIFT) with a high-throughput picoliter micropore array. The micropore array was surface-functionalized to manipulate liquid surface tension, facilitating the formation of single-cell picoliter droplets in the micropores to realize automated and highly efficient (>80%) single-cell isolation. Using an in-house built microscopic system, rare target cells were identified and automatically retrieved by LIFT with precise sorting efficiency (about 100%) for downstream single-cell analysis while maintaining high cell viability (about 80%). As a case demonstration, we demonstrated the accurate sorting of rare transfected PC-9 cells and post-transfection cell culture, minimizing cell loss and the risk of contamination. Furthermore, we performed single-cell RNA sequencing and showed that high-quality single-cell transcriptome information was efficiently and reliably obtained during cell sorting, preventing additional costs due to low sorting accuracy. The single-cell sorter will become invaluable for single-cell analysis, laying the foundation for multiomics analysis and precision medicine research.

Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases

Brain metastases frequently develop in patients with non-small cell lung cancer (NSCLC) and are a common cause of cancer-related deaths, yet our understanding of the underlying human biology is limited. Here we performed multimodal single-nucleus RNA and T cell receptor, single-cell spatial and whole-genome sequencing of brain metastases and primary tumors of patients with treatment-naive NSCLC. Chromosomal instability (CIN) is a distinguishing genomic feature of brain metastases compared with primary tumors, which we validated through integrated analysis of molecular profiling and clinical data in 4,869 independent patients, and a new cohort of 12,275 patients with NSCLC. Unbiased analyses revealed transcriptional neural-like programs that strongly enriched in cancer cells from brain metastases, including a recurring, CINhigh cell subpopulation that preexists in primary tumors but strongly enriched in brain metastases, which was also recovered in matched single-cell spatial transcriptomics. Using multiplexed immunofluorescence in an independent cohort of treatment-naive pairs of primary tumors and brain metastases from the same patients with NSCLC, we validated genomic and tumor-microenvironmental findings and identified a cancer cell population characterized by neural features strongly enriched in brain metastases. This comprehensive analysis provides insights into human NSCLC brain metastasis biology and serves as an important resource for additional discovery.

Mycoplasma ovipnuemoniae impairs the immune response of sheep and suppresses neutrophil function by inhibiting S100A9

Mycoplasma pneumonia is a chronic respiratory disease that seriously affects the health of sheep. To date, little information is available about the damage caused by Mycoplasma ovipneumoniae (MO) pneumonia to host lungs. Here, after sheep were infected with MO for 28 days, severe inflammatory reactions and pathological damage occurred. By using single-cell RNA sequencing (scRNA-seq), all the transcriptome changes in 11 cell types in sheep lung tissue were systematically analyzed, and the key biological processes regulating inflammation and immunity were identified. Moreover, we constructed both intercellular communication models and differential expression maps of key regulatory genes for each cell subgroup. We also specifically focused on the response of T cell subpopulations and neutrophils to MO infection. Long-term infection may affect an organism's immune response, inhibit intercellular communication, and highlight the important role of the cyclophilin A (CypA) and macrophage migration inhibitory factor (MIF) pathways in intercellular communication. Notably, MO infection decreased the toxicity of CD8 effector T cells and depleted regulatory T cells, thus inhibiting normal cell function. Subsequently, emphasis was placed on the important role of the neutrophil marker gene S100A9 in promoting neutrophil clearance of MO through activation of the ERK signaling pathway and reactive oxygen species (ROS) burst in vitro. These results contribute to understanding the progression of MO infection in the lungs and provide a rich database on the molecular basis of the response to different cell types in MO infection.

Cancer-associated fibroblasts promote EGFR-TKI resistance via the CTHRC1/glycolysis/H3K18la positive feedback loop

Acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) remains a major challenge in the treatment of lung cancer. Cancer associated fibroblasts (CAFs) play a key role in promoting resistance to anti-cancer therapies. This study identified a subpopulation of CAFs characterized by the overexpression of collagen triple helix repeat-containing 1 (CTHRC1) through single-cell RNA sequencing of lung cancer patients undergoing EGFR-TKI treatment. These CTHRC1+ CAFs were enriched in drug-resistant tumors. Mechanistically, CTHRC1+ CAFs enhance the glycolytic activity of cancer cells by activating the TGF-β/Smad3 signaling pathway. Excess lactate produced in the process of glycolysis further upregulates CTHRC1 expression in CAFs through histone lactylation, creating a positive feedback loop that sustains EGFR-TKI resistance. The study also demonstrated that Gambogenic Acid, a natural compound, can disrupt this feedback loop, thereby improving the efficacy of EGFR-TKI therapy. Additionally, the presence of CTHRC1+ CAFs in tumor tissues could serve as a biomarker for predicting the response to EGFR-TKI therapy and patient prognosis. Overall, this study highlights the significant role of CAFs in EGFR-TKI resistance and suggests that targeting CTHRC1+ CAFs could be a promising strategy to overcome drug resistance in lung cancer.

Cancer brain metastasis: molecular mechanisms and therapeutic strategies

Brain metastases (BMs) are the most common intracranial tumors in adults and the major cause of cancer-related morbidity and mortality. The occurrence of BMs varies according to the type of primary tumors with most frequence in lung cancer, melanoma and breast cancer. Among of them, lung cancer has been reported to have a higher risk of BMs than other types of cancers with 40 ~ 50% of such patients will develop BMs during the course of disease. BMs lead to many neurological complications and result in a poor quality of life and short life span. Although the treatment strategies were improved for brain tumors in the past decades, the prognosis of BMs patients is grim. Poorly understanding of the molecular and cellular characteristics of BMs and the complicated interaction with brain microenvironment are the major reasons for the dismal prognosis of BM patients. Recent studies have enhanced understanding of the mechanisms of BMs. The newly identified potential therapeutic targets and the advanced therapeutic strategies have brought light for a better cure of BMs. In this review, we summarized the mechanisms of BMs during the metastatic course, the molecular and cellular landscapes of BMs, and the advances of novel drug delivery systems for overcoming the obstruction of blood-brain barrier (BBB). We further discussed the challenges of the emerging therapeutic strategies, such as synergistic approach of combining targeted therapy with immunotherapy, which will provide vital clues for realizing the precise and personalized medicine for BM patients in the future.

Integrative analysis of genetic variability and functional traits in lung adenocarcinoma epithelial cells via single-cell RNA sequencing, GWAS, bayesian deconvolution, and machine learning

BACKGROUND

Lung adenocarcinoma remains a leading cause of cancer-related mortality worldwide, characterized by high genetic and cellular heterogeneity, especially within the tumor microenvironment.

OBJECTIVE

This study integrates single-cell RNA sequencing (scRNA-seq) with genome-wide association studies (GWAS) using Bayesian deconvolution and machine learning techniques to unravel the genetic and functional complexity of lung adenocarcinoma epithelial cells.

METHODS

We performed scRNA-seq and GWAS analysis to identify critical cell populations affected by genetic variations. Bayesian deconvolution and machine learning techniques were applied to investigate tumor progression, prognosis, and immune-epithelial cell interactions, particularly focusing on immune checkpoint markers such as PD-L1 and CTLA-4.

RESULTS

Our analysis highlights the importance of genes like SLC2A1, which regulates glucose metabolism and correlates with tumor invasiveness and poor prognosis. Immune-epithelial interactions suggest a suppressive tumor microenvironment, potentially hindering immune responses. Additionally, machine learning models identify core prognostic genes such as F12, GOLM1, and S100P, which are significantly associated with patient survival.

CONCLUSIONS

This comprehensive approach provides novel insights into lung adenocarcinoma biology, emphasizing the role of genetic and immune factors in tumor progression. The findings support the development of personalized therapeutic strategies targeting both tumor cells and the immune microenvironment.

Integration of single-cell and bulk RNA sequencing to identify a distinct tumor stem cells and construct a novel prognostic signature for evaluating prognosis and immunotherapy in LUAD

BACKGROUND

Cancer stem cells (CSCs) are crucial for lung adenocarcinoma (LUAD). This study investigates tumor stem cell gene signatures in LUAD using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq), aiming to develop a prognostic tumor stem cell marker signature (TSCMS) model.

METHODS

LUAD scRNA-seq and RNA-seq data were analyzed. CytoTRACE software quantified the stemness score of tumor-derived epithelial cell clusters. Gene Set Variation Analysis (GSVA) identified potential biological functions in different clusters. The TSCMS model was constructed using Lasso-Cox regression, and its prognostic value was assessed through Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses. Immune infiltration was evaluated using the Cibersortx algorithm, and drug response prediction was performed using the pRRophetic package. TAF10 functional investigations in LUAD cells involved bioinformatics analysis, qRT-PCR, Western blot, immunohistochemistry, and assays for cell proliferation.

RESULTS

Seven distinct cell clusters were identified by CytoTRACE, with epithelial cell cluster 1 (Epi_C1) showing the highest stemness potential. The TSCMS model included 49 tumor stemness-related genes; high-risk patients exhibited lower immune and ESTIMATE scores and increased tumor purity. Significant differences in immune landscapes and chemotherapy sensitivity were observed between risk groups. TAF10 positively correlated with RNA expression-based stemness scores in various tumors, including LUAD. It was over-expressed in LUAD cell lines and clinical tumor tissues, with high expression linked to poor prognosis. Silencing TAF10 inhibited LUAD cell proliferation and tumor sphere formation.

CONCLUSIONS

This study demonstrates the TSCMS model's prognostic value in LUAD, reveals insights into immune infiltration and therapeutic response, and identifies TAF10 as a potential therapeutic target.

Single-cell transcriptomics reveals the multidimensional dynamic heterogeneity from primary to metastatic gastric cancer

Reprogramming of the tumor microenvironment (TME) plays a critical role in gastric cancer (GC) progression and metastasis. However, the multidimensional features between primary tumors and organ-specific metastases remain poorly understood. In this study, we characterized the dynamic heterogeneity of GC from primary to metastatic stages. We identified seven major cell types and 27 immune and stromal subsets. Immune cells decreased, while immunosuppressive cells increased in ovarian and peritoneal metastases. A 30-gene signature for ovarian metastasis was validated in GC cohorts. Additionally, critical ligand-receptor interactions, including LGALS9-MET in liver metastasis and PVR-TIGIT in lymph node metastasis, were identified as potential therapeutic targets. Furthermore, CLOCK, a transcription factor, was associated with poor prognosis and influenced immune cell interactions and migration. Collectively, this study provides valuable insights into TME dynamics in GC and highlights potential avenues for targeted therapies.

Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

Mitochondrial carrier homolog 2 is important for mitochondrial functionality and non-small cell lung cancer cell growth

Discovering new molecular targets for non-small cell lung cancer (NSCLC) is critically important. Enhanced mitochondrial function can promote NSCLC progression by enabling metabolic reprogramming, resistance to apoptosis, and increased cell proliferation. Mitochondrial carrier homolog 2 (MTCH2), located in the outer mitochondrial membrane, is pivotal in regulating mitochondrial activities. This study examines MTCH2 expression and its functional role in NSCLC. Bioinformatic analysis showed that MTCH2 is overexpressed in NSCLC tissues, correlating with poor prognosis and other key clinical parameters of the patients. In addition, single-cell sequencing data revealed higher MTCH2 expression levels in cancer cells of NSCLC tumor mass. Moreover, MTCH2 is also upregulated in locally-treated NSCLC tissues and multiple primary/established human NSCLC cells. In various NSCLC cells, silencing MTCH2 via targeted shRNA or knockout (KO) using the CRISPR/Cas9 method significantly hindered cell proliferation, migration and invasion, while inducing apoptosis. MTCH2 knockdown or KO robustly impaired mitochondrial function, as indicated by reduced mitochondrial respiration, decreased complex I activity, lower ATP levels, lower mitochondrial membrane potential (mitochondrial depolarization), and increased reactive oxygen species (ROS) production. Conversely, ectopic overexpression of MTCH2 in primary NSCLC cells enhanced mitochondrial complex I activity and ATP production, promoting cell proliferation and migration. In vivo, the intratumoral injection of MTCH2 shRNA adeno-associated virus (aav) impeded the growth of subcutaneous xenografts of primary NSCLC cells in nude mice. In MTCH2 shRNA aav-injected NSCLC xenograft tissues, there was decreases in MTCH2 expression, mitochondrial complex I activity, ATP content, and the glutathione (GSH)/glutathione disulfide (GSSG) ratio, but increase in thiobarbituric acid reactive substances (TBAR) activity. Additionally, MTCH2 silencing led to reduced Ki-67 staining but increased apoptosis in NSCLC xenografts. Collectively, these findings demonstrate that overexpressed MTCH2 promotes NSCLC cell growth potentially through the maintenance of mitochondrial hyper-function, highlighting MTCH2 as a novel and promising therapeutic target for treating this disease.

A novel classification method for LUAD that guides personalized immunotherapy on the basis of the cross-talk of coagulation- and macrophage-related genes

Purpose

The coagulation process and infiltration of macrophages affect the progression and prognosis of lung adenocarcinoma (LUAD) patients. This study was designed to explore novel classification methods that better guide the precise treatment of LUAD patients on the basis of coagulation and macrophages.

Methods

Weighted gene coexpression network analysis (WGCNA) was applied to identify M2 macrophage-related genes, and TAM marker genes were acquired through the analysis of scRNA-seq data. The MSigDB and KEGG databases were used to obtain coagulation-associated genes. The intersecting genes were defined as coagulation and macrophage-related (COMAR) genes. Unsupervised clustering analysis was used to evaluate distinct COMAR patterns for LUAD patients on the basis of the COMAR genes. The R package "limma" was used to identify differentially expressed genes (DEGs) between COMAR patterns. A prognostic risk score model, which was validated through external data cohorts and clinical samples, was constructed on the basis of the COMAR DEGs.

Results

In total, 33 COMAR genes were obtained, and three COMAR LUAD subtypes were identified on the basis of the 33 COMAR genes. There were 341 DEGs identified between the three COMAR subtypes, and 60 prognostic genes were selected for constructing the COMAR risk score model. Finally, 15 prognosis-associated genes (CORO1A, EPHA4, FOXM1, HLF, IFIH1, KYNU, LY6D, MUC16, PPARG, S100A8, SPINK1, SPINK5, SPP1, VSIG4, and XIST) were included in the model, which was efficient and robust in predicting LUAD patient prognosis and clinical outcomes in patients receiving anti-PD-1/PD-L1 immunotherapy.

Conclusions

LUAD can be classified into three subtypes according to COMAR genes, which may provide guidance for precise treatment.

Transcriptomic profiles of single-cell autophagy-related genes (ATGs) in lung diseases

Autophagy related genes (ATGs) play essential roles in maintaining cellular functions, although biological and pathological alterations of ATG phenotypes remain poorly understood. To address this knowledge gap, we utilized the single-cell sequencing technology to elucidate the transcriptomic atlas of ATGs in lung diseases, with a focus on lung epithelium and lymphocytes. This study conducted a comprehensive investigation into RNA profiles of ATGs in the lung tissues obtained from healthy subjects and patients with different lung diseases through single-cell RNA sequencing (scRNA-seq), including COVID-19 related acute lung damage, idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), systemic sclerosis (SSC), and lung adenocarcinoma (LUAD). Our findings revealed significant variations of ATGs expression across lung epithelial cell subsets, e.g., over-expression of MAPK8 in basal cells, ATG10 in club cells, and BCL2 in a goblet cell subset. The changes of autophagy-related pathways varied between lung epithelial and lymphocyte subsets. We identified the disease-associated changes in ATG expression, including significant alterations in BCL2, BCL2L1, PRKCD, and PRKCQ in inflammatory lung diseases (COPD and IPF), and MAP2K7, MAPK3, and RHEB in lung cancer (LUAD), as compared to normal lung tissues. Key ligand-receptor pairs (e.g., CD6-ALCAM, CD99-CD99) and signaling pathways (e.g., APP, CD74) might serve as biomarkers for lung diseases. To evaluate ATGs responses to external challenges, we examined ATGs expression in different epithelial cell lines exposed to cigarette smoking extract (CSE), lysophosphatidylcholine (lysoPC), lipopolysaccharide (LPS), and cholesterol at various doses and durations. Notable changes were observed in CFLAR, EIF2S1, PPP2CA, and PPP2CB in A549 and H1299 against CSE and LPS. The heterogeneity of ATGs expression was dependent on cell subsets, pathologic conditions, and challenges, as well as varied among cellular phenotypes, functions, and behaviors, and the severity of lung diseases. In conclusion, our data might provide new insights into the roles of ATGs in epithelial biology and pulmonary disease pathogenesis, with implications for disease progression and prognosis.

Dendritic cell maturation in cancer

Dendritic cells (DCs) are specialized antigen-presenting cells that are present at low abundance in the circulation and tissues; they serve as crucial immune sentinels by continually sampling their environment, migrating to secondary lymphoid organs and shaping adaptive immune responses through antigen presentation. Owing to their ability to orchestrate tolerogenic or immunogenic responses to a specific antigen, DCs have a pivotal role in antitumour immunity and the response to immune checkpoint blockade and other immunotherapeutic approaches. The multifaceted functions of DCs are acquired through a complex, multistage process called maturation. Although the role of inflammatory triggers in driving DC maturation was established decades ago, less is known about DC maturation in non-inflammatory contexts, such as during homeostasis and in cancer. The advent of single-cell technologies has enabled an unbiased, high-dimensional characterization of various DC states, including mature DCs. This approach has clarified the molecular programmes associated with DC maturation and also revealed how cancers exploit these pathways to subvert immune surveillance. In this Review, we discuss the mechanisms by which cancer disrupts DC maturation and highlight emerging therapeutic opportunities to modulate DC states. These insights could inform the development of DC-centric immunotherapies, expanding the arsenal of strategies to enhance antitumour immunity.

Single-cell RNA sequencing reveals tumor heterogeneity in small cell neuroendocrine cervical carcinoma

Small cell neuroendocrine cervical carcinoma (SCNECC) is an aggressive gynecological malignancy with poor prognosis. The precision therapeutic strategies for SCNECC are severely limited by the complex tumor microenvironment. Here, we mapped the single-cell landscape of a total of six samples from matched SCNECC cancerous foci and normal adjacent cervical tissues. Through analysis of 68,455 high-quality cells, malignant epithelial cells were identified with increased neuroendocrine differentiation and reduced keratinization. Within four epithelial cell clusters, the key transcription factors ASCL1, NEUROD1, POU2F3, and YAP1 defined molecular subtypes. Transitional trajectory among subtypes characterized two distinct carcinogenesis pathways in SCNECC. The P-type SCNECC showed potentially enhanced immune infiltration over other subtypes. Intercellular communication analysis identified several immune checkpoints and differentially expressed signaling pathways among subtypes. Through western blotting, the TC-YIK cell line was identified as an N-type SCNECC cell with high expression of SLFN11 and mTOR. Based on immunohistochemical staining of malignant subtyping markers, a cohort of 66 SCNECC patients from our hospital were divided into five subtypes. We further combined YAP1 expression with other clinicopathological factors (Cox p < 0.05) to establish a prognostic nomogram. Overall, these findings provide clues for tumorigenesis, precision treatments and prognostic prediction in SCNECC.

Targeting HVEM-GPT2 axis: a novel approach to T cell activation and metabolic reprogramming in non-small cell lung cancer therapy

BACKGROUND

The modulation of tumor microenvironments through immune checkpoint pathways is pivotal for the development of effective cancer immunotherapies. This study aims to explore the role of HVEM in non-small cell lung cancer (NSCLC) microenvironment.

METHODS

The lung cancer datasets for this study were directly downloaded from The Cancer Genome Atlas (TCGA). Single-cell data were sourced from the Tumor Immune Single-cell Hub (TISCH). Multiplex immunohistochemistry (mIHC) was used to explore the cellular composition and spatial distribution of HVEM in lung cancer immune microenvironment. The immune microenvironment of HVEM KO mice bearing mouse lung cancer cell was also evaluated. Co-cultured system and phenotype assays facilitated the examination of Jurkat T cells' effect on A549 and H1299 lung cancer cells. Quantitative PCR and Western blotting determined gene and protein expression, respectively, cellular respiration was measured through oxygen consumption rate (OCR) assays. Lung cancer cells co-cultured with Jurkat T cells were xenografted into nude mice to evaluate tumor growth and metastatic potential. Next, RNA-seq, COIP, Dual-luciferase reporter experiment, and CHIP-seq were used to explore the potential underlying mechanism.

RESULTS

In our study, we investigated the role of HVEM in the microenvironment of NSCLC and its implications in immunotherapy. Crucially, HVEM, part of the tumor necrosis factor receptor superfamily, influences T cell activation, potentially impacting immunotherapeutic outcomes. Using the TIDE algorithm, our results showcased a link between HVEM levels and immune dysfunction in NSCLC patients. Delving deeper into the NSCLC microenvironment, we found HVEM predominantly expressed in T cell subpopulations. CD8 + HVEM + and CD4 + HVEM + indicated better prognosis in lung adenocarcinoma tissue microarray using multiplex immunohistochemistry. Activated T cells, particularly from the Jurkat cell line, significantly inhibited NSCLC progression, reducing both proliferation and invasion capabilities of A549 and H1299 lung cancer cell lines. In vivo models reinforced these observations. Manipulating HVEM expression revealed its essential role in T cell survival and activation. In addition, animal experiments revealed the importance of HVEM in maintaining activated peripheral immunity and inflamed local tumor microenvironment. Furthermore, our data suggest that HVEM is pivotal in T cell metabolic reprogramming, transitioning from oxidative phosphorylation to aerobic glycolysis. RNA sequencing illuminated a potential relationship between HVEM and GPT2, an enzyme tied to amino acid metabolism and cellular energetics. Subsequent experiments confirmed that HVEM's influence on T cell activation and metabolism is potentially mediated through its regulation of GPT2. In addition, GATA1 was validated to regulate HVEM expression in activated Jurkat T cells.

CONCLUSIONS

Our study establishes that HVEM significantly influences T cell functionality and NSCLC cell dynamics, pinpointing the HVEM-GPT2 axis as a promising target for NSCLC therapy.

A deep learning framework for in silico screening of anticancer drugs at the single-cell level

Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells. We introduce a deep learning framework named Shennong for in silico screening of anticancer drugs for targeting each of the landscape cell clusters. Utilizing Shennong, we could predict individual cell responses to pharmacologic compounds, evaluate drug candidates' tissue damaging effects, and investigate their corresponding action mechanisms. Prioritized compounds in Shennong's prediction results include FDA-approved drugs currently undergoing clinical trials for new indications, as well as drug candidates reporting anti-tumor activity. Furthermore, the tissue damaging effect prediction aligns with documented injuries and terminated discovery events. This robust and explainable framework has the potential to accelerate the drug discovery process and enhance the accuracy and efficiency of drug screening.

Functional heterogeneity of fibroblasts in primary tumors and metastases

Cancer-associated fibroblasts (CAFs) are abundant components of the tumor microenvironment (TME) of most solid malignancies and have emerged as key regulators of cancer progression and therapy response. Although recent technological advances have uncovered substantial CAF molecular heterogeneity at the single-cell level, defining functional roles for most described CAF populations remains challenging. With the aim of bridging CAF molecular and functional heterogeneity, this review focuses on recently identified functional interactions of CAF subtypes with malignant cells, immune cells, and other stromal cells in primary tumors and metastases. Dissecting the heterogeneous functional crosstalk of specific CAF populations with other components is starting to uncover candidate combinatorial strategies for therapeutically targeting the TME and cancer progression.

Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution

The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a computational algorithm that incorporates transcriptome size into scRNA-seq normalization and bulk deconvolution. ReDeconv introduces a scRNA-seq normalization approach, Count based on Linearized Transcriptome Size (CLTS), which corrects differential expressed genes typically misidentified by standard count per 10 K normalization, as confirmed by orthogonal validations. By maintaining transcriptome size variation, CLTS-normalized scRNA-seq enhances the accuracy of bulk deconvolution. Additionally, ReDeconv mitigates gene length effects and models expression variances, thereby improving deconvolution outcomes, particularly for rare cell types. Evaluated with both synthetic and real datasets, ReDeconv surpasses existing methods in precision. ReDeconv alters the practice and provides a new standard for scRNA-seq analyses and bulk deconvolution. The software packages and a user-friendly web portal are available.

Canonical and noncanonical NOTCH signaling in the nongenetic resistance of cancer: distinct and concerted control

Therapeutic resistance in cancer is responsible for numerous cancer deaths in clinical practice. While target mutations are well recognized as the basis of genetic resistance to targeted therapy, nontarget mutation resistance (or nongenetic resistance) remains poorly characterized. Despite its complex and unintegrated mechanisms in the literature, nongenetic resistance is considered from our perspective to be a collective response of innate or acquired resistant subpopulations in heterogeneous tumors to therapy. These subpopulations, e.g., cancer stem-like cells, cancer cells with epithelial-to-mesenchymal transition, and drug-tolerant persisters, are protected by their resistance traits at cellular and molecular levels. This review summarizes recent advances in the research on resistant populations and their resistance traits. NOTCH signaling, as a central regulator of nongenetic resistance, is discussed with a special focus on its canonical maintenance of resistant cancer cells and noncanonical regulation of their resistance traits. This novel view of canonical and noncanonical NOTCH signaling pathways is translated into our proposal of reshaping therapeutic strategies targeting NOTCH signaling in resistant cancer cells. We hope that this review will lead researchers to study the canonical and noncanonical arms of NOTCH signaling as an integrated resistant mechanism, thus promoting the development of innovative therapeutic strategies.

Stromal architecture and fibroblast subpopulations with opposing effects on outcomes in hepatocellular carcinoma

Dissecting the spatial heterogeneity of cancer-associated fibroblasts (CAFs) is vital for understanding tumor biology and therapeutic design. By combining pathological image analysis with spatial proteomics, we revealed two stromal archetypes in hepatocellular carcinoma (HCC) with different biological functions and extracellular matrix compositions. Using paired single-cell RNA and epigenomic sequencing with Stereo-seq, we revealed two fibroblast subsets CAF-FAP and CAF-C7, whose spatial enrichment strongly correlated with the two stromal archetypes and opposing patient prognosis. We discovered two functional units, one is the intratumor inflammatory hub featured by CAF-FAP plus CD8_PDCD1 proximity and the other is the marginal wound-healing hub with CAF-C7 plus Macrophage_SPP1 co-localization. Inhibiting CAF-FAP combined with anti-PD-1 in orthotopic HCC models led to improved tumor regression than either monotherapy. Collectively, our findings suggest stroma-targeted strategies for HCC based on defined stromal archetypes, raising the concept that CAFs change their transcriptional program and intercellular crosstalk according to the spatial context.

Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)-a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene's spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power. Celina proves powerful compared to existing methods in single-cell resolution spatial transcriptomics and stands as the only effective solution for spot-resolution spatial transcriptomics. Applied to five real datasets, Celina uncovers ct-SVGs associated with tumor progression and patient survival in lung cancer, identifies metagenes with unique spatial patterns linked to cell proliferation and immune response in kidney cancer, and detects genes preferentially expressed near amyloid-β plaques in an Alzheimer's model.

Single-cell and spatial transcriptomics reveal a potential role of ATF3 in brain metastasis of lung adenocarcinoma

Background

Brain metastasis (BrM) has been a challenge for lung cancer treatment, but the mechanisms underlying lung cancer BrM remain elusive. This study aims to dissect cellular components and their spatial distribution in human BrM tumors of lung adenocarcinoma (LUAD) and identify potential therapeutic targets.

Methods

We performed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on three LUAD BrMs, and validated our findings using public scRNA-seq data of 10 LUAD BrMs. Western blotting, quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were employed for experimental studies.

Results

By combining scRNA-seq and ST, our analysis revealed the inter- and intra-tumoral heterogeneity of cellular components and their spatial localization within LUAD BrMs. Through RNA velocity and transcription factor (TF) regulatory activity analyses, we identified ATF3 as a potential regulator of the mesenchymal-epithelial transition (MET) program, which plays crucial roles in the colonization of tumor cells at metastatic sites. Furthermore, we demonstrated that knockdown of ATF3 significantly inhibited cancer cell proliferation while promoting cancer cell migration. Mechanistically, ATF3 knockdown could reverse the MET program. Additionally, we revealed that LGALS3/ANXA2-mediated cell-cell interaction between macrophage and tumor cells may also promote the MET program.

Conclusions

Our study provides a single-cell atlas of the cellular composition in BrM of LUAD and identifies ATF3 as a potential therapeutic target for BrM treatment.

Advances in cancer genomics and precision oncology

BACKGROUND

Next-generation sequencing has revolutionized genome science over the last two decades. Indeed, the wealth of sequence information on our genome has deepened our understanding on cancer. Cancer is a genetic disease caused by genetic or epigenetic alternations that affect the expression of genes that control cell functions, particularly cell growth and division. Utilization of next-generation sequencing in cancer gene panels has enabled the identification of actionable gene alterations in cancer patients to guide personalized precision medicine.

OBJECTIVE

The aim is to provide information that can identify actionable gene alterations, enabling personalized precision medicine for cancer patients.

RESULTS & DISCUSSION

Equipped with next-generation sequencing techniques, international collaboration programs on cancer genomics have identified numerous mutations, gene fusions, microsatellite variations, copy number variations, and epigenetics changes that promote the transformation of normal cells into tumors. Cancer classification has traditionally been based on cell type or tissue-of-origin and the morphological characteristics of the cancer. However, interactive genomic analyses have currently reclassified cancers based on systemic molecular-based taxonomy. Although all cancer-causing genes and mechanisms have yet to be completely understood or identified, personalized or precision medicine is now currently possible for some forms of cancer. Unlike the "one-size-fits-all" approach of traditional medicine, precision medicine allows for customized or personalized treatment based on genomic information.

CONCLUSION

Despite the availability of numerous cancer gene panels, technological innovation in genomics and expansion of knowledge on the cancer genome will allow precision oncology to manage even more types of cancers.

Utilizing sc-linker to integrate single-cell RNA sequencing and human genetics to identify cell types and driver genes associated with non-small cell lung cancer

BACKGROUND

Genome-wide association studies (GWAS) provide a powerful method for identifying the loci and genes that contribute to disease. However, in many cases, the specific cell types and states that confer disease risk through these genes remain unknown. Determining this relationship is crucial for identifying pathogenic processes and developing therapeutic strategies.

METHODS

In this study, we utilized the sc-linker framework developed by Jagadeesh, which is an integrated framework that combines single-cell RNA sequencing (scRNA-seq), epigenomic single nucleotide polymorphism (SNP)-to-gene mapping, and GWAS summary statistics to infer potential cell types and diseases affected by genetic variations.

RESULTS

Using normal cell type programs in the sc-linker, we identified type 2 alveolar cells in normal lung tissues that are closely associated with non-small cell lung cancer (NSCLC). Additionally, we identified cancer-associated fibroblasts (CAFs) associated with lung cancer using disease-dependent programs. By integrating extensive single-cell data from NSCLC, we discerned heterogeneity among CAFs subgroups. Finally, using MAGMA, we identified RAB31 as a driver gene in disease-related fibroblasts. Proteins from the RAB family are involved in the dynamic regulation of cell membrane compartments and are dysregulated in various tumor types, potentially altering biological properties such as the proliferation, migration, and invasion of cancer cells. We found that the Ras-related protein Rab-31 (RAB31) was significantly overexpressed in tumor-associated fibroblasts compared to that in normal fibroblasts and was closely associated with poor prognosis in patients with NSCLC.

CONCLUSIONS

By integrating scRNA-seq, epigenomic, and GWAS datasets, we found that ACT2 and CAFs have specific disease heritability in lung cancer and identified the driver gene RAB31 as a potential therapeutic target.

The Role of Macrophages in Various Types of Tumors and the Possibility of Their Use as Targets for Antitumor Therapy

In solid tumors, tumor-associated macrophages (TAMs) are one of the most numerous populations and play an important role in the processes of tumor cell invasion, metastasis, and angiogenesis. Therefore, TAMs are considered promising diagnostic and prognostic biomarkers of tumors, and many attempts have been made to influence these cells as part of antitumor therapy. There are several key principles of action on ТАМs: the inhibition of monocyte/macrophage transition; the destruction of macrophages; the reprogramming of macrophage phenotypes (polarization of M2 macrophages to M1); the stimulation of phagocytic activity of macrophages and CAR-M therapy. Despite the large number of studies in this area, to date, there are no adequate approaches using antitumor therapy based on alterations in TAM functioning that would show high efficacy when administered in a mono-regimen for the treatment of malignant neoplasms. Studies devoted to the evaluation of the efficacy of drugs acting on TAMs are characterized by a small sample and the large heterogeneity of patient groups; in addition, in such studies, chemotherapy or immunotherapy is used, which significantly complicates the evaluation of the effectiveness of the agent acting on TAMs. In this review, we attempted to systematize the evidence on attempts to influence TAMs in malignancies such as lung cancer, breast cancer, colorectal cancer, cervical cancer, prostate cancer, gastric cancer, head and neck squamous cell cancer, and soft tissue sarcomas.

Germline alteration analysis reveals EPHB4R91H mutation as a key player in multiple primary lung tumors

Multiple primary lung tumors are garnering attention from clinicians, with adenocarcinoma emerging as the predominant histological type. Because of the heterogeneity and diffuse distribution of lesions in the same patient, the treatment of multiple primary lung adenocarcinoma (MPLA) is a significant challenge. As a kind of variation unaffected by tumor heterogeneity, germline alterations may play a key role in the development of MPLA. Here, whole-exome sequencing of peripheral blood was employed to obtain germline alteration data. Intergroup comparative analyses on rare and deleterious alterations of MPLA, solitary lung adenocarcinoma, and healthy individuals in an MPLA family were performed to clarify the candidate alterations. Whole-exome sequencing and targeted Sanger sequencing were performed in 27 disseminated MPLA patients to detect the mutation site that had been screened. A rare and deleterious germline alteration, EPHB4R91H, was found in all of the patients of an MPLA family and a patient with disseminated MPLA. It was revealed that EPHB4R91H was able to enhance the proliferation, migration, and invasion ability of A549 cells through increased binding affinity to ephrinB2, which in turn activated the EPHB4/ERK/JNK/MAPK pathway. Our findings corroborate that germline alterations are involved in the development of MPLA. And it was found for the first time that the EPHB4R91H mutation promotes the development of MPLA by enhancing its affinity for ephrinB2 and thereby active EPHB4/ERK/JNK/MAPK pathway.

Intratumoral Collagen Deposition Supports Angiogenesis Suggesting Anti-angiogenic Therapy in Armored and Cold Tumors

A previous study classifies solid tumors based on collagen deposition and immune infiltration abundance, identifying a refractory subtype termed armored & cold tumors, characterized by elevated collagen deposition and diminished immune infiltration. Beyond its impact on immune infiltration, collagen deposition also influences tumor angiogenesis. This study systematically analyzes the association between immuno-collagenic subtypes and angiogenesis across diverse cancer types. As a result, armored & cold tumors exhibit the highest angiogenic activity in lung adenocarcinoma (LUAD). Single-cell and spatial transcriptomics reveal close interactions and spatial co-localization of fibroblasts and endothelial cells. In vitro experiments demonstrate that collagen stimulates tumor cells to express vascular endothelial growth factor A (VEGFA) and directly enhances vessel formation and endothelial cell proliferation through sex determining region Y box 18 (SOX18) upregulation. Collagen inhibition via multiple approaches effectively suppresses tumor angiogenesis in vivo. In addition, armored & cold tumors display superior responsiveness to anti-angiogenic therapy in advanced LUAD cohorts. Post-immunotherapy resistance, the transformation into armored & cold tumors emerges as a potential biomarker for selecting anti-angiogenic therapy. In summary, collagen deposition is shown to drive angiogenesis across various cancers, providing a novel and actionable framework to refine therapeutic strategies combining chemotherapy with anti-angiogenic treatments.

Unveiling the immunomodulatory dance: endothelial cells' function and their role in non-small cell lung cancer

The dynamic interactions between tumor endothelial cells (TECs) and the immune microenvironment play a critical role in the progression of non-small cell lung cancer (NSCLC). In general, endothelial cells exhibit diverse immunomodulatory properties, influencing immune cell recruitment, antigen presentation, and regulation of immune checkpoint expression. Understanding the multifaceted roles of TECs as well as assigning specific functional hallmarks to various TEC phenotypes offer new avenues for targeted development of therapeutic interventions, particularly in the context of advanced immunotherapy and anti-angiogenic treatments. This review provides insights into the complex interplay between TECs and the immune system in NSCLC including discussion of potential optimized therapeutic opportunities.

FHL2 expression by cancer-associated fibroblasts promotes metastasis and angiogenesis in lung adenocarcinoma

Cancer-associated fibroblasts (CAFs) contribute to the progression of lung cancer. Four and a half LIM domain protein-2 (FHL2) is a component of focal adhesion structures. We analyzed the function of FHL2 expressed by CAFs in lung adenocarcinoma. Expression of FHL2 in fibroblast subtypes was investigated using database of single-cell RNA-sequencing of lung cancer tissue. The role of FHL2 in the proliferation and migration of CAFs was assessed. The effects of FHL2 knockout on the migration and invasion of human lung adenocarcinoma cells and tube formation of endothelial cells induced by CAF-conditioned medium (CM) were evaluated. The effect of FHL2 knockout in CAFs on metastasis was determined using a murine orthotopic lung cancer model. The prognostic significance of stromal FHL2 was assessed by immunohistochemistry in human adenocarcinoma specimens. FHL2 is highly expressed in myofibroblasts in cancer tissue. TGF-β1 upregulated FHL2 expression in CAFs and FHL2 knockdown attenuated CAF proliferation. FHL2 knockout reduced CAF induced migration of A110L and H23 human lung adenocarcinoma cell lines, and the induction of tube formation of endothelial cells. FHL2 knockout reduced CAF-induced metastasis of lung adenocarcinomas in an orthotopic model in vivo. The concentration of Osteopontin (OPN) in CM from CAF was downregulated by FHL2 knockout. siRNA silencing and antibody blocking of OPN reduced the pro-migratory effect of CM from CAF on lung cancer cells. In resected lung adenocarcinoma specimens, positive stromal FHL2 expression was significantly associated with higher microvascular density and worse prognosis. In conclusion, FHL2 expression by CAFs enhances the progression of lung adenocarcinoma by promoting angiogenesis and metastasis.

Metabolically activated and highly polyfunctional intratumoral VISTA+ regulatory B cells are associated with tumor recurrence in early-stage NSCLC

B cells have emerged as central players in the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC). However, although there is clear evidence for their involvement in cancer immunity, scanty data exist on the characterization of B cell phenotypes, bioenergetic profiles and possible interactions with T cells in the context of NSCLC. In this study, using polychromatic flow cytometry, mass cytometry, and spatial transcriptomics we explored the intricate landscape of B cell phenotypes, bioenergetics, and their interaction with T cells in NSCLC. Our analysis revealed that TME contains diverse B cell clusters, including VISTA+ Bregs, with distinct metabolic and functional profiles. Target liquid chromatography-tandem mass spectrometry confirmed the expression of VISTA on B cells. VISTA+ Bregs displayed high metabolic demand and were able to produce different cytokines, including interleukin (IL)-10, transforming growth factor (TGF)-β, IL-6, tumor necrosis factor (TNF), and granulocyte-macrophage colony-stimulating factor (GM-CSF). Spatial analysis showed colocalization of B cells with CD4+/CD8+ T lymphocytes in TME. The computational analysis of intercellular communications that links ligands to target genes, performed by NicheNet, predicted B-T interactions via VISTA-PSGL-1 axis. Colocalization analyses revealed that PSGL-1 T cells and VISTA+ B cells are adjacent in the TME. Notably, tumor infiltrating CD8+ T cells expressing PSGL-1 exhibited enhanced metabolism and cytotoxicity. In NSCLC patients, prediction analysis performed by PENCIL revealed the presence of an association between PSGL-1+CD8+ T cells and VISTA+ Bregs with lung recurrence. Our findings suggest a potential interaction between Bregs and T cells through the VISTA-PSGL-1 axis, that could influence NSCLC recurrence.

Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma

BACKGROUND

Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients.

METHODS

DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, "timeROC", and validated using GEO datasets.

RESULTS

Analysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy.

CONCLUSION

We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD.

KRASG12D drives immunosuppression in lung adenocarcinoma through paracrine signaling

Lung cancer is the leading cause of cancer deaths in the United States. New targeted therapies against the once-deemed undruggable oncogenic KRAS are changing current therapeutic paradigms. However, resistance to targeted KRAS inhibitors almost inevitably occurs; resistance can be driven by tumor cell-intrinsic changes or by changes in the microenvironment. Here, we utilized a genetically engineered mouse model of KRASG12D-driven lung cancer that allows for inducible and reversible expression of the oncogene: activation of oncogenic KRASG12D induces tumor growth; conversely, inactivation of KRASG12D causes tumor regression. We showed that in addition to regulating cancer cell growth and survival, oncogenic KRAS regulated the transcriptional status of cancer-associated fibroblasts and macrophages in this model. Utilizing ex vivo approaches, we showed that secreted factors from cancer cells induced the expression of multiple cytokines in lung fibroblasts, and in turn drove expression of immunosuppressive factors, such as arginase 1, in macrophages. In summary, fibroblasts emerged as a key source of immune regulatory signals, and a potential therapeutic target for improving the efficacy of KRAS inhibitors in lung cancer.

Macrophage heterogeneity and oncogenic mechanisms in lung adenocarcinoma: insights from scRNA-seq analysis and predictive modeling

Background

Macrophages play a dual role in the tumor microenvironment(TME), capable of secreting pro-inflammatory factors to combat tumors while also promoting tumor growth through angiogenesis and immune suppression. This study aims to explore the characteristics of macrophages in lung adenocarcinoma (LUAD) and establish a prognostic model based on macrophage-related genes.

Method

We performed scRNA-seq analysis to investigate macrophage heterogeneity and their potential pseudotime evolutionary processes. Specifically, we used scRNA-seq data processing, intercellular communication analysis, pseudotime trajectory analysis, and transcription factor regulatory analysis to reveal the complexity of macrophage subpopulations. Data from The Cancer Genome Atlas (TCGA) was used to assess the impact of various macrophage subtypes on LUAD prognosis. Univariate Cox regression was applied to select prognostic-related genes from macrophage markers. We constructed a prognostic model using Lasso regression and multivariate Cox regression, categorizing LUAD patients into high and low-risk groups based on the median risk score. The model's performance was validated across multiple external datasets. We also examined differences between high and low-risk groups in terms of pathway enrichment, mutation information, tumor microenvironment(TME), and immunotherapy efficacy. Finally, RT-PCR confirmed the expression of model genes in LUAD, and cellular experiments explored the carcinogenic mechanism of COL5A1.

Results

We found that signals such as SPP1 and MIF were more active in tumor tissues, indicating potential oncogenic roles of macrophages. Using macrophage marker genes, we developed a robust prognostic model for LUAD that effectively predicts prognosis and immunotherapy efficacy. A nomogram was constructed to predict LUAD prognosis based on the model's risk score and other clinical features. Differences between high and low-risk groups in terms of TME, enrichment analysis, mutational landscape, and immunotherapy efficacy were systematically analyzed. RT-PCR and cellular experiments supported the oncogenic role of COL5A1.

Conclusion

Our study identified potential oncogenic mechanisms of macrophages and their impact on LUAD prognosis. We developed a prognostic model based on macrophage marker genes, demonstrating strong performance in predicting prognosis and immunotherapy efficacy. Finally, cellular experiments suggested COL5A1 as a potential therapeutic target for LUAD.

Proton pump inhibitor attenuates acidic microenvironment to improve the therapeutic effects of MSLN-CAR-T cells on the brain metastasis

The incidence of brain metastasis (BM) is gradually increasing, and the prognosis and therapeutic effect are poor. The emergence of immunotherapy has brought hope for the development of BM treatments. This study revealed that compared with primary cancers, BMs have a colder and more acidic tumor microenvironment (TME), resulting in reduced protein levels of mesothelin (MSLN), a promising target for chimeric antigen receptor-T (CAR-T) cell therapy for triple-negative breast cancer (TNBC) with BMs. These factors could significantly decrease the efficiency of MSLN-CAR-T cells in TNBC BMs. Pantoprazole (PPZ) administration at the most commonly used dose in the clinic notably increased the pH of the TME, inhibited lysosomal activity, increased the membrane levels of the MSLN protein and improved the killing ability of MSLN-CAR-T cells both in vitro and in vivo. Similar results were obtained in non-small cell lung cancer BMs. Hence, when administered in combination with CAR-T cells, PPZ, which increases the protein levels of target antigens, may constitute a new immunotherapeutic strategy for treating solid tumors with BMs.

High-dose intravenous BCG vaccination induces enhanced immune signaling in the airways

Intradermal Bacillus Calmette-Guérin (BCG) is the most widely administered vaccine, but it does not sufficiently protect adults against pulmonary tuberculosis. Recent studies in nonhuman primates show that intravenous BCG administration offers superior protection against Mycobacterium tuberculosis (Mtb). We used single-cell analysis of bronchoalveolar lavage cells from rhesus macaques vaccinated via different routes and doses of BCG to identify alterations in the immune ecosystem in the airway following vaccination. Our findings reveal that high-dose intravenous BCG induces an influx of polyfunctional T cells and macrophages in the airways, with alveolar macrophages from high-dose intravenous BCG displaying a basal activation state in the absence of purified protein derivative stimulation, defined in part by interferon signaling. Enhanced intercellular immune signaling and stronger T helper 1-T helper 17 transcriptional responses were observed following purified protein derivative stimulation. These results suggest that high-dose intravenous BCG vaccination creates a specialized immune environment that primes airway cells for effective Mtb clearance.

Integrated bioinformatics analysis identifies ALDH18A1 as a prognostic hub gene in glutamine metabolism in lung adenocarcinoma

Glutamine metabolism is pivotal in cancer biology, profoundly influencing tumor growth, proliferation, and resistance to therapies. Cancer cells often exhibit an elevated dependence on glutamine for essential functions such as energy production, biosynthesis of macromolecules, and maintenance of redox balance. Moreover, altered glutamine metabolism can contribute to the formation of an immune-suppressive tumor microenvironment characterized by reduced immune cell infiltration and activity. In this study on lung adenocarcinoma, we employed consensus clustering and applied 101 types of machine learning methods to systematically identify key genes associated with glutamine metabolism and develop a risk model. This comprehensive approach provided a clearer understanding of how glutamine metabolism associates with cancer progression and patient outcomes. Notably, we constructed a robust nomogram based on clinical information and patient risk scores, which achieved a stable area under the curve (AUC) greater than 0.8 for predicting patient survival across four datasets, demonstrating high predictive accuracy. This nomogram not only enhances our ability to stratify patient risk but also offers potential targets for therapeutic intervention aimed at disrupting glutamine metabolism and sensitizing tumors to existing treatments. Moreover, we identified ALDH18A1 as a prognostic hub gene of glutamine metabolism, characterized by high expression levels in glutamine cluster 3, which is associated with poor clinical outcomes and worse survival, and is included in the risk model. Such insights underscore the critical role of glutamine metabolism in cancer and highlight avenues for personalized medicine in oncology research.

A Novel Competing Endogenous RNA Network Reveals Potential Mechanisms and Biomarkers of Chemoresistance in Lung Adenocarcinoma

Platinum resistance is a common cause of chemotherapy failure in lung adenocarcinoma (LUAD). Competing endogenous RNAs (ceRNAs), which function by competitively binding to miRNAs, can influence drug response. However, the regulatory mechanisms of ceRNAs underlying chemoresistance in LUAD remain largely unknown. Here, we proposed an integrated pipeline combining limma algorithm, miRNA binding prediction algorithm, expression correlation model and experimental support to identify functional lncRNA-miRNA-mRNA competing triplets associated with resistance, which showed variable competing patterns between resistant and sensitive cells. We found that a minority of altered ceRNAs overlapped in multiple types of cisplatin-resistant LUAD cell lines and were involved in biological processes known to mediate cancer drug response. We identified them as core resistance factors, forming a novel lncRNA-mediated resistance-related ceRNA network, which indicated a potential mechanism. Single-cell analysis revealed that these resistance-related ceRNAs regulated the functional states of LUAD cells, and survival analysis showed that they contributed to the prognosis of LUAD patients. The lncRNA regulators H19 and MIR193BHG were found to correlate with cisplatin activity in LUAD cell lines, and dysregulation of their expression triggered disorders of cisplatin response-related functions through multiple ceRNA regulatory axes in this network, suggesting them as potential resistance biomarkers and therapeutic targets. In summary, the integrated pipeline and the resulting data serve as a valuable resource for understanding the ceRNA mechanisms of chemoresistance and improving chemotherapy response.

Single-cell transcriptomic analysis of the senescent microenvironment in bone metastasis

Bone metastasis (BM) is a mortality-related event of late-stage cancer, with non-small cell lung cancer (NSCLC) being a common origin for BM. However, the detailed molecular profiling of the metastatic bone ecosystem is not fully understood, hindering the development of effective therapies for advanced patients. In this study, we examined the cellular heterogeneity between primary tumours and BM from tissues and peripheral blood by single-cell transcriptomic analysis, which was verified using multiplex immunofluorescence staining and public datasets. Our results demonstrate a senescent microenvironment in BM tissues of NSCLC. BM has a significantly higher infiltration of malignant cells with senescent characteristics relative to primary tumours, accompanied by aggravated metastatic properties. The endothelial-mesenchymal transition involved with SOX18 activation is related to the cellular senescence of vascular endothelial cells from BM. CD4Tstr cells, with pronounced stress and senescence states, are preferentially infiltrated in BM, indicating stress-related dysfunction contributing to the immunocompromised environment during tumour metastasis to bone. Moreover, we identify the SPP1 pathway-induced cellular crosstalk among T cells, vascular ECs and malignant cells in BM, which activates SOX18 and deteriorates patient survival. Our findings highlight the roles of cellular senescence in modulating the microenvironment of BM and implicate anti-senescence therapy for advanced NSCLC patients.

CCDC34 maintains stemness phenotype through β-catenin-mediated autophagy and promotes EGFR-TKI resistance in lung adenocarcinoma

Despite recent advances in treatment strategy, lung cancer remains the leading cause of cancer-related mortality worldwide, and it is a serious threat to human health. Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, and approximately 40-50% of patients with LUAD in Asian populations have epidermal growth factor receptor (EGFR) mutations. The use of EGFR tyrosine kinase inhibitors (EGFR-TKIs) has revolutionarily improved the prognosis of patients with EGFR-mutated LUAD. However, acquired drug resistance is the main cause of treatment failure. Therefore, new therapeutic strategies are necessary to address the resistance to EGFR-TKIs in patients with LUAD. Cancer stemness-related factors lead to multiple-drug resistance in cancer treatment, including EGFR-TKI resistance. Coiled-coil domain-containing 34 (CCDC34) serves as an oncogene in several types of cancer. However, the role and molecular mechanism of CCDC34 in the malignant progression of LUAD have not been reported to date. In the present study, we found that CCDC34 may be associated with LUAD stemness through weighted gene co-expression network analysis (WGCNA). Furthermore, we demonstrated that CCDC34 promoted LUAD stemness properties through β-catenin-mediated regulation of ATG5-induced autophagy, which was conducive to acquired EGFR-TKI resistance in LUAD in vitro and in vivo. Knockdown CCDC34 can synergistically inhibit tumor growth when combined with EGFR-TKIs. This study reveals a positive association between CCDC34 and the stemness phenotype of LUAD, providing new insights into overcoming EGFR-TKI resistance in LUAD by inhibiting CCDC34 expression.

4‑Methoxydalbergione inhibits the tumorigenesis and metastasis of lung cancer through promoting ferroptosis via the DNMT1/system Xc‑/GPX4 pathway

Lung cancer is responsible for the highest number of tumor‑related deaths worldwide. A flavonoid extracted from the heartwood of Dalbergia sissoo Roxb., 4‑methoxydalbergione (4‑MD), exhibits potent anticancer activity in multiple malignancies; however, the potential anticancer activity of 4‑MD in lung cancer has not yet been elucidated. In the present study, A549 cells were treated with increasing concentrations of 4‑MD, and cell viability was assessed using a Cell Counting Kit‑8 assay. In addition, colony formation, 5‑ethynyl‑2'‑deoxyuridine, wound healing and Transwell assays were conducted to evaluate cell proliferation, migration and invasion, respectively. Cell morphology was observed using transmission electron microscopy, and ferroptosis was determined using thiobarbituric acid reactive substance, lipid reactive oxygen species (ROS) and iron assays. Moreover, molecular docking was used to verify the potential interaction between 4‑MD and DNA methyltransferase 1 (DNMT1). Tumor‑bearing mice were established and treated with 10 or 30 mg/kg 4‑MD, and tumor volume and weight were recorded. Immunohistochemistry and Prussian blue staining were conducted to examine Ki‑67 expression and iron deposition in tumor tissues, and protein expression was further explored using western blot analysis. The results of the present study revealed that 4‑MD significantly inhibited cell proliferation, migration, invasion and epithelial‑mesenchymal transition in a concentration‑dependent manner. Notably, 4‑MD induced ferroptosis via increased lipid peroxidation, lipid ROS and Fe2+ levels. In addition, it was revealed that 4‑MD can directly bind to DNMT1 to inhibit expression, and inhibit solute carrier family 7 member 11 (SLC7A11; also known as cystine‑glutamate antiporter) and glutathione peroxidase 4 expression. Following DNMT1 overexpression, the observed antitumor activity and ferroptosis‑promoting effects of 4‑MD were partially reversed. Furthermore, 4‑MD significantly inhibited tumor growth in vivo, and reduced tumor volume and weight. In addition, Ki‑67 expression was reduced while iron deposition was increased in the tumor tissues of mice following treatment with 4‑MD. In conclusion, 4‑MD may exhibit anticancer activity through the promotion of DNMT1‑mediated cell ferroptosis. Thus, 4‑MD may have potential as a novel therapeutic agent in the treatment of lung cancer.

Construction of a neutrophil extracellular trap formation-related gene model for predicting the survival of lung adenocarcinoma patients and their response to immunotherapy

Background

Lung adenocarcinoma (LUAD) is associated with high morbidity and mortality rates. Increasing evidence indicates that neutrophil extracellular traps (NETs) play a critical role in tumor progression, metastasis and immunosuppression in the LUAD tumor microenvironment (TME). Nevertheless, the use of NET formation-related genes (NFRGs) to predict LUAD patient survival and response to immunotherapy has not been explored. Therefore, this study aimed to construct a NFRGs-based prognostic signature for stratifying LUAD patients and informing individualized management strategies.

Methods

The cell composition of the LUAD TME was investigated using the single-cell sequencing data in Single-Cell Lung Cancer Atlas (LuCA). NFRGs were identified to construct a prognostic signature based on The Cancer Genome Atlas (TCGA) cohort which was validated in the Gene Expression Omnibus (GEO) dataset. The univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression models, receiver operating characteristic (ROC) and Brier Score were applied to assess the prognostic model. A nomogram was established to facilitate the clinical application of the risk score. The Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATE) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were utilized to assess the TME and predict immunotherapy response. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was applied to quantify the expression levels of four NFRGs in LUAD paired tissue samples.

Results

Single‑cell RNA sequence analysis showed the importance of neutrophils in LUAD TME. We developed and validated a 4-NFRG (CAT, CTSG, ENO1, TLR2) prognostic signature based on TCGA and GEO cohorts, which stratified patients into high-risk and low-risk groups. Univariate and multivariate analyses showed that our risk model could independently predict the survival of LUAD patients. Patients in the low-risk group exhibited a more active immune microenvironment, lower TIDE scores, lower half-maximal inhibitory concentration (IC50) values and higher immune checkpoint molecule expression. Our risk signature could serve as a biomarker for predicting immunotherapeutic benefits.

Conclusions

We developed a novel prognostic signature for LUAD patients based on NFRGs and emphasized the critical role of this signature in predicting LUAD patient survival and immunotherapy response.

Unveiling chemotherapy-induced immune landscape remodeling and metabolic reprogramming in lung adenocarcinoma by scRNA-sequencing

Chemotherapy is widely used to treat lung adenocarcinoma (LUAD) patients comprehensively. Considering the limitations of chemotherapy due to drug resistance and other issues, it is crucial to explore the impact of chemotherapy and immunotherapy on these aspects. In this study, tumor samples from nine LUAD patients, of which four only received surgery and five received neoadjuvant chemotherapy, were subjected to scRNA-seq analysis. In vitro and in vivo assays, including flow cytometry, immunofluorescence, Seahorse assay, and tumor xenograft models, were carried out to validate our findings. A total of 83,622 cells were enrolled for subsequent analyses. The composition of cell types exhibited high heterogeneity across different groups. Functional enrichment analysis revealed that chemotherapy drove significant metabolic reprogramming in tumor cells and macrophages. We identified two subtypes of macrophages: Anti-mac cells (CD45+CD11b+CD86+) and Pro-mac cells (CD45+CD11b+ARG +) and sorted them by flow cytometry. The proportion of Pro-mac cells in LUAD tissues increased significantly after neoadjuvant chemotherapy. Pro-mac cells promote tumor growth and angiogenesis and also suppress tumor immunity. Moreover, by analyzing the remodeling of T and B cells induced by neoadjuvant therapy, we noted that chemotherapy ignited a relatively more robust immune cytotoxic response toward tumor cells. Our study demonstrates that chemotherapy induces metabolic reprogramming within the tumor microenvironment of LUAD, particularly affecting the function and composition of immune cells such as macrophages and T cells. We believe our findings will offer insight into the mechanisms of drug resistance and provide novel therapeutic targets for LUAD in the future.