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

Exploring the Complexity of Pan-Cancer: Gene Convergences and in silico Analyses

Cancer is a complex and multifaceted group of diseases characterized by highly intricate mechanisms of tumorigenesis and tumor progression, which complicates diagnosis, prognosis, and treatment. In recent years, targeted therapies have gained prominence by focusing on specific mutations and molecular features unique to each tumor type, offering more effective and personalized treatment options. However, it is equally critical to explore the genetic commonalities across different types of cancer, which has led to the rise of pan-cancer studies. These approaches help identify shared therapeutic targets across various tumor types, enabling the development of broader and potentially more widely applicable treatment strategies. This review aims to provide a comprehensive overview of key concepts related to tumors, including tumorigenesis processes, the tumor microenvironment, and the role of extracellular vesicles in tumor biology. Additionally, we explore the molecular interactions and mechanisms driving tumor progression, with a particular focus on the pan-cancer perspective. To achieve this, we conducted an in silico analysis using publicly available datasets, which facilitated the identification of both common and divergent genetic and molecular patterns across different tumor types. By integrating these diverse areas, this review offers a clearer and deeper understanding of the factors influencing tumorigenesis and highlights potential therapeutic targets.

TLS and immune cell profiling: immunomodulatory effects of immunochemotherapy on tumor microenvironment in resectable stage III NSCLC

Background

The use of programmed death-1 (PD-1) inhibitors in the neoadjuvant setting for patients with resectable stage III NSCLC has revolutionized this field in recent years. However, there is still 40%-60% of patients do not benefit from this approach. The complex interactions between immune cell subtypes and tertiary lymphoid structures (TLSs) within the tumor microenvironment (TME) may influence prognosis and the response to immunochemotherapy. This study aims to assess the relationship between immune cells subtypes and TLSs to better understand their impact on immunotherapy response.

Methods

This study initially compared the tertiary lymphoid structures (TLSs) density among patients who underwent immunochemotherapy, chemotherapy and upfront surgery using 123 tumor samples from stage-matched patients. Multiplex immunohistochemistry (mIHC) was employed to analyze the spatial distribution of PD-L1+CD11c+ cells and PD1+CD8+ T cells within TLSs. Cytometry by time-of-flight (CyTOF) was used to assess immune cell dynamics in paired biopsy and resection specimens from six patients who underwent immunochemotherapy. Key immune cells were validated in newly collected samples using flow cytometry, mIHC, and in vitro CAR-T cells model.

Results

Patients who underwent neoadjuvant chemotherapy or immunochemotherapy exhibited increased TLSs compared to those who opted for upfront surgery. The TLS area-to-tumor area ratio distinguished pCR+MPR and NR patients in the immunochemotherapy group. Spatial analysis revealed variations in the distance between PD-L1+CD11c+ cells and PD1+CD8+ T cells within TLSs in the immunochemotherapy group. CyTOF analysis revealed an increase in the frequency of key immune cells (CCR7+CD127+CD69+CD4+ and CD38+CD8+ cells) following combined therapy. Treatment responders exhibited an increase in CCR7+CD4+ T cells, whereas CD38+CD8+ T cells were associated with compromised treatment effectiveness.

Conclusions

Immunochemotherapy and chemotherapy increase TLSs and granzyme B+ CD8+ T cells in tumors. The TLS area-to-tumor ratio distinguishes responders from non-responders, with PD-L1+ dendritic cells near CD8+PD-1+ T cells linked to efficacy, suggesting that PD-1 inhibitors disrupt harmful interactions. Post-immunochemotherapy, CD8+ T cells increase, but CD38+CD8+ T cells show reduced functionality. These findings highlight the complex immune dynamics and their implications for NSCLC treatment.

Immunological roles for resistin and related adipokines in obesity-associated tumors

Rationale Obesity is an independent risk factor for the occurrence and development of tumors. Obesity is influenced by signaling of adipokines, which are secreted factors from adipocytes and resident immune cells within adipose tissues that mediate lipid metabolism. More recently, adipokines have been implicated in chronic inflammation as well as in tumor formation and growth. Among them, resistin has received increasing attention in research related to the growth and expansion of solid tumors and hematological cancers through various signaling pathways. Objective and findings We reviewed the physiological, biochemical, and immune functions of adipose tissue, with a focus on the structure and expression of resistin and adipokines within multiple adipose cell types, their signaling pathways and putative effects on tumor cells, as well as their in vivo regulation. Current evidence indicates that adipokines such as resistin act as pro-inflammatory factors to stimulate immune cells which, in turn, promotes tumor angiogenesis, connective tissue proliferation, and matrix fibrosis. Concurrently, in states of metabolic dysfunction and lipotoxicity in obese individuals, the numbers and functions of immune cells are compromised, leading to an immunosuppressive environment that fosters tumor cell survival and weak cancer immune monitoring. Conclusion Adipokines such as resistin are important to the development of obesity-related tumors. Clarifying the roles for obesity-related factors in immune regulation and tumor progression may lead to the discovery of novel anti-tumor strategies for targeting obesity factors such as resistin to limit tumor growth and manage obesity, or both.

ALDH2 attenuates radiation-induced lung injury by inhibiting ROS and epithelial-mesenchymal transition mediated by the TGF-β1/Smad pathway

Radiation-induced lung injury is a significant complication of thoracic malignant tumor radiotherapy, yet effective treatments remain scarce. Aldehyde dehydrogenase 2 (ALDH2) possesses antioxidant and anti-inflammatory properties, but its specific role in radiation-induced lung injury is not well understood. This study aimed to investigate the impact of ALDH2 on radiation-induced lung injury and elucidate the underlying mechanisms. Through analysis of radiation-induced lung injury datasets, intervention with ALDH2 agonists and inhibitors in an in vivo radiation-induced lung injury model, and establishment of an in vitro radiation-induced lung injury model using A549 stable cells with varying ALDH2 expressions, we discovered that ALDH2 expression is reduced in radiation-induced lung injury. Enrichment analysis suggested that ALDH2 may mitigate radiation-induced lung injury by modulating oxidative stress and inflammation levels. Additionally, single-cell data analysis reveals that ALDH2 is primarily localized in myeloid macrophages within the lungs, with its expression also being reduced in lung cancer patients. Subsequent examination of mouse pathological sections, reactive oxygen species (ROS), and inflammatory factor levels confirmed that ALDH2 can lessen radiation-induced lung injury by suppressing ROS and inflammatory factors. Both in vivo and in vitro Western blot analysis further validated that ALDH2 can attenuate epithelial-mesenchymal transition and inhibit the TGF-β1/Smad pathway. Therefore, ALDH2 shows promise in reducing radiation-induced lung injury by inhibiting ROS and TGF-mediated epithelial-mesenchymal transition, making it a potential target for the treatment of radiation-induced lung injury.

The convergence of mTOR signaling and ethanol teratogenesis

Ethanol is one of the most common teratogens and causes of human developmental disabilities. Fetal alcohol spectrum disorders (FASD), which describes the wide range of deficits due to prenatal ethanol exposure, are estimated to affect between 1.1 % and 5.0 % of births in the United States. Ethanol dysregulates numerous cellular mechanisms such as programmed cell death (apoptosis), protein synthesis, autophagy, and various aspects of cell signaling, all of which contribute to FASD. The mechanistic target of rapamycin (mTOR) regulates these cellular mechanisms via sensing of nutrients like amino acids and glucose, DNA damage, and growth factor signaling. Despite an extensive literature on ethanol teratogenesis and mTOR signaling, there has been less attention paid to their interaction. Here, we discuss the impact of ethanol teratogenesis on mTORC1's ability to coordinate growth factor and amino acid sensing with protein synthesis, autophagy, and apoptosis. Notably, the effect of ethanol exposure on mTOR signaling depends on the timing and dose of ethanol as well as the system studied. Overall, the overlap between the functions of mTORC1 and the phenotypes observed in FASD suggest a mechanistic interaction. However, more work is required to fully understand the impact of ethanol teratogenesis on mTOR signaling.

Identification of psoriasis-associated immune marker G3BP2 through single-cell RNA sequencing and meta analysis

Psoriasis is a chronic skin disease with an increasing prevalence each year. However, the mechanisms underlying its onset and progression remain unclear, and effective therapeutic targets are lacking. Therefore, we employs an innovative approach by combining single-cell RNA sequencing (scRNA-seq) with meta-analysis. This not only elucidates the potential mechanisms of psoriasis at the cellular level but also identifies immunoregulatory marker genes that play a statistically significant role in driving psoriasis progression through comprehensive analysis of multiple datasets. Skin tissue samples from 12 psoriasis patients underwent scRNA-seq, followed by quality control, filtering, PCA dimensionality reduction, and tSNE clustering analysis to identify T cell subtypes and differentially expressed genes (DEGs) in psoriatic skin tissue. Next, three psoriasis datasets were standardised and merged to identify differentially expressed genes (DEGs). Subsequently, weighted gene co-expression network analysis (WGCNA) was applied for clustering analysis of gene co-expression network modules and to assess the correlation between these modules and DEGs. Least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curve analyses were conducted to select disease-specific genes and evaluate their diagnostic value. Single-cell data revealed nine cell types in psoriatic skin tissue, with seven T cell subtypes identified. Intersection analysis identified ADAM8 and G3BP2 as key genes. Through the integration of scRNA-seq and Meta analysis, we identified the immunoregulatory marker gene G3BP2, which is associated with the onset and progression of psoriasis and holds clinical significance. G3BP2 is speculated to promote the development of psoriasis by increasing the proportion of CD8+ T cells.

Integrated proteomic and glycoproteomic analysis reveals heterogeneity and molecular signatures of brain metastases from lung adenocarcinomas

Brain metastasis is a major cause of poor prognosis and death in lung adenocarcinoma (LUAD); however, the understanding of therapeutic strategies and mechanisms for brain metastases from LUAD (BM-LUAD) remains notably limited, especially at the proteomics levels. To address this issue, we conducted integrated proteomic and glycoproteomic analyses on 49 BM-LUAD tumors, revealing two distinct subtypes of the disease: BM-S1 and BM-S2. Whole exome sequencing analysis revealed that somatic mutations in STK11 and KEAP1, as well as copy number deletions on chr19p13.3, such as STK11, UQCR11, and SLC25A23, were more frequently detected in BM-S2. In BM-S1 tumors, we observed significant infiltration of GFAP + astrocytes, as evidenced by elevated levels of GFAP, GABRA2, GABRG1 and GAP43 proteins and an enrichment of astrocytic signatures in both our proteomic data and external spatial transcriptomic data. Conversely, BM-S2 tumors demonstrated higher levels of PD-1 immune cell infiltration, supported by the upregulation of PD-1 and LAG-3 genes. These findings suggest distinct microenvironmental adaptations required by the different BM-LUAD subtypes. Additionally, we observed unique glycosylation patterns between the subtypes, with increased fucosylation in BM-S1 and enhanced sialylation in BM-S2, primarily affected by glycosylation enzymes such as FUT9, B4GALT1, and ST6GAL1. Specifically, in BM-S2, these sialylation modifications are predominantly localized to the lysosomes, underscoring the critical role of N-glycosylation in the tumor progression of BM-LUAD. Overall, our study not only provides a comprehensive multi-omic data resource but also offers valuable biological insights into BM-LUAD, highlighting potential mechanisms and therapeutic targets for further investigation.

EGFR and MUC1 as dual-TAA drug targets for lung cancer and colorectal cancer

Background

Epidermal growth factor receptor (EGFR) is a key protein in cellular signaling that is overexpressed in many human cancers, making it a compelling therapeutic target. On-target severe skin toxicity has limited its clinical application. Dual-targeting therapy represents a novel approach to overcome the challenges of EGFR-targeted therapies.

Methods

A single-cell tumor-normal RNA transcriptomic meta-atlas of lung adenocarcinoma (LUAD) and normal lung tissues was constructed from published data. Tumor associated antigens (TAAs) were screened from the genes which were expressed on cell surface and could distinguish cancer cells from normal cells. Expression of MUC1 and EGFR in tumors and normal tissues was detected by immunohistochemistry (IHC), bulk transcriptomic and single-cell transcriptomic analyses. RNA cut-off values were calculated using paired analysis of RNA sequencing and IHC in patient-derived tumor xenograft samples. They were used to estimate the abundance of EGFR- and MUC-positive subjects in The Cancer Genome Atlas Program (TCGA) database. Survival analysis of EGFR and MUC1 expression was carried out using the transcription and clinical data from TCGA.

Results

A candidate TAA target, transmembrane glycoprotein mucin 1 (MUC1), showed strong expression in cancer cells and low expression in normal cells. Single-cell analysis suggested EGFR and MUC1 together had better tumor specificity than the combination of EGFR with other drug targets. IHC data confirmed that EGFR and MUC1 were highly expressed on LUAD and colorectal cancer (CRC) clinical samples but not on various normal tissues. Notably, co-expression of EGFR and MUC1 was observed in 98.4% (n=64) of patients with LUAD and in 91.6% (n=83) of patients with CRC. It was estimated that EGFR and MUC1 were expressed in 97.5% of LUAD samples in the TCGA dataset. Besides, high expression of EGFR and MUC1 was significantly associated with poor prognosis of LUAD and CRC patients.

Conclusions

Single-cell RNA, bulk RNA and IHC data demonstrated the high expression levels and co-expression patterns of EGFR and MUC1 in tumors but not normal tissues. Therefore, it is a promising TAA combination for therapeutic targeting which could enhance on-tumor efficacy while reducing off-tumor toxicity.

Integrated analyses of multi-omic data derived from paired primary lung cancer and brain metastasis reveal the metabolic vulnerability as a novel therapeutic target

BACKGROUND

Lung cancer brain metastases (LC-BrMs) are frequently associated with dismal mortality rates in patients with lung cancer; however, standard of care therapies for LC-BrMs are still limited in their efficacy. A deep understanding of molecular mechanisms and tumor microenvironment of LC-BrMs will provide us with new insights into developing novel therapeutics for treating patients with LC-BrMs.

METHODS

Here, we performed integrated analyses of genomic, transcriptomic, proteomic, metabolomic, and single-cell RNA sequencing data which were derived from a total number of 154 patients with paired and unpaired primary lung cancer and LC-BrM, spanning four published and two newly generated patient cohorts on both bulk and single cell levels.

RESULTS

We uncovered that LC-BrMs exhibited a significantly greater intra-tumor heterogeneity. We also observed that mutations in a subset of genes were almost always shared by both primary lung cancers and LC-BrM lesions, including TTN, TP53, MUC16, LRP1B, RYR2, and EGFR. In addition, the genome-wide landscape of somatic copy number alterations was similar between primary lung cancers and LC-BrM lesions. Nevertheless, several regions of focal amplification were significantly enriched in LC-BrMs, including 5p15.33 and 20q13.33. Intriguingly, integrated analyses of transcriptomic, proteomic, and metabolomic data revealed mitochondrial-specific metabolism was activated but tumor immune microenvironment was suppressed in LC-BrMs. Subsequently, we validated our results by conducting real-time quantitative reverse transcription PCR experiments, immunohistochemistry, and multiplexed immunofluorescence staining of patients' paired tumor specimens. Therapeutically, targeting oxidative phosphorylation with gamitrinib in patient-derived organoids of LC-BrMs induced apoptosis and inhibited cell proliferation. The combination of gamitrinib plus anti-PD-1 immunotherapy significantly improved survival of mice bearing LC-BrMs. Patients with a higher expression of mitochondrial metabolism genes but a lower expression of immune genes in their LC-BrM lesions tended to have a worse survival outcome.

CONCLUSIONS

In conclusion, our findings not only provide comprehensive and integrated perspectives of molecular underpinnings of LC-BrMs but also contribute to the development of a potential, rationale-based combinatorial therapeutic strategy with the goal of translating it into clinical trials for patients with LC-BrMs.

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods. First, they do not fully exploit cell-to-cell differential features. Second, they are developed based on shallow features and lack of flexibility in integrating high-order features in the data. Finally, the low-dimensional gene features may lead to overfitting in neural networks. To overcome those limitations, we propose a novel DL-based model, cell type annotation of single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning (scRGCL), based on residual graph convolutional neural network and contrastive learning for cell type annotation of single-cell RNA-seq data. scRGCL mainly consists of a residual graph convolutional neural network, contrastive learning, and weight freezing. A residual graph convolutional neural network is utilized to extract complex high-order features from data. Contrastive learning can help the model learn meaningful cell-to-cell differential features. Weight freezing can avoid overfitting and help the model discover the impact of specific gene expression on cell type annotation. To verify the effectiveness of scRGCL, we compared its performance with six methods (three shallow learning algorithms and three state-of-the-art DL-based methods) on eight single-cell benchmark datasets from two species (seven in human and one in mouse). Experimental results not only show that scRGCL outperforms competing methods but also demonstrate the generalizability of scRGCL for cell type annotation. scRGCL is available at https://github.com/nathanyl/scRGCL.

DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment

Cellular deconvolution via bulk RNA sequencing (RNA-seq) presents a cost-effective and efficient alternative to experimental methods such as flow cytometry and single-cell RNA-seq (scRNA-seq) for analyzing the complex cellular composition of tumor microenvironments. Despite challenges due to heterogeneity within and among tumors, our innovative deep learning-based approach, DeSide, shows exceptional accuracy in estimating the proportions of 16 distinct cell types and subtypes within solid tumors. DeSide integrates biological pathways and assesses noncancerous cell types first, effectively sidestepping the issue of highly variable gene expression profiles (GEPs) associated with cancer cells. By leveraging scRNA-seq data from six cancer types and 185 cancer cell lines across 22 cancer types as references, our method introduces distinctive sampling and filtering techniques to generate a high-quality training set that closely replicates real tumor GEPs, based on The Cancer Genome Atlas (TCGA) bulk RNA-seq data. With this model and high-quality training set, DeSide outperforms existing methods in estimating tumor purity and the proportions of noncancerous cells within solid tumors. Our model precisely predicts cellular compositions across 19 cancer types from TCGA and proves its effectiveness with multiple additional external datasets. Crucially, DeSide enables the identification and analysis of combinatorial cell type pairs, facilitating the stratification of cancer patients into prognostically significant groups. This approach not only provides deeper insights into the dynamics of tumor biology but also highlights potential therapeutic targets by underscoring the importance of specific cell type or subtype interactions.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a metastasis-related gene signature for predicting prognosis in lung adenocarcinoma

BACKGROUND

Metastasis has been documented as an independent and significant prognostic feature of lung adenocarcinoma (LUAD) patients. However, the underlying genetic and molecular mechanisms responsible for LUAD metastasis and their prognostic significance are not exactly defined.

METHODS

The single-cell transcriptomic profiles of primary and metastatic LUAD samples were integrated as a whole dataset. Enrichment analysis and pseudotime trajectory analysis were performed to illustrate the cellular origins and changes during the metastatic process. The LUAD metastasis-related genes (LMRGs) molecular cluster and signature was constructed through unsupervised consensus clustering and ten machine-learning algorithms in The Cancer Genome Atlas (TCGA) LUAD cohort using ten machine-learning algorithms. Validation of the signature was conducted using four independent cohorts from the Gene Expression Omnibus (GEO) database. Kaplan-Meier, ROC, univariate and multivariate Cox-regression analyses were performed to test the stability of the signature. The gene CCT6A was subjected to knockdown, followed by validation through western blot analysis, flow cytometry, wound healing and transwell-migration assays to determine its potential significance.

RESULTS

First, the signaling pathway networks remodeling and metabolic reprogramming were demonstrated to be involved in the metastasis of malignant LUAD cells, which facilitate their extravasation and adaptation to other organs. Furthermore, distinct subtypes of malignant LUAD cells exhibit tissue-specific patterns. Then, two distinct molecular patterns of LMRGs were established, which showed diverse prognoses. A LUAD metastasis-related gene signature (LMRGS) was constructed via a multiple machine-learning-based integrative procedure, which possesses distinctly superior accuracy than most common clinical features and 69 published prognostic signatures. The patients stratified by the signature into high-risk group had a significantly poorer prognosis compared to those in the low-risk group, and this was well validated across different clinical subgroups. In addition, the risk score calculated by LMRGS remained an independent prognostic parameter in both univariate and multivariate Cox regression. Notably, knockdown of CCT6A gene promoted cell apoptosis and decelerated the cell migration obviously.

CONCLUSION

LMRGS could serve as a novel and promising tool to improve clinical outcomes for individual LUAD patients.

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICIs). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an discriminative power (area under the curve [AUC]) exceeding 95% in identifying patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

Reactive astrocytes promote tumor progression by up-regulating tumor protocadherin 1 expression in lung cancer brain metastasis

Brain metastasis (BM) is one of the main causes of death in patients with non-small cell lung carcinoma. The specific pathological processes of BM, which are inextricably linked to the brain tumor microenvironment, such as the abundance of astrocytes, lead to limited treatment options and poor prognosis. Reactive astrocytes are acquired in the BM; however, the underlying mechanisms remain unclear. This study aimed to explore the mechanisms by which astrocytes promote BM development. We determined the crucial role of reactive astrocytes in promoting the proliferation and migration of brain metastatic lung tumor cells by upregulating protocadherin 1 (PCDH1) expression in an in vitro co-culture model. The overexpression of PCDH1 was confirmed in clinical BM samples using immunohistochemical staining. Survival analysis indicated that high-PCDH1 expression was associated with poor survival in patients with lung adenocarcinoma. In vivo assays further showed that silence of PCDH1 effectively inhibited the tumor progression of brain metastases and prolonged the survival of animals. RNA sequencing has revealed that PCDH1 plays an important role in cell proliferation and adhesion. In conclusion, the present study revealed the promoting role of astrocytes in enhancing the aggressive phenotype of brain metastatic tumor cells by regulating the expression of PCDH1, which might be a biomarker for BM diagnosis and prognosis, suggesting the potential efficacy of targeting important astrocyte-tumor interactions in the treatment of patients with non-small cell lung carcinoma with BM.

Spatial Multiomics Reveals Intratumoral Immune Heterogeneity with Distinct Cytokine Networks in Lung Cancer Brain Metastases

The tumor microenvironment of brain metastases has become a focus in the development of immunotherapeutic drugs. However, countless patients with brain metastasis have not experienced clinical benefit. Thus, understanding the immune cell composition within brain metastases and how immune cells interact with each other and other microenvironmental cell types may be critical for optimizing immunotherapy. We applied spatial whole-transcriptomic profiling with extensive multiregional sampling (19-30 regions per sample) and multiplex IHC on formalin-fixed, paraffin-embedded lung cancer brain metastasis samples. We performed deconvolution of gene expression data to infer the abundances of immune cell populations and inferred spatial relationships from the multiplex IHC data. We also described cytokine networks between immune and tumor cells and used a protein language model to predict drug-target interactions. Finally, we performed deconvolution of bulk RNA data to assess the prognostic significance of immune-metastatic tumor cellular networks. We show that immune cell infiltration has a negative prognostic role in lung cancer brain metastases. Our in-depth multiomics analyses further reveal recurring intratumoral immune heterogeneity and the segregation of myeloid and lymphoid cells into distinct compartments that may be influenced by distinct cytokine networks. By using computational modeling, we identify drugs that may target genes expressed in both tumor core and regions bordering immune infiltrates. Finally, we illustrate the potential negative prognostic role of our immune-metastatic tumor cell networks. Our findings advocate for a paradigm shift from focusing on individual genes or cell types toward targeting networks of immune and tumor cells.

SIGNIFICANCE

Immune cell signatures are conserved across lung cancer brain metastases, and immune-metastatic tumor cell networks have a prognostic effect, implying that targeting cytokine networks between immune and metastatic tumor cells may generate more precise immunotherapeutic approaches.

SPP1+ TAM Regulates the Metastatic Colonization of CXCR4+ Metastasis-Associated Tumor Cells by Remodeling the Lymph Node Microenvironment

Lymph node metastasis, the initial step in distant metastasis, represents a primary contributor to mortality in patients diagnosed with oral squamous cell carcinoma (OSCC). However, the underlying mechanisms of lymph node metastasis in OSCC remain incompletely understood. Here, the transcriptomes of 56 383 single cells derived from paired tissues of six OSCC patients are analyzed. This study founds that CXCR4+ epithelial cells, identified as highly malignant disseminated tumor cells (DTCs), exhibited a propensity for lymph node metastasis. Importantly, a distinct subset of tumor-associated macrophages (TAMs) characterized by exclusive expression of phosphoprotein 1 (SPP1) is discovered. These TAMs may remodel the metastatic lymph node microenvironment by potentially activating fibroblasts and promoting T cell exhaustion through SPP1-CD44 and CD155-CD226 ligand-receptor interactions, thereby facilitating colonization and proliferation of disseminated tumor cells. The research advanced the mechanistic understanding of metastatic tumor microenvironment (TME) and provided a foundation for the development of personalized treatments for OSCC patients with metastasis.

Characterization of an orthotopic mouse transplant model reveals early changes in the tumor microenvironment of lung cancer

To understand the cellular and molecular dynamics in the early stages of lung cancer, we explored a mouse model of orthotopic tumor transplant created from the Lewis Lung Carcinoma (LLC) cell line. Employing single-cell RNA sequencing, we analyzed the cellular landscape during tumor engraftment, focusing particularly on LLC cells harboring the Kras G12C mutation. This allowed us to identify LLC tumor cells via the detection of mutant Kras transcripts and observe elevated levels of Myc and mesenchymal gene expression. Moreover, our study revealed significant alterations in the lung microenvironment, including the activation of tissue remodeling genes in fibroblasts and the downregulation of MHC class II genes in myeloid subsets. Additionally, T/NK cell subsets displayed more regulatory phenotypes, coupled with reduced proliferation in CD8+ T cells. Collectively, these findings enhance our understanding of lung cancer progression, particularly in a tumor microenvironment with low immunogenicity. [BMB Reports 2024; 57(11): 484-489].

Immunomodulation by endothelial cells: prospects for cancer therapy

Growing evidence highlights the importance of tumor endothelial cells (TECs) in the tumor microenvironment (TME) for promoting tumor growth and evading immune responses. Immunomodulatory endothelial cells (IMECs) represent a distinct plastic phenotype of ECs that exerts the ability to modulate immunity in health and disease. This review discusses our current understanding of IMECs in cancer biology, scrutinizing insights from single-cell reports to compare their characteristics and function dynamics across diverse tumor types, conditions, and species. We investigate possible implications of exploiting IMECs in the context of cancer treatment, particularly examining their influence on the efficacy of existing therapies and the potential to leverage them as targets in optimizing immunotherapeutic strategies.

SPP1+ macrophages and FAP+ fibroblasts promote the progression of pMMR gastric cancer

Immunotherapy has become a primary and secondary treatment for gastric cancer (GC) patients with mismatch repair deficiency (dMMR), and is used in both perioperative and advanced stages. The tumor immune microenvironment (TiME) is crucial for immunotherapy efficacy, yet the impact of MMR status on TiME remains understudied. We employed single-cell RNA sequencing (scRNA-seq) to analyze 33 fresh tissue samples from 25 patients, which included 10 normal tissues, 6 dMMR tumor tissues, and 17 pMMR tumor tissues, aiming to characterize the cellular and molecular components of the TiME. The proficient mismatch repair (pMMR) group displayed a significantly higher prevalence of a specific GC cell type, termed GC2, characterized by increased hypoxia, epithelial-mesenchymal transition (EMT), and angiogenic activities compared to the dMMR group. GC2 cells overexpressed BEX3 and GPC3, and they significantly correlated with poorer survival. The pMMR group also showed increased infiltration of SPP1 + macrophages and FAP + fibroblasts, exhibiting strong hypoxic and pro-angiogenic features. Furthermore, a higher proportion of E2 endothelial cells, involved in extracellular matrix (ECM) remodeling and showing heightened VEGF pathway, HIF pathway, and angiogenesis activity, were identified in pMMR patients. Intercellular communication analyses revealed that GC2 cells, SPP1 + macrophages, FAP + fibroblasts, and E2 endothelial cells interact through VEGF, SPP1, and MIF signals, forming a TiME characterized by hypoxia, pro-angiogenesis, and ECM remodeling. This study uncovered TiME heterogeneity among GC patients with different MMR states, highlighting that the pMMR TiME is distinguished by hypoxia, pro-angiogenesis, and ECM remodeling, driven by the presence of GC2 cells, SPP1 + macrophages, FAP + fibroblasts, and E2 endothelial cells. These findings are pivotal for developing targeted immunotherapies for GC patients with pMMR.

Ferroptosis-related gene signature for predicting prognosis and identifying potential therapeutic drug in EGFR wild-type lung adenocarcinoma

Epidermal growth factor receptor wild type lung adenocarcinoma (EGFRWT LUAD) still has limited treatment options and unsatisfactory clinical outcomes. Ferroptosis, as a form of cell death, has been reported to play a dual role in regulating tumor cell survival. In this study, we constructed a 3-ferroptosis-gene signature, FeSig, and verified its accuracy and efficacy in predicting EGFRWT LUAD prognosis at both the RNA and protein levels. Patients with higher FeSig scores were found to have worse clinical outcomes. Additionally, we explored the relationship between FeSig and tumor microenvironment, revealing that enhanced interactions between fibroblasts and tumor cells in FeSighigh patients causing tumor resistance to ferroptosis. To address this challenge, we screened potential drugs from NCI-60 (The US National Cancer Institute 60 human tumour cell line anticancer drug screen) and Connectivity map database, ultimately identifying 6-mercatopurine (6-MP) as a promising candidate. Both in vitro and in vivo experiments demonstrated its efficacy in treating FeSighigh EGFRWT LUAD tumor models. In summary, we develop a novel FeSig for predicting prognosis and guiding drug application.

Differential cellular communication in tumor immune microenvironment during early and advanced stages of lung adenocarcinoma

Heterogeneous behavior of each cell type and their cross-talks in tumor immune microenvironment (TIME) refers to tumor immunological heterogeneity that emerges during tumor progression and represents formidable challenges for effective anti-tumor immune response and promotes drug resistance. To comprehensively elucidate the heterogeneous behavior of individual cell types and their interactions across different stages of tumor development at system level, a computational framework was devised that integrates cell specific data from single-cell RNASeq into networks illustrating interactions among signaling and metabolic response genes within and between cells in TIME. This study identified stage specific novel markers which remodel the cross-talks, thereby facilitating immune stimulation. Particularly, multicellular knockout of metabolic gene APOE (Apolipoprotein E in mast cell, myeloid cell and fibroblast) combined with signaling gene CAV1 (Caveolin1 in endothelial and epithelial cells) resulted in the activation of T-cell mediated signaling pathways. Additionally, this knockout also initiated intervention of cytotoxic gene regulations during tumor immune cell interactions at the early stage of Lung Adenocarcinoma (LUAD). Furthermore, a unique interaction motif from multiple cells emerged significant in regulating the overall immune response at the advanced stage of LUAD. Most significantly, FCER1G (Fc Fragment of IgE Receptor Ig) was identified as the common regulator in activating the anti-tumor immune response at both stages. Predicted markers exhibited significant association with patient overall survival in patient specific dataset. This study uncovers the significance of signaling and metabolic interplay within TIME and discovers important targets to enhance anti-tumor immune response at each stage of tumor development.

Identification of a Novel Signature Based on Ferritinophagy-Related Genes to Predict Prognosis in Lung Adenocarcinoma: Focus on AHNAK2

BACKGROUND

Lung adenocarcinoma (LUAD) accounts for over 40% of all non-small cell lung cancer (NSCLC) cases and continues to be difficult to treat despite advancements in diagnostics and therapies. Ferritinophagy, a newly recognized autophagy process linked to ferroptosis, has been associated with LUAD development. Recent studies have shown a dysregulation of genes related to ferritinophagy in LUAD, indicating its potential as a therapeutic target.

METHODS

We constructed a predictive model using seven genes associated with ferritinophagy. The model's accuracy was evaluated across three independent gene expression datasets. We analyzed the biological functions, immune environment, mutations, and drug sensitivities in groups with high and low risk. Utilizing a single-cell sequencing (scRNA-seq) dataset, we confirmed the expression of the model genes and identified a subtype of epithelial cells expressing AHNAK2. We further investigated the impact of the ferritinophagy-related gene AHNAK2 on LUAD cell proliferation, invasion, migration, and ferroptosis in vitro.

RESULTS

Our prediction model, comprising seven genes (AHNAK2, ARNTL2, CD27, LTB, SLC15A1, SLC2A1, and SYT1), has shown potential in predicting the prognosis of individuals diagnosed with LUAD. Notably, AHNAK2 impedes ferroptosis, promoting LUAD progression in vitro.

CONCLUSIONS

Our research suggests that ferritinophagy-associated genes are promising prognostic markers for LUAD and lay the groundwork for further exploration of ferritinophagy's role in LUAD. Furthermore, we present AHNAK2 as a novel regulator of ferroptosis, which requires further investigation to understand its mechanism.

Secretory Nogo-B regulates Th2 differentiation in the lung cancer microenvironment

Nogo-B, a ubiquitously expressed member of the reticulon family, plays an important role in maintaining endoplasmic reticulum (ER) structure, regulating protein folding, and calcium homeostasis. In this study, we demonstrate that Nogo-B expression and secretion are upregulated in lung cancer and correlate to overall survival. Nogo-B is secreted by various cells, particularly lung cancer cells. ER stress and phosphorylation at serine 107 can induce Nogo-B secretion. Secretory Nogo-B suppresses the differentiation of Th2 cells and the release of type 2 cytokines, thus influencing the anti-tumor effects of Th2-related immune cells, including IgE+B cell class switching and eosinophil activation.

Single-cell RNA sequencing and spatial transcriptome reveal potential molecular mechanisms of lung cancer brain metastasis

BACKGROUND

Lung cancer is a highly aggressive and prevalent disease worldwide. By the time it is first diagnosed, distant metastases have usually already occurred. Among them, the prognosis of patients with brain metastasis from lung cancer is very poor. Therefore, it is particularly important to identify the evolutionary status of tumor cells during lung cancer brain metastases and discover the underlying mechanisms of lung cancer brain metastases.

METHODS

In this study, we analysed three types of data: single-cell RNA sequencing, bulk RNA sequencing, and spatial transcriptome. Firstly, we identified early metastatic epithelial cell clusters (EMEC) using CNV and trajectory analysis in scRNA-seq data. Secondly, we integrated scRNA-seq and spatial transcriptome data with the help of MIA (Multimodal intersection analysis) to explore the biological characteristics of EMEC. Finally, we used bulk RNA-seq data to validate the molecular characteristics of EMEC.

RESULT

A total of 55,763 single cells were obtained and divided into 9 cell types. In brain metastasis, we found a significantly higher proportion of epithelial cells. In addition, we identified a specific subpopulation of epithelial cells, which was named as "early metastatic epithelial cell clusters (EMEC)". It is enriched in oxidative phosphorylation, coagulation, complement. Moreover, we also found that EMEC underwent cellular communication with other immune cells through ligand-receptor pairs such as MIF-(CD74 + CXCR4) and MIF-(CD74 + CD44). Next, we validated that EMEC were associated with poor clinical prognosis using three independent external datasets. Finally, spatial transcriptome analysis revealed specificity in the spatial distribution of EMEC, which shifted from the peripheral regions to the central regions of the tumour as the depth of tumor invasion progressed.

CONCLUSION

This study reveals the potential molecular mechanisms of lung cancer brain metastasis from both single-cell and spatial transcriptomic perspectives, providing biological insights and clinical reference value for detecting patients suffering from lung cancer brain metastasis.

Non-classical action of Ku70 promotes Treg suppressive function through a FOXP3-dependent mechanism in lung adenocarcinoma

Ku70, a DNA repair protein, binds to the damaged DNA ends and orchestrates the recruitment of other proteins to facilitate repair of DNA double-strand breaks. Besides its essential role in DNA repair, several studies have highlighted non-classical functions of Ku70 in cellular processes. However, its function in immune homeostasis and anti-tumor immunity remains unknown. Here, we discovered a marked association between elevated Ku70 expression and unfavorable prognosis in lung adenocarcinoma, focusing specifically on increased Ku70 levels in tumor-infiltrated Treg cells. Using a lung-colonizing tumor model of in mice with Treg-specific Ku70 deficiency, we demonstrated that deletion of Ku70 in Treg cells led to a stronger anti-tumor response and slower tumor growth due to impaired immune-suppressive capacity of Treg cells. Furthermore, we confirmed that Ku70 played a critical role in sustaining the suppressive function of human Treg cells. We found that Ku70 bound to FOXP3 and occupied FOXP3-bound genomic sites to support its transcriptional activities. These findings not only unveil a non-homologous end joining (NHEJ)-independent role of Ku70 crucial for Treg suppressive function, but also underscore the potential of targeting Ku70 as an effective strategy in cancer therapy, aiming to both restrain cancer cells and enhance pulmonary anti-tumor immunity.

Proteomic and transcriptomic analyses identify apo-transcobalamin-II as a biomarker of overall survival in osteosarcoma

Background

The large-scale proteomic platform known as the SomaScan® assay is capable of simultaneously measuring thousands of proteins in patient specimens through next-generation aptamer-based multiplexed technology. While previous studies have utilized patient peripheral blood to suggest serum biomarkers of prognostic or diagnostic value in osteosarcoma (OSA), the most common primary pediatric bone cancer, they have ultimately been limited in the robustness of their analyses. We propose utilizing this aptamer-based technology to describe the systemic proteomic milieu in patients diagnosed with this disease.

Methods

To determine novel biomarkers associated with overall survival in OSA, we deployed the SomaLogic SomaScan® 7k assay to investigate the plasma proteomic profile of naive primary, recurrent, and metastatic OSA patients. Following identification of differentially expressed proteins (DEPs) between 2-year deceased and survivor cohorts, publicly available databases including Survival Genie, TIGER, and KM Plotter Immunotherapy, among others, were utilized to investigate the significance of our proteomic findings.

Results

Apo-transcobalamin-II (APO-TCN2) was identified as the most DEP between 2-year deceased and survivor cohorts (Log2 fold change = 6.8, P-value = 0.0017). Survival analysis using the Survival Genie web-based platform indicated that increased intratumoral TCN2 expression was associated with better overall survival in both OSA (TARGET-OS) and sarcoma (TCGA-SARC) datasets. Cell-cell communication analysis using the TIGER database suggested that TCN2+ Myeloid cells likely interact with marginal zone and immunoglobin-producing B lymphocytes expressing the TCN2 receptor (CD320) to promote their proliferation and survival in both non-small cell lung cancer and melanoma tumors. Analysis of publicly available OSA scRNA-sequencing datasets identified similar populations in naive primary tumors. Furthermore, circulating APO-TCN2 levels in OSA were then associated with a plasma proteomic profile likely necessary for robust B lymphocyte proliferation, infiltration, and formation of intratumoral tertiary lymphoid structures for improved anti-tumor immunity.

Conclusions

Overall, APO-TCN2, a circulatory protein previously described in various lymphoproliferative disorders, was associated with 2-year survival status in patients diagnosed with OSA. The relevance of this protein and apparent immunological function (anti-tumor B lymphocyte responses) was suggested using publicly available solid tumor RNA-sequencing datasets. Further studies characterizing the biological function of APO-TCN2 and its relevance in these diseases is warranted.

Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-the-art predictive computational methods have facilitated the prediction of cancer stemness, there remains a lack of efficient resources to accommodate various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at both bulk and single-cell levels. This resource integrates eight robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five distinct aspects: identifying the signature genes of cancer stemness; exploring the associations with cancer hallmarks and cellular states; exploring the associations with immune response and the communications with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding downstream functional interpretation, including immune response and cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.

Molecular and functional landscape of malignant serous effusions for precision oncology

Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells from patient biopsies. Here, we comprehensively characterize a pan-cancer cohort of 150 malignant serous effusion (MSE) samples at the cellular, molecular, and functional level. We find that MSE-derived cancer cells retain the genomic and transcriptomic profiles of their corresponding primary tumors, validating their use as a patient-relevant model system for solid tumor biology. Integrative analyses reveal that baseline gene expression patterns relate to global ex vivo drug sensitivity, while high-throughput drug-induced transcriptional changes in MSE samples are indicative of drug mode of action and acquired treatment resistance. A case study exemplifies the added value of multi-modal MSE profiling for patients who lack genetically stratified treatment options. In summary, our study provides a functional multi-omics view on a pan-cancer solid tumor cohort and underlines the feasibility and utility of MSE-based precision oncology.

Machine-learning and scRNA-Seq-based diagnostic and prognostic models illustrating survival and therapy response of lung adenocarcinoma

Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to avoid overdiagnosis and overtreatment. Taking full advantage of scRNA-Seq data to resolve the tumor heterogeneities, we explored the overall landscape of LUAD microenvironment. Utilizing the stage-specific tumor cell markers, we have developed highly accurate diagnostic and prognostic models with elevated sensitivity and specificity. The diagnostic model, developed through random forest algorithms with a thirteen-gene signature, achieved an accuracy of 96.4% and an AUC of 0.993. These metrics were further demonstrated by benchmarking with available models and scoring systems in independent cohorts. Concurrently, the prognostic model, formulated via Cox regression with a six-gene signature, effectively predicted overall survival, with elevated risk scores associated with increased fractions of cancer-associated fibroblasts, and higher likelihood of immune escape and T-cell exclusion. Subsequently, two nomograms were developed to predict survival and drug responses, facilitating their integration into clinical practice. Overall, this study underscores the potential of our models for efficient, rapid, and cost-effective diagnosis and prognosis of LUAD, adaptable to multiple expression profiling platforms and quantification methods.

A single-cell RNA-seq dataset describing macrophages in NSCLC tumor and peritumor tissues

Examining tumor-associated macrophages in the immune microenvironment of non-small cell lung cancer (NSCLC) is essential for gaining an understanding of the genesis and development of NSCLC as well as for identifying key clinical therapeutic targets. Although previous studies have reported the diverse phenotypes and functions of macrophages in tumor tissues, thereby highlighting their significant role in the tumor microenvironment, the characteristic differences and correlations between tumor and peritumor tissue-derived macrophages that are necessary for an understanding of NSCLC progression remain unclear. Based on single-cell RNA sequencing, we generated a comprehensive dataset of transcriptomes from NSCLC tumor and peritumor tissues, thereby facilitating comprehensive analysis and providing significant insights. In summary, our dataset will serve as a valuable transcriptomic resource for further studies investigating NSCLC development.

The spatial and cellular portrait of transposable element expression during gastric cancer

Gastric Cancer (GC) is a lethal malignancy, with urgent need for the discovery of novel biomarkers for its early detection. I previously showed that Transposable Elements (TEs) become activated in early GC (EGC), suggesting a role in gene expression. Here, I follow-up on that evidence using single-cell data from gastritis to EGC, and show that TEs are expressed and follow the disease progression, with 2,430 of them being cell populations markers. Pseudotemporal trajectory modeling revealed 111 TEs associated with the origination of cancer cells. Analysis of spatial data from GC also confirms TE expression, with 204 TEs being spatially enriched in the tumor regions and the tumor microenvironment, hinting at a role of TEs in tumorigenesis. Finally, a network of TE-mediated gene regulation was modeled, indicating that ~ 2,000 genes could be modulated by TEs, with ~ 500 of them already implicated in cancer. These results suggest that TEs might play a functional role in GC progression, and highlights them as potential biomarker for its early detection.

IL-1 receptor-associated kinase 3 (IRAK3) in lung adenocarcinoma predicts prognosis and immunotherapy resistance: involvement of multiple inflammation-related pathways

Background

Lung cancer is a globally prevailing malignancy, and the predominant histological subtype is lung adenocarcinoma (LUAD). IL-1 receptor-associated kinase 3 (IRAK3) has been identified in connection with innate immune and inflammatory response. The aim of this study is to investigate the impact of IRAK3 on prognosis and immunotherapy efficacy in LUAD, which remains incompletely elucidated.

Methods

Our study delved into multiple online databases to find out expression, methylation and prognostic potentials of IRAK3 in LUAD and other malignancies. We employed tissue microarrays to assess IRAK3 protein levels in our LUAD cohort [National Cancer Center (NCC), China] and explore prognostic values. The correlations between IRAK3 and immune infiltration based on The Cancer Genome Atlas (TCGA) data were analyzed by corresponding algorithms. The contribution of IRAK3 to immunotherapy response was explored through the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Both LinkedOmics database and gene set enrichment analysis (GSEA) were applied to investigate how IRAK3 influences the tumor immune microenvironment and regulates immunotherapy response. We applied single-cell RNA sequencing datasets for the investigation of IRAK3 expression across diverse immune cells. Moreover, we employed genomics of drug sensitivity in cancer (GDSC) databases to examine how IRKA3 expression correlates with different drug responses.

Results

Compared with normal tissues, various tumor tissues had lower IRAK3 expression which could be regulated by its high methylation level. Reduced IRAK3 protein level was observed to correlate with advanced tumor stages and unfavorable prognosis among patients with LUAD, especially individuals with lymph node metastasis. Gene set enrichment analysis (GSEA) and tumor infiltration analysis proved that IRAK3 provoked immune infiltration. Macrophages/monocytes, CD4+ T cells, CD8+ T cells and neutrophils correlated significantly with IRAK3 expression. With TIDE algorithm, IRAK3 was verified to be related to poor immune checkpoint blockade (ICB) response. IRAK3 demonstrated positive associations with T-cell dysfunction score and immune checkpoint markers. Conversely, it exhibited negative correlations with microsatellite instability (MSI) and tumor mutation burden (TMB). High IRAK3 expression exacerbated cytotoxic T lymphocyte (CTL) dysfunction and predicted immunotherapy resistance by involvement of multiple inflammation-related pathways including IL-6/JAK/STAT3 signaling, inflammatory response and interferon-gamma (IFN-γ) response pathways. Additionally, elevated IRAK3 expression was predicted to be related with better responses to chemotherapeutic and molecular targeted drugs.

Conclusions

Our findings indicated that IRAK3 could function as an independent prognostic predictor and an immunotherapeutic indicator in LUAD through involvement of multiple inflammation-related pathways.

Exploring a new signature for lung adenocarcinoma: analyzing cuproptosis-related genes through Integrated single-cell and bulk RNA sequencing

OBJECTIVES

Lung adenocarcinoma (LUAD) continues to pose a significant global health challenge. This research investigates cuproptosis and its association with LUAD progression. Employing various bioinformatics techniques, the study explores the heterogeneity of LUAD cells, identifies prognostic cuproptosis-related genes (CRGs), examines cell-to-cell communication networks, and assesses their functional roles.

METHODS

We downloaded single-cell RNA sequencing data from TISCH2 and bulk RNA sequencing data from TCGA for exploring LUAD cell heterogeneity. Subsequently, "CellChat" package was employed for intercellular communication network analysis, while weighted correlation network analysis was applied for identification of hub CRGs. Further, A cuproptosis related prognostic signature was constructed via LASSO regression, validated through survival analysis, nomogram development, and ROC curves. We assessed immune infiltration, gene mutations, and GSEA of prognostic CRGs. Finally, in vitro experiments were applied to validate CDC25C's role in LUAD.

RESULTS

We identified 15 clusters and nine cell type in LUAD. Malignant cells showed active communication and pathway enrichment in "oxidative phosphorylation" and "glycolysis". Meanwhile, prognostic hub CRGs including PFKP, CDC25C, F12, SIGLEC6, and NLRP7 were identified, with a robust prognostic signature. Immune infiltration, gene mutations, and functional enrichment correlated with prognostic CRGs. In vitro cell experiments have shown that CDC25C-deficient LUAD cell lines exhibited reduced activity.

CONCLUSION

This research reveals the heterogeneity of LUAD cells, identifies key prognostic CRGs, and maps intercellular communication networks, providing insights into LUAD pathogenesis. These findings pave the way for developing targeted therapies and precision medicine approaches.

Selective loss of Y chromosomes in lung adenocarcinoma modulates the tumor immune environment through cancer/testis antigens

There is increasing recognition that the sex chromosomes, X and Y, play an important role in health and disease that goes beyond the determination of biological sex. Loss of the Y chromosome (LOY) in blood, which occurs naturally in aging men, has been found to be a driver of cardiac fibrosis and heart failure mortality. LOY also occurs in most solid tumors in males and is often associated with worse survival, suggesting that LOY may give tumor cells a growth or survival advantage. We analyzed LOY in lung adenocarcinoma (LUAD) using both bulk and single-cell expression data and found evidence suggesting that LOY affects the tumor immune environment by altering cancer/testis antigen expression and consequently facilitating tumor immune evasion. Analyzing immunotherapy data, we show that LOY and changes in expression of particular cancer/testis antigens are associated with response to pembrolizumab treatment and outcome, providing a new and powerful biomarker for predicting immunotherapy response in LUAD tumors in males.

New insights into allergic rhinitis treatment: MSC nanovesicles targeting dendritic cells

Allergic rhinitis (AR) is a condition with limited treatment options. This study investigates the potential use of mesenchymal stem cell (MSC) nanovesicles as a novel therapy for AR. Specifically, the study explores the underlying mechanisms of MSC nanovesicle therapy by targeting dendritic cells (DCs). The researchers fabricated DC-targeted P-D2-EVs nanovesicles and characterized their properties. Transcriptomic sequencing and single-cell sequencing analyses were performed to study the impact of P-D2-EVs on AR mice, identifying core genes involved in the treatment. In vitro cell experiments were conducted to validate the effects of P-D2-EVs on DC metabolism, Th2 differentiation, and ILC2 activation. The results showed that P-D2-EVs efficiently targeted DCs. Transcriptomic sequencing analysis revealed differential expression of 948 genes in nasal tissue DCs of mice treated with P-D2-EVs. Single-cell sequencing further revealed that P-D2-EVs had inhibitory effects on DC activation, Th2 differentiation, and ILC2 activation, with Fut1 identified as the core gene. Validation experiments demonstrated that P-D2-EVs improved IL10 metabolism in DCs by downregulating Fut1 expression, thereby suppressing Th2 differentiation and ILC2 activation. Animal experiments confirmed the inhibitory effects of P-D2-EVs and their ability to ameliorate AR symptoms in mice. The study suggests that P-D2-EVs reshape DC metabolism and suppress Th2 differentiation and ILC2 activation through the inhibition of the Fut1/ICAM1/P38 MAPK signaling pathway, providing a potential therapeutic approach for AR.

Longitudinal Intravascular Antibody Labeling Identified Regulatory T Cell Recruitment as a Therapeutic Target in a Mouse Model of Lung Cancer

Anticancer immunity is predicated on leukocyte migration into tumors. Once recruited, leukocytes undergo substantial reprogramming to adapt to the tumor microenvironment. A major challenge in the field is distinguishing recently recruited from resident leukocytes in tumors. In this study, we developed an intravascular Ab technique to label circulating mouse leukocytes before they migrate to tissues, providing unprecedented insight into the kinetics of recruitment. This approach unveiled the substantial role of leukocyte migration in tumor progression using a preclinical mouse model of lung adenocarcinoma. Regulatory T cells (Tregs), critical mediators of immunosuppression, were continuously and rapidly recruited into tumors throughout cancer progression. Moreover, leukocyte trafficking depended on the integrins CD11a/CD49d, and CD11a/CD49d blockade led to significant tumor burden reduction in mice. Importantly, preventing circulating Treg recruitment through depletion or sequestration in lymph nodes was sufficient to decrease tumor burden, indicating that Treg migration was crucial for suppressing antitumor immunity. These findings underscore the dynamic nature of the immune compartment within mouse lung tumors and demonstrate the relevance of a temporal map of leukocyte recruitment into tumors, thereby advancing our understanding of leukocyte migration in the context of tumor development.

Exploring the relationship between the interleukin family and lung adenocarcinoma through Mendelian randomization and RNA sequencing analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is still one of the most prevalent malignancies. Interleukin factors are closely associated with the initiation and progression of cancer. However, the relationship between interleukin factors and LUAD has not been fully elucidated. This study aimed to use Mendelian randomization (MR) and RNA sequencing (RNA-seq) analyses to identify the interleukin factors associated with the onset and progression of LUAD.

METHODS

Exposure-related instrumental variables were selected from interleukin factor summary datasets. The LUAD summary dataset from FINGENE served as the outcome. MR and sensitivity analyses were conducted to screen for interleukin factors associated with LUAD occurrence. Transcriptome analyses revealed the role of interleukin factors in lung tissues. The results were validated through Western blotting and further confirmed with driver gene-negative patients from multiple centers. Potential mechanisms influencing LUAD occurrence and development were explored using bulk RNA-seq and single-cell RNA-seq data.

RESULTS

MR analysis indicated that elevated plasma levels of IL6RB, IL27RA, IL22RA1, and IL16 are causally associated with increased LUAD risk, while IL18R1 and IL11RA exhibit the opposite effect. Transcriptome analyses revealed that IL11RA, IL18R1, and IL16 were downregulated in tumor tissues compared with normal lung tissue, but only higher expression of IL11RA correlated with improved prognosis in patients with LUAD from different centers and persisted even in driver-gene negative patients. The IL11RA protein level was lower in various LUAD cell lines than in human bronchial epithelial cells. The genes co-expressed with IL11RA were enriched in the Ras signaling pathway and glycosylation processes. Fibroblasts were the primary IL11RA-expressing cell population, with IL11RA+fibroblasts exhibiting a more immature state. The genes differentially expressed between IL11RA+and IL11RA- fibroblasts were involved in the PI3K-Akt/TNF signaling pathway.

CONCLUSION

According to the MR and transcriptome analyses, the downregulation of IL11RA was closely related to the occurrence and development of LUAD.

Single-cell analysis reveals the disparities in immune profiles between younger and elder patients

PURPOSE

The immune profiles of elder patients with non-small cell lung cancer (NSCLC) differ significantly from those of younger patients. The tumor microenvironment (TME) is a crucial factor in cancer progression and therapeutic responses. The present study aims to decipher the difference in TME between younger and elderly patients with lung cancers.

METHODS

We downloaded single-cell RNA data from public databases. The algorithm of uniform manifold approximation and projection (UMAP) was applied to cluster and visualize single-cell sequencing data. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) analysis were performed to evaluate the physiological functional characteristics in sub-group cells. CellPhoneDB was used to identify cell-cell interactions between immune cells within TME.

RESULTS

We conducted single-cell RNA sequencing on 96,491 cells from elderly patients and 169,207 cells from younger patients, respectively. We observed that epithelial cells were the predominant component of the TME in younger patients, whereas T/NK cells were the predominant cell type in the TME of elderly patients. We also found that there was a higher proportion of Tregs and a lower proportion of NK, effector CD8+T and γδT cells in elder patients compared with younger patients. In addition, a comparative GSEA analysis of NK cells between older and younger patients revealed that the pathways of Parkinson's disease, Alzheimer's disease, mismatch repair, and base excision repair were up-regulated in NK cells from elderly patients, while the pathways related to natural killer cell-mediated cytotoxicity and allograft rejection were downregulated. Furthermore, we identified tumor-associated neutrophils (TANs) in elder patients, and GSVA analysis demonstrated that the pathway of angiogenesis was upregulated, and the pathway of interferon_γ_response, inflammatory_response, TNFα_signaling_via_NFκB pathways were downregulated. Importantly, the pro-inflammatory response scores of complement C1q C chain positive (C1QC+) macrophages, tissue-resident macrophages (TRM), non-classical monocytes (NCM), secreted phosphoprotein 1 positive (SPP1+) macrophages, and classical monocytes (CM) in elder patients were significantly lower compared to those in younger patients. Finally, cell-to-cell communication analyses unveiled the disparities in regulatory patterns between elder and younger patients, namely the pairs of CXCL13-ACKR4 and CSF1-SIRPA in elder patients and the pairs of CTLA4-CD86 and TIGIT-NECTIN2 in younger patients.

CONCLUSION

This study reveals the distinct immune profiles between younger and elder NSCLC patients, and the elder patients were likely to exhibit a more immunosuppressive TME and attenuated tumor-killing capability compared with younger patients.

Dendritic cell-based immunotherapy in non-small cell lung cancer: a comprehensive critical review

Despite treatment advances through immunotherapies, including anti-PD-1/PD-L1 therapies, the overall prognosis of non-small cell lung cancer (NSCLC) patients remains poor, underscoring the need for novel approaches that offer long-term clinical benefit. This review examined the literature on the subject over the past 20 years to provide an update on the evolving landscape of dendritic cell-based immunotherapy to treat NSCLC, highlighting the crucial role of dendritic cells (DCs) in immune response initiation and regulation. These cells encompass heterogeneous subsets like cDC1s, cDC2s, and pDCs, capable of shaping antigen presentation and influencing T cell activation through the balance between the Th1, Th2, and Th17 profiles and the activation of regulatory T lymphocytes (Treg). The intricate interaction between DC subsets and the high density of intratumoral mature DCs shapes tumor-specific immune responses and impacts therapeutic outcomes. DC-based immunotherapy shows promise in overcoming immune resistance in NSCLC treatment. This article review provides an update on key clinical trial results, forming the basis for future studies to characterize the role of different types of DCs in situ and in combination with different therapies, including DC vaccines.

Characterization of tumor heterogeneity through segmentation-free representation learning

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

Multi-omics analysis reveals a pericyte-associated gene expression signature for predicting prognosis and therapeutic responses in solid cancers

The influence of the stroma on cancer progression has been underestimated, particularly the role of vascular pericytes in the tumor microenvironment. Herein, we identified 51 differentially expressed genes in tumor-derived pericytes (TPCs) by analyzing transcriptomic data from TCGA alongside our proteomic data. Using five key TPC-related genes, we constructed a prognostic risk model that accurately predicts prognosis and treatment responses in liver and lung cancers. Enrichment analyses linked these genes to blood vessel remodeling, function, and immune-related pathways. Single-cell RNA sequencing data from the GEO database validated these findings, showing significant upregulation of AKAP12 and RRAS in TPCs. Immunostaining confirmed increased expression of these genes in liver and lung tumors. Depletion of RRAS or AKAP12 in TPCs restored their blood vessel-supporting role. Overall, our findings suggest that TPC-related gene profiles can predict patient outcomes and therapeutic responses in solid cancers, and targeting these profiles could be an improved treatment strategy.

Titration of RAS alters senescent state and influences tumour initiation

Oncogenic RAS-induced senescence (OIS) is an autonomous tumour suppressor mechanism associated with premalignancy1,2. Achieving this phenotype typically requires a high level of oncogenic stress, yet the phenotype provoked by lower oncogenic dosage remains unclear. Here we develop oncogenic RAS dose-escalation models in vitro and in vivo, revealing a RAS dose-driven non-linear continuum of downstream phenotypes. In a hepatocyte OIS model in vivo, ectopic expression of NRAS(G12V) does not induce tumours, in part owing to OIS-driven immune clearance3. Single-cell RNA sequencing analyses reveal distinct hepatocyte clusters with typical OIS or progenitor-like features, corresponding to high and intermediate levels of NRAS(G12V), respectively. When titred down, NRAS(G12V)-expressing hepatocytes become immune resistant and develop tumours. Time-series monitoring at single-cell resolution identifies two distinct tumour types: early-onset aggressive undifferentiated and late-onset differentiated hepatocellular carcinoma. The molecular signature of each mouse tumour type is associated with different progenitor features and enriched in distinct human hepatocellular carcinoma subclasses. Our results define the oncogenic dosage-driven OIS spectrum, reconciling the senescence and tumour initiation phenotypes in early tumorigenesis.

A detailed insight into macrophages' role in shaping lung carcinogenesis

Despite significant advancements in cancer treatment in recent decades, the high mortality rate associated with lung cancer remains a significant concern. The development and proper execution of new targeted therapies needs more deep knowledge regarding the lung cancer associated tumour microenvironment. One of the key component of that tumour microenvironment is the lung resident macrophages. Although in normal physiological condition the lung resident macrophages are believed to maintain lung homeostasis, but they may also initiate a vicious inflammatory response in abnormal conditions which is linked to lung cancer development. Depending on the activation pathway, the lung resident macrophages are either of M1 or M2 sub-type. The M1 and M2 sub-types differ significantly in various prospectuses, from phenotypic markers to metabolic pathways. In addition to this generalized classification, the recent advancement of the multiomics technology is able to identify some other sub-types of lung resident macrophages. Researchers have also observed that these different sub-types can manipulate the pathogenesis of lung carcinogenesis in a context dependent manner and can either promote or inhibit the development of lung carcinogenesis upon receiving proper activation. As proper knowledge about the role played by the lung resident macrophages' in shaping the lung carcinogenesis is limited, so the main purpose of this review is to bring all the available information under the same roof. We also elaborated the different mechanisms involved in maintenance of the plasticity of M1/M2 sub-type, as this plasticity can be a good target for lung cancer treatment.

The co-location of CD14+APOE+ cells and MMP7+ tumour cells contributed to worse immunotherapy response in non-small cell lung cancer

Intra-tumour immune infiltration is a crucial determinant affecting immunotherapy response in non-small cell lung cancer (NSCLC). However, its phenotype and related spatial structure have remained elusive. To overcome these restrictions, we undertook a comprehensive study comprising spatial transcriptomic (ST) data (28 712 spots from six samples). We identified two distinct intra-tumour infiltration patterns: immune exclusion (characterised by myeloid cells) and immune activation (characterised by plasma cells). The immune exclusion and immune activation signatures showed adverse and favourable roles in NSCLC patients' survival, respectively. Notably, CD14+APOE+ cells were recognised as the main cell type in immune exclusion samples, with increased epithelial‒mesenchymal transition and decreased immune activities. The co-location of CD14+APOE+ cells and MMP7+ tumour cells was observed in both ST and bulk transcriptomics data, validated by multiplex immunofluorescence performed on 20 NSCLC samples. The co-location area exhibited the upregulation of proliferation-related pathways and hypoxia activities. This co-localisation inhibited T-cell infiltration and the formation of tertiary lymphoid structures. Both CD14+APOE+ cells and MMP7+ tumour cells were associated with worse survival. In an immunotherapy cohort from the ORIENT-3 clinical trial, NSCLC patients who responded unfavourably exhibited higher infiltration of CD14+APOE+ cells and MMP7+ tumour cells. Within the co-location area, the MK, SEMA3 and Macrophage migration inhibitory factor (MIF) signalling pathway was most active in cell‒cell communication. This study identified immune exclusion and activation patterns in NSCLC and the co-location of CD14+APOE+ cells and MMP7+ tumour cells as contributors to immune resistance.

Identifying genetically-supported drug repurposing targets for non-small cell lung cancer through mendelian randomization of the druggable genome

Background

Lung cancer is responsible for most cancer-related deaths, and non-small cell lung cancer (NSCLC) accounts for the majority of cases. Targeted therapy has made promising advancements in systemic treatment for NSCLC over the last two decades, but inadequate drug targets with clinically proven survival benefits limit its universal application in clinical practice compared to chemotherapy and immunotherapy. There is an urgent need to explore new drug targets to expand the beneficiary group. This study aims to identify druggable genes and to predict the efficacy and prognostic value of the corresponding targeted drugs in NSCLC.

Methods

Two-sample mendelian randomization (MR) of druggable genes was performed to predict the efficacy of their corresponding targeted therapy for NSCLC. Subsequent sensitivity analyses were performed to assess potential confounders. Accessible RNA sequencing data were incorporated for subsequent verifications, and Kaplan-Meier survival curves of different gene expressions were used to explore the prognostic value of candidate druggable genes.

Results

MR screening encompassing 4,863 expression quantitative trait loci (eQTL) and 1,072 protein quantitative trait loci (pQTL, with 453 proteins overlapping) were performed. Seven candidate druggable genes were identified, including CD33, ENG, ICOSLG and IL18R1 for lung adenocarcinoma, and VSIR, FSTL1 and TIMP2 for lung squamous cell carcinoma. The results were validated by further transcriptomic investigations.

Conclusions

Drugs targeting genetically supported genomes are considerably more likely to yield promising efficacy and succeed in clinical trials. We provide compelling genetic evidence to prioritize drug development for NSCLC.

Exploring the causal relationship between the immune cell-inflammatory factor axis and lung cancer: a Mendelian randomization study

Background

Lung cancer is a major health burden globally and smoking is a well-known risk factor. It has been observed that chronic inflammation contributes to lung cancer progression, with immune cells and inflammatory cytokines implicated in tumor development. Clarifying the causal links between these immune components and lung cancer could enhance prevention and therapy.

Methods

We performed Mendelian randomization (MR) to explore causal connections between immune cells, inflammatory markers, and lung cancer risk, using genetic variants as instruments. Data from GWAS on these variables underpinned our MR analyses.

Results

Our findings indicated an inverse association between some immune cells and lung cancer risk, implying that more immune cells might be protective. NK T cells (CD16-CD56) and myeloid cells (HLA DR+ on CD33dim HLA DR+ CD11b+) had an inverse correlation with lung cancer risk. Furthermore, a direct relationship was observed between inflammatory cytokines and these immune cells. In contrast, IL-18 was inversely associated with lung cancer, while IL-13 showed a direct correlation.

Conclusion

The study underscores the role of immune and inflammatory factors in lung cancer. These insights could lead to new therapeutic strategies for combating lung cancer.

Identification of Siglec-10 as a new dendritic cell checkpoint for cervical cancer immunotherapy

BACKGROUND

The occurrence of chronic inflammation resulting from infection with human papillomaviruses is an important factor in the development of cervical cancer (CC); thus, deciphering the crosstalk between the tumor microenvironment and innate immune cells during the establishment of immune tolerance is vital for identifying potential treatment strategies.

METHODS

Single-cell RNA sequencing data and primary tumor samples from patients with CC were used to evaluate the functional role of Siglec-10 on dendritic cells (DCs). Patient-derived tumor fragment platforms were used to examine the ability of Siglec-10 blockade to reinvigorate DC-mediate T-cell activation and tumor clearance.

RESULTS

Here, we demonstrated that Siglec-10 is a prominent inhibitory checkpoint for DCs infiltrated in CC. CC epithelial cells use their aberrant surface sialylated structures to induce the transformation of conventional DCs into phenotypes characterized by low immunogenicity and high immunotolerance. Additionally, Siglec-10+ DCs suppress the function of adaptive T cells via galectin-9 signaling to strengthen the immunosuppressive CC microenvironment. Disturbance of Siglec-10 signaling restored the DC-mediated tumoricidal response and increased adaptive T cells sensitivity to programmed cell death protein 1 inhibition.

CONCLUSION

Our study confirms the checkpoint role of Siglec-10 on DCs and proposes that targeting Siglec-10 may be a promising avenue for immunotherapy against CC.

Immune cell-mediated features of atherosclerosis

Atherosclerosis is a chronic inflammatory disease characterized by innate and adaptive immune responses, which seriously threatens human life and health. It is a primary cause of coronary heart disease, myocardial infarction, and peripheral vascular disease. Research has demonstrated that immune cells are fundamental to the development of atherosclerosis and chronic inflammation. Therefore, it is anticipated that immunotherapy targeting immune cells will be a novel technique in the management of atherosclerosis. This article reviews the growth of research on the regulatory role of immune cells in atherosclerosis and targeted therapy approaches. The purpose is to offer new therapeutic approaches for the control and treatment of cardiovascular illnesses caused by atherosclerosis.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.