Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics

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.

Genomic and immune heterogeneity of multiple synchronous lung adenocarcinoma at different developmental stages

Multiple synchronous lung cancers (MSLCs) constitute a unique subtype of lung cancer. To explore the genomic and immune heterogeneity across different pathological stages of MSLCs, we analyse 16 MSLCs from 8 patients using single-cell RNA-seq, single-cell TCR sequencing, and bulk whole-exome sequencing. Our investigation indicates clonally independent tumours with convergent evolution driven by shared driver mutations. However, tumours from the same individual exhibit few shared mutations, indicating independent origins. During the transition from pre-invasive to invasive adenocarcinoma, we observe a shift in T cell phenotypes characterized by increased Treg cells and exhausted CD8+ T cells, accompanied by diminished cytotoxicity. Additionally, invasive adenocarcinomas exhibit greater neoantigen abundance and a more diverse TCR repertoire, indicating heightened heterogeneity. In summary, despite having a common genetic background and environmental exposure, our study emphasizes the individuality of MSLCs at different stages, highlighting their unique genomic and immune characteristics.

Deciphering the prognostic role of endoplasmic reticulum stress in lung adenocarcinoma: integrating prognostic prediction and immunotherapy strategies

Endoplasmic reticulum stress (ERS) is a critical factor influencing lung adenocarcinoma (LUAD) progression and patient outcomes. In this study, we analyzed gene expression data from LUAD samples sourced from The Cancer Genomic Atlas and Gene Expression Omnibus databases. Utilizing advanced statistical methods including LASSO and Cox regression, we developed a ERS-associated signature (ERAS) based on ten ERS-related genes. This model stratified patients into high- and low-risk groups, with the high-risk group exhibiting decreased survival rates, elevated tumor mutational burden, and heightened chemotherapy sensitivity. Additionally, we observed lower immune and ESTIMATE scores in the high-ERAS group, indicating a potentially compromised immune response. Experimental validation through quantitative real-time polymerase chain reaction confirmed the utility of our model. Furthermore, we constructed a nomogram to predict 1-, 3-, and 5-year survival rates, providing clinicians with a valuable tool for personalized patient management. In conclusion, our study demonstrates the efficacy of the ERAS in identifying high-ERAS LUAD patients, offering promising implications for improved prognostication and treatment strategies.

Multiplexed spatial transcriptomics methods and the application of expansion microscopy

While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.

Integration tools for scRNA-seq data and spatial transcriptomics sequencing data

Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application. Researchers need to select the most suitable method for their research purposes based on the actual situation. This review article presents a compilation of 19 integration methods sourced from a wide range of available approaches, serving as a comprehensive reference for researchers to select the suitable integration method for their specific research inquiries. By understanding the principles of these methods, we can identify their similarities and differences, comprehend their applicability and potential complementarity, and lay the foundation for future method development and understanding. This review article presents 19 methods that aim to integrate scRNA-seq data and spatial transcriptomics data. The methods are classified into two main groups and described accordingly. The article also emphasizes the incorporation of High Variance Genes in annotating various technologies, aiming to obtain biologically relevant information aligned with the intended purpose.

Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine

Chemotherapeutics, radiation, targeted therapeutics, and immunotherapeutics each demonstrate clinical benefits for a small subset of patients with solid malignancies. Immune cells infiltrating the tumor and the surrounding stroma play a critical role in shaping cancer progression and modulating therapy response. They do this by interacting with the other cellular and molecular components of the tumor microenvironment. Spatial multi-omics technologies are rapidly evolving. Currently, such technologies allow high-throughput RNA and protein profiling and retain geographical information about the tumor microenvironment cellular architecture and the functional phenotype of tumor, immune, and stromal cells. An in-depth spatial characterization of the heterogeneous tumor immune landscape can improve not only the prognosis but also the prediction of therapy response, directing cancer patients to more tailored and efficacious treatments. This review highlights recent advancements in spatial transcriptomics and proteomics profiling technologies and the ways these technologies are being applied for the dissection of the immune cell composition in solid malignancies in order to further both basic research in oncology and the implementation of precision treatments in the clinic.

Unraveling dynamic immunological landscapes in intracerebral hemorrhage: insights from single-cell and spatial transcriptomic profiling

Intracerebral hemorrhage (ICH) poses a formidable challenge in stroke management, with limited therapeutic options, particularly in the realm of immune-targeted interventions. Clinical trials targeting immune responses post-ICH have encountered setbacks, potentially attributable to the substantial cellular heterogeneity and intricate intercellular networks within the brain. Here, we present a pioneering investigation utilizing single-cell RNA sequencing and spatial transcriptome profiling at hyperacute (1 h), acute (24 h), and subacute (7 days) intervals post-ICH, aimed at unraveling the dynamic immunological landscape and spatial distributions within the cerebral tissue. Our comprehensive analysis revealed distinct cell differentiation patterns among myeloid and lymphocyte populations, along with delineated spatial distributions across various brain regions. Notably, we identified a subset of lymphocytes characterized by the expression of Spp1 and Lyz2, termed macrophage-associated lymphocytes, which exhibited close interactions with myeloid cells. Specifically, we observed prominent interactions between Lgmn+Macro-T cells and microglia through the spp1-cd44 pathway during the acute phase post-ICH in the choroid plexus. These findings represent a significant advancement in our understanding of immune cell dynamics at single-cell resolution across distinct post-ICH time points, thereby laying the groundwork for exploring critical temporal windows and informing the development of targeted therapeutic strategies.

Foundation models in molecular biology

Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.

Smoking-induced CCNA2 expression promotes lung adenocarcinoma tumorigenesis by boosting AT2/AT2-like cell differentiation

Lung adenocarcinoma (LUAD), a type of non-small cell lung cancer (NSCLC), originates from not only bronchial epithelial cells but also alveolar type 2 (AT2) cells, which could differentiate into AT2-like cells. AT2-like cells function as cancer stem cells (CSCs) of LUAD tumorigenesis to give rise to adenocarcinoma. However, the mechanism underlying AT2 cell differentiation into AT2-like cells in LUAD remains unknown. We analyze genes differentially expressed and genes with significantly different survival curves in LUAD, and the combination of these two analyses yields 147 differential genes, in which 14 differentially expressed genes were enriched in cell cycle pathway. We next analyze the protein levels of these genes in LUAD and find that Cyclin-A2 (CCNA2) is closely associated with LUAD tumorigenesis. Unexpectedly, high CCNA2 expression in LUAD is restrictedly associated with smoking and independent of other driver mutations. Single-cell sequencing analyses reveal that CCNA2 is predominantly involved in AT2-like cell differentiation, while inhibition of CCNA2 significantly reverses smoking-induced AT2-like cell differentiation. Mechanistically, CCNA2 binding to CDK2 phosphorylates the AXIN1 complex, which in turn induces ubiquitination-dependent degradation of β-catenin and inhibits the WNT signaling pathway, thereby failing AT2 cell maintenance. These results uncover smoking-induced CCNA2 overexpression and subsequent WNT/β-catenin signaling inactivation as a hitherto uncharacterized mechanism controlling AT2 cell differentiation and LUAD tumorigenesis.

[Advancements in Single-cell RNA Sequencing Technology in the Study of the Tumor Microenvironment in Lung Cancer]

The immune microenvironment plays a key role in the development and progression of tumors. In recent years, with the rapid advancement of high-throughput sequencing technologies, researchers have gained a deeper understanding of the composition and function of immune cells in the tumor microenvironment. However, traditional bulk sequencing technologies are limited in resolving heterogeneity at the single-cell level, constraining a comprehensive understanding of the complexity of the tumor microenvironment. The advent of single-cell RNA sequencing technology has brought new opportunities to uncover the heterogeneity of the immune microenvironment in lung cancer. Currently, T-cell-centered immunotherapy in clinical settings is prone to side effects affecting prognosis, such as immunogenic drug resistance or immune-related pneumonia, with the key factor being changes in the interactions between immune cells and tumor cells in the tumor microenvironment. Single-cell RNA sequencing technology can reveal the origins and functions of different subgroups within the tumor microenvironment from perspectives such as intercellular interactions and pseudotime analysis, thereby discovering new cell subgroups or novel biomarkers, providing new avenues for uncovering resistance to immunotherapy and monitoring therapeutic efficacy. This review comprehensively discusses the newest research techniques and advancements in single-cell RNA sequencing technology for unveiling the heterogeneity of the tumor microenvironment after lung cancer immunotherapy, offering insights for enhancing the precision and personalization of immunotherapy.
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Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

Patho-transcriptomic analysis of invasive mucinous adenocarcinoma of the lung (IMA): comparison with lung adenocarcinoma with signet ring cell features (SRCC)

Background

Invasive mucinous adenocarcinoma (IMA) comprises ∼5% of lung adenocarcinoma. There is no effective therapy for IMA when surgical resection is not possible. IMA is sometimes confused with adenocarcinoma with signet ring cell features (SRCC) pathologically since both adenocarcinomas feature tumor cells with abundant intracellular mucin. The molecular mechanisms by which such mucin-producing lung adenocarcinomas develop remain unknown.

Methods

Using a Visium spatial transcriptomics approach, we analyzed IMA and compared it with SRCC patho-transcriptomically. Combining spatial transcriptomics data with in vitro studies using RNA-seq and ChIP-seq, we assessed downstream targets of transcription factors HNF4A and SPDEF that are highly expressed in IMA and/or SRCC.

Results

Spatial transcriptomics analysis indicated that there are 6 distinct cell clusters in IMA and SRCC. Notably, two clusters (C1 and C3) of mucinous tumor cells exist in both adenocarcinomas albeit at a different ratio. Importantly, a portion of genes (e.g., NKX2-1 , GKN1 , HNF4A and FOXA3 ) are distinctly expressed while some mucous-related genes (e.g., SPDEF and FOXA2 ) are expressed in both adenocarcinomas. We determined that HNF4A induces MUC3A/B and TM4SF4 and that BI 6015, an HNF4A antagonist, suppressed the growth of IMA cells. Using mutant SPDEF that is associated with COVID-19, we also determined that an intact DNA-binding domain of SPDEF is required for SPDEF-mediated induction of mucin genes ( MUC5AC , MUC5B and AGR2 ). Additionally, we found that XMU-MP-1, a SPDEF inhibitor, suppressed the growth of IMA cells.

Conclusion

These results revealed that IMA and SRCC contain heterogenous tumor cell types, some of which are targetable.

The diagnostic potential role of thioredoxin reductase and TXNRD1 in early lung adenocarcinoma: A cohort study

Background

Lung adenocarcinoma (LUAD) is the primary form of lung cancer, yet the reliable biomarkers for early diagnosis remain insufficient. Thioredoxin reductase (TrxR) is strongly linked to the occurrence, development, and drug resistance of lung cancer, making it a potential biomarker. However, further research is required to assess its diagnostic value in LUAD.

Methods

A retrospective analysis was performed on patients who underwent pulmonary nodule resection at our center from 2018 to 2022. Clinical data, including preoperative TrxR levels, imaging, and laboratory characteristics, were identified as study variables. Two prediction models were constructed using multiple logistic regression, and their prediction performance was evaluated comprehensively. Besides, bioinformatics analyses of TrxR coding genes including differential expression, functional enrichment, immune infiltration, drug sensitivity, and single-cell landscape were performed based on TCGA database, which were subsequently validated by Human Protein Atlas.

Results

A total of 506 eligible patients (72 benign lesions, 77 AISs, 185 MIAs and 172 IACs) were identified in the clinical cohort. Two TrxR-based models were developed, which were able to distinguish between benign and malignant pulmonary nodules, as well as pathological subtypes of LUAD, respectively. The models exhibited good predictive ability with all AUC values ranging from 0.7 to 0.9. Based on calibration curves and clinical decision analysis, the nomogram models showed high reliability. Functional analysis indicated that TXNRD1 primarily participated in cell cycle and lipid metabolism. Immune infiltration analysis showed that TXNRD1 has a strong association with immune cells and could impact immunotherapy. Then, we identified small molecular compounds that inhibit TXNRD1 and confirmed TXNRD1 expression by single-cell landscape and immunohistochemistry.

Conclusion

This study validated the diagnostic value of TrxR and TXNRD1 in clinical cohorts and transcriptional data, respectively. TrxR and TXNRD1 could be used in the risk diagnosis of early LUAD and facilitate personalized treatment strategies.

Integrated whole-exome and bulk transcriptome sequencing delineates the dynamic evolution from preneoplasia to invasive lung adenocarcinoma featured with ground-glass nodules

OBJECTIVE

The genomic and molecular ecology involved in the stepwise continuum progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) remains unclear and requires further elucidation. We aimed to characterize gene mutations and expression landscapes, and explore the association between differentially expressed genes (DEGs) and significantly mutated genes (SMGs) during the dynamic evolution from AIS to IAC.

METHODS

Thirty-five patients with ground-glass nodules (GGNs) lung adenocarcinomas were enrolled. Whole-exome sequencing (WES) and transcriptome sequencing (RNA-Seq) were conducted on all patients, encompassing both tumor samples and corresponding noncancerous tissues. Data obtained from WES and RNA-Seq were subsequently analyzed.

RESULTS

The findings from WES delineated that the predominant mutations were observed in EGFR (49%) and ANKRD36C (17%). SMGs, including EGFR and RBM10, were associated with the dynamic evolution from AIS to IAC. Meanwhile, DEGs, including GPR143, CCR9, ADAMTS16, and others were associated with the entire process of invasive LUAD. We found that the signaling pathways related to cell migration and invasion were upregulated, and the signaling pathways of angiogenesis were downregulated across the pathological stages. Furthermore, we found that the messenger RNA (mRNA) levels of FAM83A, MAL2, DEPTOR, and others were significantly correlated with CNVs. Gene set enrichment analysis (GSEA) showed that heme metabolism and cholesterol homeostasis pathways were significantly upregulated in patients with EGFR/RBM10 co-mutations, and these patients may have poorer overall survival than those with EGFR mutations. Based on the six calculation methods for the immune infiltration score, NK/CD8+ T cells decreased, and Treg/B cells increased with the progression of early LUAD.

CONCLUSIONS

Our findings offer valuable insights into the unique genomic and molecular features of LUAD, facilitating the identification and advancement of precision medicine strategies targeting the invasive progression of LUAD from AIS to IAC.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing

Tumor immune microenvironment (TIME) and its indications for lung cancer patient prognosis and therapeutic response have become new hotspots in cancer research in recent years. Tumor cells, immune cells, various regulatory factors, and their interactions in the TIME have been suggested to commonly influence lung cancer development and therapeutic outcome. The heterogeneity of TIME is composed of dynamic immune-related components, including various cancer cells, immune cells, cytokine/chemokine environments, cytotoxic activity, or immunosuppressive factors. The specific composition of cell subtypes may facilitate or hamper the response to immunotherapy and influence patient prognosis. Various markers have been found to stratify the patient prognosis or predict the therapeutic outcome. In this article, we systematically reviewed the recent advancement of TIME studies in lung adenocarcinoma (LUAD) using single-cell RNA sequencing (scRNA-seq) techniques, with specific focuses on the roles of TIME in LUAD development, TIME heterogeneity, indications of TIME in patient prognosis and therapeutic response during immunotherapy and drug resistance. The main findings in TIME heterogeneity and relevant markers or models for prognosis stratification and response prediction have been summarized. We hope that this review provides an overview of TIME status in LUAD and an inspiration for future development of strategies and biomarkers in LUAD treatment.

Purine metabolism in lung adenocarcinoma: A single-cell analysis revealing prognostic and immunotherapeutic insights

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer, yet the contribution of purine metabolism (PM) to its pathogenesis remains poorly elucidated. PM, a critical component of intracellular nucleotide synthesis and energy metabolism, is hypothesized to exert a significant influence on LUAD development. Herein, we employed single-cell analysis to investigate the role of PM within the tumour microenvironment (TME) of LUAD. PM scoring (PMS) across distinct cell types was determined using AUCell, UCell, singscore and AddModuleScore algorithms. Subsequently, we explored communication networks among cells within high- and low-PMS groups, establishing a robust PM-associated signature (PAS) utilizing a comprehensive dataset comprising LUAD samples from TCGA and five GEO datasets. Our findings revealed that the high-PMS group exhibited intensified cell interactions, while the PAS, constructed using PM-related genes, demonstrated precise prognostic predictive capability. Notably, analysis across the TCGA dataset and five GEO datasets indicated that low-PAS patients exhibited a superior prognosis. Furthermore, the low-PAS group displayed increased immune cell infiltration and elevated CD8A expression, coupled with reduced PD-L1 expression. Moreover, data from eight publicly available immunotherapy cohorts suggested enhanced immunotherapy outcomes in the low-PAS group. These results underscore a close association between PAS and tumour immunity, offering predictive insights into genomic alterations, chemotherapy drug sensitivity and immunotherapy responses in LUAD. The newly established PAS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

Identification of the key DNA damage response genes for predicting immunotherapy and chemotherapy efficacy in lung adenocarcinoma based on bulk, single-cell RNA sequencing, and spatial transcriptomics

BACKGROUND

Immune checkpoint inhibitors (ICI) plus chemotherapy is the preferred first-line treatment for advanced driver-negative lung adenocarcinoma (LUAD). The DNA damage response (DDR) is the main mechanism underlying chemotherapy resistance, and EGLN3 is a key DDR component.

METHOD

We conducted an analysis utilizing TCGA and GEO databases employing multiple labels-WGCNA, DEGs, and prognostic assessments. Using bulk RNA-seq and scRNA-seq data, we isolated EGLN3 as the single crucial DDR gene. Spatial transcriptome analysis revealed the spatial differential distribution of EGLN3. TIDE/IPS scores and pRRophetic/oncoPredict R packages were used to predict resistance to ICI and chemotherapy drugs, respectively.

RESULTS

EGLN3 was overexpressed in LUAD tissues (p < 0.001), with the high EGLN3 expression group exhibiting a poor prognosis (p = 0.00086, HR: 1.126 [1.039-1.22]). Spatial transcriptome analysis revealed EGLN3 overexpression in cancerous and hypoxic regions, positively correlating with DDR-related and TGF-β pathways. Drug response predictions indicated EGLN3's resistance to the common chemotherapy drugs, including cisplatin (p = 6.1e-14), docetaxel (p = 1.1e-07), and paclitaxel (p = 4.2e-07). Furthermore, on analyzing the resistance mechanism, we found that EGLN3 regulated DDR-related pathways and induced chemotherapy resistance. Additionally, EGLN3 influenced TGF-β signaling, Treg cells, and cancer-associated fibroblast cells, culminating in immunotherapy resistance. Moreover, validation using real-world data, such as GSE126044, GSE135222, and, IMvigor210, substantiated the response trends to immunotherapy and chemotherapy.

CONCLUSIONS

EGLN3 emerges as a potential biomarker predicting lower response to both immunotherapy and chemotherapy, suggesting its promise as a therapeutic target in advanced LUAD.

A review of natural products targeting tumor immune microenvironments for the treatment of lung cancer

Lung cancer (LC) produces some of the most malignant tumors in the world, with high morbidity and mortality. Tumor immune microenvironment (TIME), a component of the tumor microenvironment (TME), are critical in tumor development, immune escape, and drug resistance. The TIME is composed of various immune cells, immune cytokines, etc, which are important biological characteristics and determinants of tumor progression and outcomes. In this paper, we reviewed the recently published literature and discussed the potential uses of natural products in regulating TIME. We observed that a total of 37 natural compounds have been reported to exert anti-cancer effects by targeting the TIME. In different classes of natural products, terpenoids are the most frequently mentioned compounds. TAMs are one of the most investigated immune cells about therapies with natural products in TIME, with 9 natural products acting through it. 17 natural products exhibit anti-cancer properties in LC by modulating PD-1 and PD-L1 protein activity. These natural products have been extensively evaluated in animal and cellular LC models, but their clinical trials in LC patients are lacking. Based on the current review, we have revealed that the mechanisms of LC can be treated with natural products through TIME intervention, resulting in a new perspective and potential therapeutic drugs.

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma

BACKGROUND

Patients who possess various histological subtypes of early-stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early-stage LUAD due to intratumour intricacy.

METHODS

We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single-cell RNA sequencing (scRNA-seq) was carried out on matched tumour and normal tissue. Three formalin-fixed and paraffin-embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP).

RESULTS

Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA-seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = -.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD-L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region.

CONCLUSIONS

These findings illustrate that assessing patient histological subtypes at the single-cell level is feasible. Additionally, tumour endothelial cells that express PD-L1 in stage IA LUAD suppress immune-responsive CD8+ T cells.

Lung regeneration: diverse cell types and the therapeutic potential

Lung tissue has a certain regenerative ability and triggers repair procedures after injury. Under controllable conditions, lung tissue can restore normal structure and function. Disruptions in this process can lead to respiratory system failure and even death, causing substantial medical burden. The main types of respiratory diseases are chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), and acute respiratory distress syndrome (ARDS). Multiple cells, such as lung epithelial cells, endothelial cells, fibroblasts, and immune cells, are involved in regulating the repair process after lung injury. Although the mechanism that regulates the process of lung repair has not been fully elucidated, clinical trials targeting different cells and signaling pathways have achieved some therapeutic effects in different respiratory diseases. In this review, we provide an overview of the cell type involved in the process of lung regeneration and repair, research models, and summarize molecular mechanisms involved in the regulation of lung regeneration and fibrosis. Moreover, we discuss the current clinical trials of stem cell therapy and pharmacological strategies for COPD, IPF, and ARDS treatment. This review provides a reference for further research on the molecular and cellular mechanisms of lung regeneration, drug development, and clinical trials.

SORC: an integrated spatial omics resource in cancer

The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722 899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencing data in the majority of spatial transcriptomics datasets, involving 334 379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell-cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.

A gene-based score for the risk stratification of stage IA lung adenocarcinoma

OBJECTIVE

We aim to molecularly stratify stage IA lung adenocarcinoma (LUAD) for precision medicine.

METHODS

Twelve multi-institution datasets (837 cases of IA) were used to classify the high- and low-risk types (based on survival status within 5 years), and the biological differences were compared. Then, a gene-based classifying score (IA score) was trained, tested and validated by several machine learning methods. Furthermore, we estimated the significance of the IA score in the prognostic assessment, chemotherapy prediction and risk stratification of stage IA LUAD. We also developed an R package for the clinical application. The SEER database (15708 IA samples) and TCGA Pan-Cancer (1881 stage I samples) database were used to verify clinical significance.

RESULTS

Compared with the low-risk group, the high-risk group of stage IA LUAD has obvious enrichment of the malignant pathway and more driver mutations and copy number variations. The effect of the IA score on the classification of high- and low-risk stage IA LUAD was much better than that of classical clinicopathological factors (training set: AUC = 0.9, validation set: AUC = 0.7). The IA score can significantly predict the prognosis of stage IA LUAD and has a prognostic effect for stage I pancancer. The IA score can effectively predict chemotherapy sensitivity and occult metastasis or invasion in stage IA LUAD. The R package IAExpSuv has a good risk probability prediction effect for both groups and single stages of IA LUAD.

CONCLUSIONS

The IA score can effectively stratify the risk of stage IA LUAD, offering good assistance in precision medicine.

Whole-genome sequencing reveals the molecular implications of the stepwise progression of lung adenocarcinoma

The mechanism underlying the development of tumors, particularly at early stages, still remains mostly elusive. Here, we report whole-genome long and short read sequencing analysis of 76 lung cancers, focusing on very early-stage lung adenocarcinomas such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma. The obtained data is further integrated with bulk and spatial transcriptomic data and epigenomic data. These analyses reveal key events in lung carcinogenesis. Minimal somatic mutations in pivotal driver mutations and essential proliferative factors are the only detectable somatic mutations in the very early-stage of AIS. These initial events are followed by copy number changes and global DNA hypomethylation. Particularly, drastic changes are initiated at the later AIS stage, i.e., in Noguchi type B tumors, wherein cancer cells are exposed to the surrounding microenvironment. This study sheds light on the pathogenesis of lung adenocarcinoma from integrated pathological and molecular viewpoints.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

Behind the Indolent Facade: Uncovering the Molecular Features and Malignancy Potential in Lung Minimally Invasive Adenocarcinoma by Single-Cell Transcriptomics

The increased use of low-dose computed tomography screening has led to more frequent detection of early stage lung tumors, including minimally invasive adenocarcinoma (MIA). To unravel the intricacies of tumor cells and the immune microenvironment in MIA, this study performs a comprehensive single-cell transcriptomic analysis and profiles the transcriptomes of 156,447 cells from fresh paired MIA and invasive adenocarcinoma (IA) tumor samples, peripheral blood mononuclear cells, and adjacent normal tissue samples from three patients with synchronous multiple primary lung adenocarcinoma. This study highlights a connection and heterogeneity between the tumor ecosystem of MIA and IA. MIA tumor cells exhibited high expression of aquaporin-1 and angiotensin II receptor type 2 and a basal-like molecular character. Furthermore, it identifies that cathepsin B+ tumor-associated macrophages may over-activate CD8+ T cells in MIA, leading to an enrichment of granzyme K+ senescent CD8+ T cells, indicating the possibility of malignant progression behind the indolent appearance of MIA. These findings are further validated in 34 MIA and 35 IA samples by multiplexed immunofluorescence. These findings provide valuable insights into the mechanisms that maintain the indolent nature and prompt tumor progression of MIA and can be used to develop more effective therapeutic targets and strategies for MIA patients.

Combining single-cell RNA sequencing data and transcriptomic data to unravel potential mechanisms and signature genes of the progression of idiopathic pulmonary fibrosis to lung adenocarcinoma and predict therapeutic agents

Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, although the underlying association is unclear. The raw data involved were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs). Genes intersecting DEGs and MSGs were considered hub genes for IPF and LUAD. Machine learning algorithms were applied to capture epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. Immunohistochemical staining and K-M plots were used to denote the prognostic value of EDSGs in LUAD. Based on EDSGs, we constructed a TF-gene-miRNA regulatory network. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Epithelial cells, 650 DEGs, and 1773 MSGs were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these genes shared 8 items with 379 hub genes. Through the machine learning algorithms, we further fished TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves emphasized the significance of EDSGs in predicting the onset of LUAD and IPF. The TF-gene-miRNA network revealed regulatory relationships behind EDSGs. Finally, we predicted appropriate therapeutic agents. Our study preliminarily identified potential mechanisms between IPF and LUAD, which will inform subsequent studies.

Machine Learning-Based Integration Develops a Macrophage-Related Index for Predicting Prognosis and Immunotherapy Response in Lung Adenocarcinoma

BACKGROUND

Macrophages play a critical role in tumor immune microenvironment (TIME) formation and cancer progression in lung adenocarcinoma (LUAD). However, few studies have comprehensively and systematically described the characteristics of macrophages in LUAD.

METHODS

This study identified macrophage-related markers with single-cell RNA sequencing data from the GSE189487 dataset. An integrative machine learning-based procedure based on 10 algorithms was developed to construct a macrophage-related index (MRI) in The Cancer Genome Atlas (TCGA), GSE30219, GSE31210, and GSE72094 datasets. Several algorithms were used to evaluate the associations of MRI with TIME and immunotherapy-related biomarkers. The role of MRI in predicting the immunotherapy response was evaluated with the GSE91061 dataset.

RESULTS

The optimal MRI constructed by the combination of the Lasso algorithm and plsRCox was an independent risk factor in LUAD and showed a stable and powerful performance in predicting the overall survival rate of patients with LUAD. Those with low MRI scores had a higher TIME score, a higher level of immune cells, a higher immunophenoscore, and a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a better response to immunotherapy. The IC50 value of common drugs for chemotherapy and target therapy with low MRI scores was higher compared to high MRI scores. Moreover, the survival prediction nomogram, developed from MRI, had good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of LUAD.

CONCLUSION

Our study constructed for the first time a consensus MRI for LUAD with 10 machine learning algorithms. The MRI could be helpful for risk stratification, prognosis, and selection of treatment approach in LUAD.

Combined Inhibition of UBE2C and PLK1 Reduce Cell Proliferation and Arrest Cell Cycle by Affecting ACLY in Pan-Cancer

Various studies have shown that the cell-cycle-related regulatory proteins UBE2C, PLK1, and BIRC5 promote cell proliferation and migration in different types of cancer. However, there is a lack of in-depth and systematic research on the mechanism of these three as therapeutic targets. In this study, we found a positive correlation between the expression of UBE2C and PLK1/BIRC5 in the Cancer Genome Atlas (TCGA) database, revealing a potential combination therapy candidate for pan-cancer. Quantitative real-time PCR (qRT-PCR), Western blotting (WB), cell phenotype detection, and RNA-seq techniques were used to evidence the effectiveness of the combination candidate. We found that combined interference of UBE2C with PLK1 and UBE2C with BIRC5 affected metabolic pathways by significantly downregulating the mRNA expression of IDH1 and ACLY, which was related to the synthesis of acetyl-CoA. By combining the PLK1 inhibitor volasertib and the ACLY inhibitor bempedoic acid, it showed a higher synergistic inhibition of cell viability and higher synergy scores in seven cell lines, compared with those of other combination treatments. Our study reveals the potential mechanisms through which cell-cycle-related genes regulate metabolism and proposes a potential combined targeted therapy for patients with higher PLK1 and ACLY expression in pan-cancer.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

The seductive allure of spatial biology: accelerating new discoveries in the life sciences

Spatial biology is a rapidly developing field which enables the visualization of protein and transcriptomic data while preserving tissue context and architecture. Initially used in discovery, there is growing promise for translational and diagnostic assay developments. Immediate applications are in precision medicine, such as being able to match patients to optimal therapies through better understanding the tumor microenvironment. However, it also has ramifications for many other disciplines (e.g. immunology, cancer, infectious disease and digital pathology). With increasingly massive data sets being generated, data storage, curation, analysis and sharing require more computational approaches and artificial intelligence-powered tools to fully utilize spatial tools. Here, we discuss spatial biology as an important convergent science approach to tackling complex global challenges in areas such as health.

ELOVL6 is associated with immunosuppression in lung adenocarcinoma through bioinformatics analysis

The aim of this paper was to reveal the correlation between the expression of ELOVL fatty acid elongase 6 (ELOVL6) gene in lung adenocarcinoma (LUAD) and its clinical significance, immune cell infiltration level and prognosis. Expression profile data of ELOVL6 mRNA were collected from the cancer genome atlas database to analyze the differences in ELOVL6 mRNA expression in LUAD tissues and normal lung tissues, and to analyze the correlation between ELOVL6 and information on clinicopathological features. Based on TIMER database, TISDIB database and GEPIA2 database, the correlation between ELOVL6 expression and tumor immune cell infiltration in LUAD was analyzed. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of ELOVL6-related co-expressed genes were performed to identify the involved signaling pathways and to construct their co-expressed gene protein interaction networks. Drugs affected by ELOVL6 expression were screened based on the Cell Miner database. These findings suggest that ELOVL6 plays an important role in the course of LUAD, and the expression level of this gene has a close relationship with clinicopathological characteristics and survival prognosis, and has the potential to become a prognostic marker and therapeutic target for LUAD.

How single-cell techniques help us look into lung cancer heterogeneity and immunotherapy

Lung cancer patients tend to have strong intratumoral and intertumoral heterogeneity and complex tumor microenvironment, which are major contributors to the efficacy of and drug resistance to immunotherapy. From a new perspective, single-cell techniques offer an innovative way to look at the intricate cellular interactions between tumors and the immune system and help us gain insights into lung cancer and its response to immunotherapy. This article reviews the application of single-cell techniques in lung cancer, with focuses directed on the heterogeneity of lung cancer and the efficacy of immunotherapy. This review provides both theoretical and experimental information for the future development of immunotherapy and personalized treatment for the management of lung cancer.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Identification of immunotherapy biomarkers for improving the clinical outcome of homologous recombination deficiency patients with lung adenocarcinoma

Homologous recombination deficiency (HRD) is a common molecular signature of genomic instability and has been shown to be a biomarker for targeted therapies. However, there is a lack of studies on the role of HRD changes in lung adenocarcinoma (LUAD) transcriptomics. HRD scores were determined using single nucleotide polymorphism (SNP) array data from LUAD patients from The Cancer Genome Atlas (TCGA) database. Transcriptional data from patients with different scores were analyzed to identify biomarkers associated with HRD. Candidate biomarkers were validated using Gene Expression Omnibus (GEO)-sourced datasets and an immunotherapy cohort. According to the bulk transcriptome and clinical characteristics of 912 LUAD patients and Single-cell RNA-seq of 9 LUAD patients from TCGA and GEO databases, we observed increased MS4A6A expression in HRD tumors; high MS4A6A expression predicted improved survival outcomes. Furthermore, a comprehensive analysis of the tumor immune microenvironment (TIME) revealed a positive correlation between MS4A6A expression and neoantigen loading and immune cell infiltration. Additionally, the immunotherapy cohort confirmed the possibility of using MS4A6A as a biomarker. Collectively, we suggest that MS4A6A is associated with HRD and provide a new perspective toward identifying promising biomarkers for immunotherapy.

Promising immunotherapeutic targets in lung cancer based on single-cell RNA sequencing

Immunotherapy has made great strides in the treatment of lung cancer, but a significant proportion of patients still do not respond to treatment. Therefore, the identification of novel targets is crucial to improving the response to immunotherapy. The tumor microenvironment (TME) is a complex niche composed of diverse pro-tumor molecules and cell populations, making the function and mechanism of a unique cell subset difficult to understand. However, the advent of single-cell RNA sequencing (scRNA-seq) technology has made it possible to identify cellular markers and understand their potential functions and mechanisms in the TME. In this review, we highlight recent advances emerging from scRNA-seq studies in lung cancer, with a particular focus on stromal cells. We elucidate the cellular developmental trajectory, phenotypic remodeling, and cell interactions during tumor progression. Our review proposes predictive biomarkers and novel targets for lung cancer immunotherapy based on cellular markers identified through scRNA-seq. The identification of novel targets could help improve the response to immunotherapy. The use of scRNA-seq technology could provide new strategies to understand the TME and develop personalized immunotherapy for lung cancer patients.