Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer

T cell-related circRNA pairs to predict prognosis of patients with esophageal squamous cell carcinoma

The prognosis for esophageal squamous cell carcinoma (ESCC), a prevalent and aggressive form of cancer, remains poor despite advancements in treatment options. Addressing the gap in comprehensive prognostic information derived from circRNA expression profiles for ESCC, our study aimed to establish a linkage between circRNA expressions and ESCC prognosis. To achieve this, we first developed an optimized prognostic model named T cell-related risk score (TRRS), which integrates T cell-associated features with machine learning algorithms. In parallel, we re-analyzed existing RNA-seq datasets to redefine the expression profiles of circRNAs and mRNAs. Utilizing the TRRS as a foundational "bridge," we identified circRNAs correlated with TRRS, leading to the development of a novel circRNA pair-based prognostic model, the TCRS, which is independent of specific expression levels. Further investigations uncovered two circRNAs, circNLK(5,6,7).1 and circRC3H1(2).1, with potential functional significance. These findings underscore the utility of these risk scores as tools for predicting overall survival and identifying potential therapeutic targets for ESCC patients.

Exploring cellular diversity in lung adenocarcinoma epithelium: Advancing prognostic methods and immunotherapeutic strategies

Immunotherapy has brought significant advancements in the treatment of lung adenocarcinoma (LUAD), but identifying suitable candidates remains challenging. In this study, we investigated tumour cell heterogeneity using extensive single-cell data and explored the impact of different tumour cell cluster abundances on immunotherapy in the POPLAR and OAK immunotherapy cohorts. Notably, we found a significant correlation between CKS1B+ tumour cell abundance and treatment response, as well as stemness potential. Leveraging marker genes from the CKS1B+ tumour cell cluster, we employed machine learning algorithms to establish a prognostic and immunotherapeutic signature (PIS) for LUAD. In multiple cohorts, PIS outperformed 144 previously published signatures in predicting LUAD prognosis. Importantly, PIS reliably predicted genomic alterations, chemotherapy sensitivity and immunotherapy responses. Immunohistochemistry validated lower expression of immune markers in the low-PIS group, while in vitro experiments underscored the role of the key gene PSMB7 in LUAD progression. In conclusion, PIS represents a novel biomarker facilitating the selection of suitable LUAD patients for immunotherapy, ultimately improving prognosis and guiding clinical decisions.

Single-cell and Spatial Transcriptomic Analyses Implicate Formation of the Immunosuppressive Microenvironment during Breast Tumor Progression

Ductal carcinoma in situ and invasive ductal carcinoma represent two stages of breast cancer progression. A multitude of studies have shown that genomic instability increases during tumor development, as manifested by higher mutation and copy number variation rates. The advent of single-cell and spatial transcriptomics has enabled the investigation of the subtle differences in cellular states during the tumor progression at single-cell level, thereby providing more nuanced understanding of the intercellular interactions within the solid tumor. However, the evolutionary trajectory of tumor cells and the establishment of the immunosuppressive microenvironment during breast cancer progression remain unclear. In this study, we performed an exploratory analysis of the single-cell sequencing dataset of 13 ductal carcinoma in situ and invasive ductal carcinoma samples. We revealed that tumor cells became more malignant and aggressive during their progression, and T cells transited to an exhausted state. The tumor cells expressed various coinhibitory ligands that interacted with the receptors of immune cells to create an immunosuppressive tumor microenvironment. Furthermore, spatial transcriptomics data confirmed the spatial colocalization of tumor and immune cells, as well as the expression of the coinhibitory ligand-receptor pairs. Our analysis provides insights into the cellular and molecular mechanism underlying the formation of the immunosuppressive landscape during two typical stages of breast cancer progression.

SLIT3 deficiency promotes non-small cell lung cancer progression by modulating UBE2C/WNT signaling

In our prior research, it was noted that slit guidance ligand 3 (SLIT3), a member of the SLIT-secreted protein family, may play a potential role in tumorigenesis. In addition, our prior work has found that the SLIT3 gene is highly methylated, especially in advanced-stage lung cancer tissues. Herein, we propose the hypothesis that abnormal SLIT3 expression may be linked to lung cancer development. In this study, decreased SLIT3 at the transcriptome and proteome levels was observed in lung cancer tissues. Furthermore, the downregulation of SLIT3 was related to a higher tumor stage and poorer prognosis. Silencing SLIT3 expression enhanced cell proliferation and migration, indicating potential characteristics of a tumor suppressor gene of SLIT3 in non-small-cell lung cancer (NSCLC). Furthermore, SLIT3 deficiency stimulates UBE2C upregulation and regulates NSCLC progression through Wnt3A/β-catenin signaling. The activation of the WNT signaling pathway was highly correlated with chemoresistance development in lung cancer. In conclusion, SLIT3 deficiency promotes lung cancer onset and progression by modulating UBE2C/WNT signaling. SLIT3/UBE2C/WNT may serve as novel biomarkers and therapeutic targets in NSCLC.

RNA splicing junction landscape reveals abundant tumor-specific transcripts in human cancer

RNA splicing is a critical process governing gene expression and transcriptomic diversity. Despite its importance, a detailed examination of transcript variation at the splicing junction level remains scarce. Here, we perform a thorough analysis of RNA splicing junctions in 34,775 samples across multiple sample types. We identified 29,051 tumor-specific transcripts (TSTs) in pan-cancer, with a majority of these TSTs being unannotated. Our findings show that TSTs are positively correlated with tumor stemness and linked to unfavorable outcomes in cancer patients. Additionally, TSTs display mutual exclusivity with somatic mutations and are overrepresented in transposable-element-derived transcripts possessing oncogenic functions. Importantly, TSTs can generate putative neoantigens for immunotherapy. Moreover, TSTs can be detected in blood extracellular vesicles from cancer patients. Our results shed light on the intricacies of RNA splicing and offer promising avenues for cancer diagnosis and therapy.

Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.

Mimicking and analyzing the tumor microenvironment

The tumor microenvironment (TME) is increasingly appreciated to play a decisive role in cancer development and response to therapy in all solid tumors. Hypoxia, acidosis, high interstitial pressure, nutrient-poor conditions, and high cellular heterogeneity of the TME arise from interactions between cancer cells and their environment. These properties, in turn, play key roles in the aggressiveness and therapy resistance of the disease, through complex reciprocal interactions between the cancer cell genotype and phenotype, and the physicochemical and cellular environment. Understanding this complexity requires the combination of sophisticated cancer models and high-resolution analysis tools. Models must allow both control and analysis of cellular and acellular TME properties, and analyses must be able to capture the complexity at high depth and spatial resolution. Here, we review the advantages and limitations of key models and methods in order to guide further TME research and outline future challenges.

The Generation of ROS by Exposure to Trihalomethanes Promotes the IκBα/NF-κB/p65 Complex Dissociation in Human Lung Fibroblast

Background: Disinfection by-products used to obtain drinking water, including halomethanes (HMs) such as CH2Cl2, CHCl3, and BrCHCl2, induce cytotoxicity and hyperproliferation in human lung fibroblasts (MRC-5). Enzymes such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) modulate these damages through their biotransformation processes, potentially generating toxic metabolites. However, the role of the oxidative stress response in cellular hyperproliferation, modulated by nuclear factor-kappa B (NF-κB), remains unclear. Methods: In this study, MRC-5 cells were treated with these compounds to evaluate reactive oxygen species (ROS) production, lipid peroxidation, phospho-NF-κB/p65 (Ser536) levels, and the activities of SOD, CAT, and GPx. Additionally, the interactions between HMs and ROS with the IκBα/NF-κB/p65 complex were analyzed using molecular docking. Results: Correlation analysis among biomarkers revealed positive relationships between pro-oxidant damage and antioxidant responses, particularly in cells treated with CH2Cl2 and BrCHCl2. Conversely, negative relationships were observed between ROS levels and NF-κB/p65 levels in cells treated with CH2Cl2 and CHCl3. The estimated relative free energy of binding using thermodynamic integration with the p65 subunit of NF-κB was -3.3 kcal/mol for BrCHCl2, -3.5 kcal/mol for both CHCl3 and O2•, and -3.6 kcal/mol for H2O2. Conclusions: Chloride and bromide atoms were found in close contact with IPT domain residues, particularly in the RHD region involved in DNA binding. Ser281 is located within this domain, facilitating the phosphorylation of this protein. Similarly, both ROS interacted with the IPT domain in the RHD region, with H2O2 forming a side-chain oxygen interaction with Leu280 adjacent to the phosphorylation site of p65. However, the negative correlation between ROS and phospho-NF-κB/p65 suggests that steric hindrance by ROS on the C-terminal domain of NF-κB/p65 may play a role in the antioxidant response.

The role of NOP58 in prostate cancer progression through SUMOylation regulation and drug response

Background

Prostate cancer is one of the leading causes of cancer-related deaths in men. Its molecular pathogenesis is closely linked to various genetic and epigenetic alterations, including posttranslational modifications like SUMOylation. Identifying biomarkers that predict outcomes and specific therapeutic targets depends on a comprehensive understanding of these processes. With growing interest in SUMOylation as a mechanism affecting prostate cancer-related genes, this study aimed to investigate the central role of SUMOylation in prostate cancer prognostics, focusing on the significance of NOP58.

Methods

We conducted a comprehensive bioinformatics analysis, integrating differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and single-cell transcriptomic analyses using data from The Cancer Genome Atlas (TCGA). Key genes were identified through intersections of Venn diagrams, Boralta algorithm signatures, and machine learning models. These signaling mechanisms were validated through experimental studies, including immunohistochemical staining and gene ontology analyses.

Results

The dual-gene molecular subtype analysis with SUMO1, SUMO2, and XPO1 genes revealed significant differences in survival outcomes across molecular subtypes, further emphasizing the potential impact of NOP58 on SUMOylation, a key post-translational modification, in prostate cancer. NOP58 overexpression was strongly associated with shorter overall survival (OS), progression-free interval (PFI), and disease-specific death in prostate cancer patients. Immunohistochemical analysis confirmed that NOP58 was significantly overexpressed in prostate cancer tissues compared to normal tissues. ROC curve analysis demonstrated that NOP58 could distinguish prostate cancer from control samples with high diagnostic accuracy. Gene Ontology analysis, along with GSVA and GSEA, suggested that NOP58 may be involved in cell cycle regulation and DNA repair pathways. Moreover, NOP58 knockdown led to increased BCL2 expression and decreased Ki67 levels, promoting apoptosis and inhibiting cell proliferation. Colony formation assays further showed that NOP58 knockdown inhibited, while its overexpression promoted, colony formation, highlighting the critical role of NOP58 in prostate cancer cell growth and survival. Additionally, NOP58 was linked to drug responses, including Methotrexate, Rapamycin, Sorafenib, and Vorinostat.

Conclusion

NOP58 is a key regulator of prostate cancer progression through its mediation of the SUMOylation pathway. Its expression level serves as a reliable prognostic biomarker and an actionable therapeutic target, advancing precision medicine for prostate cancer. Targeting NOP58 may enhance therapeutic efficacy and improve outcomes in oncology.

CD34 as a potential prognostic indicator for camrelizumab response in advanced non-small-cell lung cancer: insights from digital spatial profiling

Background

Given that only a small subset of patients with advanced non-small-cell lung cancer (aNSCLC) benefit from immune checkpoint inhibitors (ICIs), the effectiveness of ICIs is often compromised by the complex interplay within the tumor microenvironment (TME).

Objectives

To identify predictive biomarkers associated with ICI resistance at a multi-omics spatial level.

Design

A total of eight aNSCLC patients who received first-line anti-programmed cell death protein-1 (PD-1) monoclonal antibody camrelizumab at Shandong Cancer Hospital and Institute between 2021 and 2022 were included in the discovery cohort. An additional validation cohort of 45 samples from camrelizumab-treated aNSCLC patients was also enrolled.

Methods

NanoString GeoMx® digital spatial profiling was conducted at the transcriptomic and proteomic level within pan-cytokeratin (panCK+), CD45+, and CD68+ compartments. For validation, multiplex immunofluorescence (mIF) staining was performed.

Results

Distinct spatial expression patterns and levels of immune infiltration were observed between tumor and leukocyte compartments. Higher CD34 expression in the macrophage compartment correlated with poorer prognosis and response to camrelizumab (p < 0.05). mIF validation confirmed the association of elevated CD34 expression level with reduced progression-free survival (PFS; hazard ratio (HR) = 5.011, 95% confidence interval: 1.057-23.752, p = 0.042), outperforming traditional tumor markers in predictive accuracy.

Conclusion

Our findings identify CD34 as a novel spatial biomarker for anti-PD-1 therapy efficacy, potentially guiding the selection of aNSCLC patients who are more likely to benefit from ICI treatment.

Trial registration

ChiCTR2000040416.

Applications of single-cell analysis in immunotherapy for lung cancer: Current progress, new challenges and expectations

BACKGROUND

Lung cancer is a prevalent form of cancer worldwide, presenting a substantial risk to human well-being. Lung cancer is classified into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The advancement of tumor immunotherapy, specifically immune checkpoint inhibitors and adaptive T-cell therapy, has encountered substantial obstacles due to the rapid progression of SCLC and the metastasis, recurrence, and drug resistance of NSCLC. These challenges are believed to stem from the tumor heterogeneity of lung cancer within the tumor microenvironment.

AIM OF REVIEW

This review aims to comprehensively explore recent strides in single-cell analysis, a robust sequencing technology, concerning its application in the realm of tumor immunotherapy for lung cancer. It has been effectively integrated with transcriptomics, epigenomics, genomics, and proteomics for various applications. Specifically, these techniques have proven valuable in mapping the transcriptional activity of tumor-infiltrating lymphocytes in patients with NSCLC, identifying circulating tumor cells, and elucidating the heterogeneity of the tumor microenvironment.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review emphasizes the paramount significance of single-cell analysis in mapping the immune cells within NSCLC patients, unveiling circulating tumor cells, and elucidating the tumor microenvironment heterogeneity. Notably, these advancements highlight the potential of single-cell analysis to revolutionize lung cancer immunotherapy by characterizing immune cell fates, improving therapeutic strategies, and identifying promising targets or prognostic biomarkers. It is potential to unravel the complexities within the tumor microenvironment and enhance treatment strategies marks a significant step towards more effective therapies and improved patient outcomes.

A tumor cornification and immune-infiltration-based scheme for anti-PD-1 plus chemotherapy response in advanced squamous cell lung carcinoma

BACKGROUND

Anti-PD-1 immunotherapy plus chemotherapy (combo) exhibits significantly prolonged survival for squamous cell lung cancer (LUSC). An exploration of predictive biomarkers is still needed.

METHODS

High-throughput RNA sequencing (RNA-seq) of 349 LUSC samples from the randomized, multi-center, phase 3 trial ORIENT-12 (ClinicalTrials.gov: NCT03629925) was conducted for biomarker discovery, followed by flow cytometry and multiplex immunohistochemistry (mIHC) in additional clinical cohorts, and in vitro experiments were performed for verification.

RESULTS

A high abundance of activated CD8+ T and CD56bright natural killer (NK) cells benefited patients' outcomes (progression-free survival [PFS]; overall survival [OS]) with combo treatment. Tumor cornification level remarkably affected the infiltration of the two crucial immune cells. Thus, a novel scheme of LUSC immune infiltration and cornification characterization-based classification (LICC) was established for combo efficacy prediction. Patients who received combo treatment achieved significant PFS improvements in LICC1 (hazard ratio [HR] = 0.43, 95% confidence interval [CI]: 0.25-0.75, p = 0.0029) and LICC2 (HR = 0.32, 95% CI: 0.17-0.58, p = 0.0002) subtypes but not in the LICC3 subtype (HR = 0.86, 95% CI: 0.60-1.23, p = 0.4053). Via single-cell RNA-seq analysis, the tumor cornification signal was mainly mapped to SPRR3+ tumor cells, whose relationships with activated CD8+ T or CD56bright NK cells were verified using flow cytometry and mIHC. Our data suggest that SPRR3+ tumor cells might evade immune surveillance via the CD24-SIGLEC10 (M2 macrophage) axis to maintain a suppressive tumor microenvironment.

CONCLUSIONS

Tumor cornification greatly impacts immune infiltration, and the LICC scheme may guide clinical medication of anti-PD-1+chemo treatment in patients with LUSC.

FUNDING

The study was funded by the National Key R&D Program of China, the National Natural Science Foundation of China, Shanghia Multidisplinary Cooperation Building Project for Diagnosis and Treatment of Major Disease, and Innovent Biologics, Inc.

Spatial analyses revealed S100P + TFF1 + tumor cells in spread through air spaces samples correlated with undesirable therapy response in non-small cell lung cancer

Spread through air spaces (STAS) is a recognized aggressive pattern in lung cancer, serving as a crucial risk factor for postoperative recurrence. However, its phenotype and related spatial structure have remained elusive. To address these limitations, we conducted a comprehensive study based on spatial data, analyzing over 30,000 spots from 14 non-STAS samples and one STAS sample. We observed increased proliferation activities and angiogenesis in STAS, identifying S100P as a potential biomarker for STAS. Furthermore, our investigation into the heterogeneity of STAS tumor cells revealed a subset identified as S100P + TFF1 +, exhibiting a negative impact on patients' survival in public datasets. This subtype exhibited the highest activities in the TGFb and hypoxia, suggesting its potential pro-tumor role within the tumor microenvironment. To assess the role of S100P + TFF1 + tumor cells in therapy response, we included data from two clinical trial cohorts (BPI-7711 for EGFR-TKI therapy and ORIENT-3 for immunotherapy). The presence of S100P + TFF1 + tumor cells correlated with worse responses to both EGFR-TKI therapy and immunotherapy. Notably, TFF1 emerged as a serum marker for predicting EGFR-TKI response. Cell-cell communication analysis revealed that the TGFb signaling pathway was the most activated in S100P + TFF1 + tumor cells, with TGFB2-TGFBR2 identified as the main ligand-receptor pair. This was further validated by multiplex immunofluorescence performed on twenty NSCLC samples. In summary, our study identified S100P as the biomarker for STAS and highlighted the adverse role of S100P + TFF1 + tumor cells in survival outcomes.

Assessing the metastatic potential of circulating tumor cells using an organ-on-chip model

Metastatic lung cancer remains a leading cause of death worldwide, with its intricate metastatic cascade posing significant challenges to researchers and clinicians. Despite substantial progress in understanding this cascade, many aspects remain elusive. Microfluidic-based vasculature-on-chip models have emerged as powerful tools in cancer research, enabling the simulation of specific stages of tumor progression. In this study, we investigate the extravasation behaviors of A549 lung cancer cell subpopulations, revealing distinct differences based on their phenotypes. Our results show that holoclones, which exhibit an epithelial phenotype, do not undergo extravasation. In contrast, paraclones, characterized by a mesenchymal phenotype, demonstrate a notable capacity for extravasation. Furthermore, we observed that paraclones migrate significantly faster than holoclones within the microfluidic model. Importantly, we found that the depletion of vascular endothelial growth factor (VEGF) effectively inhibits the extravasation of paraclones. These findings highlight the utility of microfluidic-based models in replicating key aspects of the metastatic cascade. The insights gained from this study underscore the potential of these models to advance precision medicine by facilitating the assessment of patient-specific cancer cell dynamics and drug responses. This approach could lead to improved strategies for predicting metastatic risk and tailoring personalized cancer therapies, potentially involving the sampling of cancer cells from patients during tumor resection or biopsies.

Intratumoral and peritumoral radiomics model for the preoperative prediction of cribriform component in invasive lung adenocarcinoma: a multicenter study

PURPOSE

This study aimed to investigate the predictive value of intratumoral and peritumoral radiomics model for the cribriform component (CC) of invasive lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

The 144 patients with invasive LUAD from our center were randomly divided into training set (n = 100) and internal validation set (n = 44) in a ratio of 7:3, and 75 patients from center 2 were regarded as the external validation set. Clinical risk factors were examined using univariate and multivariate logistic regression to construct the clinical model. We extracted radiomics features from gross tumor volume (GTV), gross and peritumoral volume (GPTV), and peritumoral volume (PTV), respectively. Radiomics models were constructed with selected features. A combined model based on the optimal Radscore and clinically independent predictors was constructed, and its predictive performance was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

The area under curves (AUCs) of the GTV model were 0.882 (95% CI 0.817-0.948), 0.794 (95% CI 0.656-0.932), and 0.766 (95% CI 0.657-0.875) in the training, internal validation, and external validation sets, and the PTV model had AUCs of 0.812 (95% CI 0.725-0.899), 0.749 (95% CI 0.597-0.902), and 0.670 (95% CI 0.543-0.798) in the training, internal validation, and external validation sets, respectively. However, the GPTV radiomics model showed better predictive performance compared with the GTV and PTV radiomics models, with the AUCs of 0.950 (95% CI 0.911-0.989), 0.844 (95% CI 0.728-0.959), and 0.815 (95% CI 0.713-0.917) in the training, internal validation and external validation sets, respectively. In the clinical model, tumor shape, lobulation sign and maximal diameter were the independent predictors of CC in invasive LUAD. The combined model including independent clinical predictors and GPTV-Radscore show the considerable instructive to clinical practice, with the AUCs of 0.954(95% CI 0.918-0.990), 0.861(95% CI 0.752-0.970), and 0.794(95% CI 0.690-0.898) in training, internal validation, and external validation sets, respectively. DCA showed that the combined model had good clinical value and correction effect.

CONCLUSION

Radiomics model is a very powerful tool for predicting CC growth pattern in invasive LUAD and can help clinicians make the strategies of treatment and surveillance in patients with invasive LUAD.

Unraveling the ecological landscape of mast cells in esophageal cancer through single-cell RNA sequencing

Background

Esophageal cancer (EC) is a major health issue, ranking seventh in incidence and sixth in mortality worldwide. Despite advancements in multidisciplinary treatment approaches, the 5-year survival rate for EC remains low at 21%. Challenges in EC treatment arise from late-stage diagnosis, high malignancy, and poor prognosis. Understanding the tumor microenvironment is critical, as it includes various cellular and extracellular components that influence tumor behavior and treatment response. Mast cells (MCs), as tissue-resident immune cells, play dual roles in tumor dynamics. High-throughput single-cell RNA sequencing offers a powerful tool for analyzing tumor heterogeneity and immune interactions, although its application in EC is limited.

Methods

In this study, we investigated the immune microenvironment of EC using single-cell RNA sequencing and established a comprehensive immune profile. We also performed analysis of upstream transcription factors and downstream pathway enrichment to further comprehensively decipher MCs in EC. Besides, we performed knockdown experiments to explore the role of epidermal growth factor receptor (EGFR) signaling pathway in MCs-tumor cell interactions, highlighting its potential as a prognostic marker. Finally, we constructed a prognostic model for EC, which provided valuable suggestions for the diagnosis and prognosis of EC.

Results

Our analysis identified 11 major cell types, of which MCs were particularly present in pericarcinoma tissues. Further grouping of the 5,001 MCs identified 8 distinct subtypes, including SRSF7-highly expressed MCs, which showed strong tumor preference and potential tumor-promoting properties. Moreover, we identified the key signaling receptor EGFR and validated it by in vitro knockdown experiments, demonstrating its cancer-promoting effects. In addition, we established an independent prognostic indicator, SRSF7+ MCs risk score (SMRS), which showed a correlation between high SMRS group and poor prognosis.

Conclusion

These findings illuminate the complex interactions within the tumor microenvironment of EC and suggest that targeting specific MCs subtypes, particularly via the EGFR signaling pathway, may present novel therapeutic strategies. This study establishes a comprehensive immune map of EC, offering insights for improved treatment approaches.

Beyond the Gut: The intratumoral microbiome's influence on tumorigenesis and treatment response

The intratumoral microbiome (TM) refers to the microorganisms in the tumor tissues, including bacteria, fungi, viruses, and so on, and is distinct from the gut microbiome and circulating microbiota. TM is strongly associated with tumorigenesis, progression, metastasis, and response to therapy. This paper highlights the current status of TM. Tract sources, adjacent normal tissue, circulatory system, and concomitant tumor co-metastasis are the main origin of TM. The advanced techniques in TM analysis are comprehensively summarized. Besides, TM is involved in tumor progression through several mechanisms, including DNA damage, activation of oncogenic signaling pathways (phosphoinositide 3-kinase [PI3K], signal transducer and activator of transcription [STAT], WNT/β-catenin, and extracellular regulated protein kinases [ERK]), influence of cytokines and induce inflammatory responses, and interaction with the tumor microenvironment (anti-tumor immunity, pro-tumor immunity, and microbial-derived metabolites). Moreover, promising directions of TM in tumor therapy include immunotherapy, chemotherapy, radiotherapy, the application of probiotics/prebiotics/synbiotics, fecal microbiome transplantation, engineered microbiota, phage therapy, and oncolytic virus therapy. The inherent challenges of clinical application are also summarized. This review provides a comprehensive landscape for analyzing TM, especially the TM-related mechanisms and TM-based treatment in cancer.

HCC spatial transcriptomic profiling reveals significant and potentially targetable cancer-endothelial interactions

BACKGROUND

HCC is a highly vascular tumor, and many effective drug regimens target the tumor blood vessels. Prior bulk HCC subtyping data used bulk transcriptomes, which contained a mixture of parenchymal and stromal contributions.

METHODS

We utilized computational deconvolution and cell-cell interaction analyses to cell type-specific (tumor-enriched and vessel-enriched) spatial transcriptomic data collected from 41 resected HCC tissue specimens.

RESULTS

We report that the prior Hoshida bulk transcriptional subtyping schema is driven largely by an endothelial fraction, show an alternative tumor-specific schema has potential prognostic value, and use spatially paired ligand-receptor analyses to identify known and novel (LGALS9 tumor-HAVCR2 vessel) signaling relationships that drive HCC biology in a subtype-specific and potentially targetable manner.

CONCLUSIONS

Our study leverages spatial gene expression profiling technologies to dissect HCC heterogeneity and identify heterogeneous signaling relationships between cancer cells and their endothelial cells. Future validation and expansion of these findings may validate novel cancer-endothelial cell interactions and related drug targets.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals immune suppression subtypes and establishes a novel signature for determining the prognosis in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with lower survival rates. Recent advancements in targeted therapies and immunotherapies targeting immune checkpoints have achieved remarkable success, there is still a large percentage of LUAD that lacks available therapeutic options. Due to tumor heterogeneity, the diagnosis and treatment of LUAD are challenging. Exploring the biology of LUAD and identifying new biomarker and therapeutic targets options are essential.

METHOD

We performed single-cell RNA sequencing (scRNA-seq) of 6 paired primary and adjacent LUAD tissues, and integrative omics analysis of the scRNA-seq, bulk RNA-seq and whole-exome sequencing data revealed molecular subtype characteristics. Our experimental results confirm that CDC25C gene can serve as a potential marker for poor prognosis in LUAD.

RESULTS

We investigated aberrant gene expression in diverse cell types in LUAD via the scRNA-seq data. Moreover, multi-omics clustering revealed four subgroups defined by transcriptional profile and molecular subtype 4 (MS4) with poor survival probability, and immune cell infiltration signatures revealed that MS4 tended to be the immunosuppressive subtype. Our study revealed that the CDC25C gene can be a distinct prognostic biomarker that indicates immune infiltration levels and response to immunotherapy in LUAD patients. Our experimental results concluded that CDC25C expression affects lung cancer cell invasion and migration, might play a key role in regulating Epithelial-Mesenchymal Transition (EMT) pathways.

CONCLUSIONS

Our multi-omics result revealed a comprehensive set of molecular attributes associated with prognosis-related genes in LUAD at the cellular and tissue level. Identification of a subtype of immunosuppressive TME and prognostic signature for LUAD. We identified the cell cycle regulation gene CDC25C affects lung cancer cell invasion and migration, which can be used as a potential biomarker for LUAD.

Defining heritability, plasticity, and transition dynamics of cellular phenotypes in somatic evolution

Single-cell sequencing has characterized cell state heterogeneity across diverse healthy and malignant tissues. However, the plasticity or heritability of these cell states remains largely unknown. To address this, we introduce PATH (phylogenetic analysis of trait heritability), a framework to quantify cell state heritability versus plasticity and infer cell state transition and proliferation dynamics from single-cell lineage tracing data. Applying PATH to a mouse model of pancreatic cancer, we observed heritability at the ends of the epithelial-to-mesenchymal transition spectrum, with higher plasticity at more intermediate states. In primary glioblastoma, we identified bidirectional transitions between stem- and mesenchymal-like cells, which use the astrocyte-like state as an intermediary. Finally, we reconstructed a phylogeny from single-cell whole-genome sequencing in B cell acute lymphoblastic leukemia and delineated the heritability of B cell differentiation states linked with genetic drivers. Altogether, PATH replaces qualitative conceptions of plasticity with quantitative measures, offering a framework to study somatic evolution.

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.

Cross-omics strategies and personalised options for lung cancer immunotherapy

Lung cancer is one of the most common malignant tumours worldwide and its high mortality rate makes it a leading cause of cancer-related deaths. To address this daunting challenge, we need a comprehensive understanding of the pathogenesis and progression of lung cancer in order to adopt more effective therapeutic strategies. In this regard, integrating multi-omics data of the lung provides a highly promising avenue. Multi-omics approaches such as genomics, transcriptomics, proteomics, and metabolomics have become key tools in the study of lung cancer. The application of these methods not only helps to resolve the immunotherapeutic mechanisms of lung cancer, but also provides a theoretical basis for the development of personalised treatment plans. By integrating multi-omics, we have gained a more comprehensive understanding of the process of lung cancer development and progression, and discovered potential immunotherapy targets. This review summarises the studies on multi-omics and immunology in lung cancer, and explores the application of these studies in early diagnosis, treatment selection and prognostic assessment of lung cancer, with the aim of providing more personalised and effective treatment options for lung cancer patients.

3D cell culture models in research: applications to lung cancer pharmacology

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, necessitating innovative research methodologies to improve treatment outcomes and develop novel strategies. The advent of three-dimensional (3D) cell cultures has marked a significant advancement in lung cancer research, offering a more physiologically relevant model compared to traditional two-dimensional (2D) cultures. This review elucidates the various types of 3D cell culture models currently used in lung cancer pharmacology, including spheroids, organoids and engineered tissue models, having pivotal roles in enhancing our understanding of lung cancer biology, facilitating drug development, and advancing precision medicine. 3D cell culture systems mimic the complex spatial architecture and microenvironment of lung tumours, providing critical insights into the cellular and molecular mechanisms of tumour progression, metastasis and drug responses. Spheroids, derived from commercialized cell lines, effectively model the tumour microenvironment (TME), including the formation of hypoxic and nutrient gradients, crucial for evaluating the penetration and efficacy of anti-cancer therapeutics. Organoids and tumouroids, derived from primary tissues, recapitulate the heterogeneity of lung cancers and are instrumental in personalized medicine approaches, supporting the simulation of in vivo pharmacological responses in a patient-specific context. Moreover, these models have been co-cultured with various cell types and biomimicry extracellular matrix (ECM) components to further recapitulate the heterotypic cell-cell and cell-ECM interactions present within the lung TME. 3D cultures have been significantly contributing to the identification of novel therapeutic targets and the understanding of resistance mechanisms against conventional therapies. Therefore, this review summarizes the latest findings in drug research involving lung cancer 3D models, together with the common laboratory-based assays used to study drug effects. Additionally, the integration of 3D cell cultures into lung cancer drug development workflows and precision medicine is discussed. This integration is pivotal in accelerating the translation of laboratory findings into clinical applications, thereby advancing the landscape of lung cancer treatment. By closely mirroring human lung tumours, these models not only enhance our understanding of the disease but also pave the way for the development of more effective and personalized therapeutic strategies.

A Study of Disease Prognosis in Lung Adenocarcinoma Using Single-Cell Decomposition and Immune Signature Analysis

Background: The development of tumors is a highly complex process that entails numerous interactions and intricate relationships between the host immune system and cancer cells. It has been demonstrated in studies that the treatment response of patients can be correlated with the tumor microenvironment (TME). Consequently, the examination of diverse immune profiles within the TME can facilitate the elucidation of tumor development and the development of advantageous models for diagnoses and prognoses. Methods: In this study, we utilized a single-cell decomposition method to analyze the relationships between cell proportions and immune signatures in lung adenocarcinoma (LUAD) patients. Results: Our findings indicate that specific immune cell populations and immune signatures are significantly associated with patient prognosis. By identifying poor prognosis signatures (PPS), we reveal the critical role of immune profiles and cellular composition in disease outcomes, emphasizing their diagnostic potential for predicting patient prognosis. Conclusions: This study highlights the importance of immune signatures and cellular composition, which may serve as valuable biomarkers for disease prognosis in LUAD patients.

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.

Recapitulating the Cancer-Immunity Cycle on a Chip

The cancer-immunity cycle is a fundamental framework for understanding how the immune system interacts with cancer cells, balancing T cell recognition and elimination of tumors while avoiding autoimmune reactions. Despite advancements in immunotherapy, there remains a critical need to dissect each phase of the cycle, particularly the interactions among the tumor, vasculature, and immune system within the tumor microenvironment (TME). Innovative platforms such as organ-on-a-chip, organoids, and bioprinting within microphysiological systems (MPS) are increasingly utilized to enhance the understanding of these interactions. These systems meticulously replicate crucial aspects of the TME and immune responses, providing robust platforms to study cancer progression, immune evasion, and therapeutic interventions with greater physiological relevance. This review explores the latest advancements in MPS technologies for modeling various stages of the cancer-immune cycle, critically evaluating their applications and limitations in advancing the understanding of cancer-immune dynamics and guiding the development of next-generation immunotherapeutic strategies.

Investigation of tumour environments through advancements in microtechnology and nanotechnology

Cancer has a significant negative social and economic impact on both developed and developing countries. As a result, understanding the onset and progression of cancer is critical for developing therapies that can improve the well-being and health of individuals with cancer. With time, study has revealed, the tumor microenvironment has great influence on this process. Micro and nanoscale engineering techniques can be used to study the tumor microenvironment. Nanoscale and Microscale engineering use Novel technologies and designs with small dimensions to recreate the TME. Knowing how cancer cells interact with one another can help researchers develop therapeutic approaches that anticipate and counteract cancer cells' techniques for evading detection and fighting anti-cancer treatments, such as microfabrication techniques, microfluidic devices, nanosensors, and nanodevices used to study or recreate the tumor microenvironment. Nevertheless, a complicated action just like the growth and in cancer advancement, and their intensive association along the environment around it that has to be studied in more detail.

Bioactive sphingolipids as emerging targets for signal transduction in cancer development

Sphingolipids, crucial components of cellular membranes, play a vital role in maintaining cellular structure and signaling integrity. Disruptions in sphingolipid metabolism are increasingly implicated in cancer development. Key bioactive sphingolipids, such as ceramides, sphingosine-1-phosphate (S1P), ceramide-1-phosphate (C1P), and glycosphingolipids, profoundly impact tumor biology. They influence the behavior of tumor cells, stromal cells, and immune cells, affecting tumor aggressiveness, angiogenesis, immune modulation, and extracellular matrix remodeling. Furthermore, abnormal expression of sphingolipids and their metabolizing enzymes modulates the secretion of tumor-derived extracellular vesicles (TDEs), which are key players in creating an immunosuppressive tumor microenvironment, remodeling the extracellular matrix, and facilitating oncogenic signaling within in situ tumors and distant pre-metastatic niches (PMNs). Understanding the role of sphingolipids in the biogenesis of tumor-derived extracellular vesicles (TDEs) and their bioactive contents can pave the way for new biomarkers in cancer diagnosis and prognosis, ultimately enhancing comprehensive tumor treatment strategies.

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.

Tumor immune microenvironment of NSCLC with EGFR exon 20 insertions may predict efficacy of first-line ICI-combined regimen

OBJECTIVES

Non-small cell lung cancer (NSCLC) patients with exon 20 insertion mutations (ex20ins) of the epidermal growth factor receptor (EGFR) were resistant to monotherapy of immune checkpoint inhibitor (ICI). However, recent reports have shown that the combination of ICI and chemotherapy (ICI-combined regimen) exhibited certain efficacy for NSCLC with EGFR ex20ins. The mechanisms behind this phenomenon have not been thoroughly clarified. Hence, we conducted this study tofind correlations between the tumor immune microenvironment of EGFR ex20ins and the efficacy of ICI-combined regimen.

METHODS

We performed single-cell transcriptome sequencing and multiplex immunofluorescence staining (mIF) to investigate the immune microenvironment of NSCLC patients with EGFR ex20ins, L858R, and EGFR wild-type. We analyzed 15 treatment-naïve NSCLC samples utilizing single-cell RNA sequencing (scRNA-seq). Another 30 cases of EGFR L858R and 4 cases of wild-type were recruited to compare the immune microenvironment with that of EGFR ex20ins (28 cases) by mIF.

RESULTS

We observed that cell components, function and interactions varied between EGFR ex20ins, L858R, and wild-type NSCLC.We discovered similar T cell and CD8+ T cell distributions among groups but found noninferior or even better T cell activation in ex20ins patients. Infiltrating CD8+ FOXP3- T cells were significantly lower in the tumor region of EGFR ex20ins compared to wild-type. T cells from the ex20ins group had a greater tendency to promote cancer cell inflammation and epithelial-mesenchymal transition (EMT) compared to wild-type group. For macrophages, there were more M2-like macrophages in ex20ins patients. M1-like macrophages in ex20ins group produced fewer antitumor cytokines than in other groups.

CONCLUSIONS

The immune microenvironment of EGFR ex20ins is more suppressive than that of L858R and wild-type, suggesting that ICI monotherapy may not be sufficient for these patients. ICI-combined regimen might be a treatment option for EGFR ex20ins due to tumor-promoting inflammation and noninferior T cell functions in the immune microenvironment.

A practical handbook on single-cell RNA sequencing data quality control and downstream analysis

Advancements in single-cell analysis have facilitated high-resolution observation of the transcriptome in individual cells. However, standards for obtaining high-quality cells and data analysis pipelines remain variable. Here, we provide the groundwork for improving the quality of single-cell analysis by delineating guidelines for selecting high-quality cells and considerations throughout the analysis. This review will streamline researchers' access to single-cell analysis and serve as a valuable guide for analysis.

Inhibition of ASAP1 Modulates the Tumor Immune Microenvironment and Suppresses Lung Cancer Metastasis via the p-STAT3 Signaling Pathway

ADP ribosylation factor guanylate kinase 1 (ASAP1), a key protein regulating cell migration and invasion, has attracted extensive attention in oncological research in recent years. This study aims to explore the effects of ASAP1 inhibition on lung cancer metastasis and its potential mechanisms, particularly how it modulates the tumor immune microenvironment through the p-STAT3 signaling pathway. In this study, shRNA technology was employed to specifically inhibit ASAP1 expression in lung cancer cell lines A549, NCI-H1299, and PC-9. The effects of ASAP1 inhibition on lung cancer cell viability, apoptosis, migration, and invasion were evaluated using CCK-8, TUNEL apoptosis detection, and cell migration and invasion assays. Furthermore, animal experiments were conducted to assess the in vivo effects of ASAP1 inhibition on lung cancer metastasis, and immunohistochemical analysis was performed to investigate changes in immune cells in lung metastasis models, further exploring its impact on the tumor immune microenvironment. The experimental results demonstrated that ASAP1 inhibition significantly reduced lung cancer cell viability, induced apoptosis in A549, NCI-H1299, and PC-9 cells, and suppressed the migration and invasion abilities of these cells. In vivo experiments revealed that ASAP1 inhibition effectively suppressed lung cancer metastasis and altered the tumor immune microenvironment by regulating immune cells. Moreover, we found that ASAP1 inhibition could decrease tumor cell proliferation and induce tumor apoptosis in lung metastasis models by inhibiting the p-STAT3 signaling pathway. This study confirms that ASAP1 inhibition can suppress lung cancer metastasis by modulating the tumor immune microenvironment through the inhibition of the p-STAT3 signaling pathway. These findings provide new targets for lung cancer treatment and a theoretical basis for developing novel strategies against lung cancer metastasis. Future research will further explore the mechanisms of ASAP1 in lung cancer metastasis and how to optimize treatment strategies for lung cancer patients by targeting ASAP1.

Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement

Rationale: Despite significant advances in precision treatments and immunotherapy, lung cancer is the most common cause of cancer death worldwide. To reduce incidence and improve survival rates, a deeper understanding of lung premalignancy and the multistep process of tumorigenesis is essential, allowing timely and effective intervention before cancer development. Objectives: To summarize existing information, identify knowledge gaps, formulate research questions, prioritize potential research topics, and propose strategies for future investigations into the premalignant progression in the lung. Methods: An international multidisciplinary team of basic, translational, and clinical scientists reviewed available data to develop and refine research questions pertaining to the transformation of premalignant lung lesions to advanced lung cancer. Results: This research statement identifies significant gaps in knowledge and proposes potential research questions aimed at expanding our understanding of the mechanisms underlying the progression of premalignant lung lesions to lung cancer in an effort to explore potential innovative modalities to intercept lung cancer at its nascent stages. Conclusions: The identified gaps in knowledge about the biological mechanisms of premalignant progression in the lung, together with ongoing challenges in screening, detection, and early intervention, highlight the critical need to prioritize research in this domain. Such focused investigations are essential to devise effective preventive strategies that may ultimately decrease lung cancer incidence and improve patient outcomes.

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.

Single-cell transcriptomics reveals tumor microenvironment remodeling in hepatocellular carcinoma with varying tumor subclonal complexity

Introduction

The complexity of tumor cell subclonal structure has been extensively investigated in hepatocellular carcinoma. However, the role of subclonal complexity in reshaping the tumor microenvironment (TME) remains poorly understood.

Methods

We integrated single-cell transcriptome sequencing data from four independent HCC cohorts, involving 30 samples, to decode the associations between tumor subclonal complexity and the TME. We proposed a robust metric to accurately quantify the degree of subclonal complexity for each sample based on discrete copy number variations (CNVs) profiles.

Results

We found that tumor cells in the high-complexity group originated from the cell lineage with FGB overexpression and exhibited high levels of transcription factors associated with poor survival. In contrast, tumor cells in low-complexity patients showed activation of more hallmark signaling pathways, more active cell-cell communications within the TME and a higher immune activation status. Additionally, cytokines signaling activity analysis suggested a link between HMGB1 expressed by a specific endothelial subtype and T cell proliferation.

Discussion

Our study sheds light on the intricate relationship between the complexity of subclonal structure and the TME, offering novel insights into potential therapeutic targets for HCC.

RAC1 inhibition ameliorates IBSP-induced bone metastasis in lung adenocarcinoma

Macrophage-to-osteoclast differentiation (osteoclastogenesis) plays an essential role in tumor osteolytic bone metastasis (BM), while its specific mechanisms remain largely uncertain in lung adenocarcinoma BM. In this study, we demonstrate that integrin-binding sialoprotein (IBSP), which is highly expressed in the cancer cells from bone metastatic and primary lesions of patients with lung adenocarcinoma, can facilitate BM and directly promote macrophage-to-osteoclast differentiation independent of RANKL/M-CSF. In vivo results further suggest that osteolytic BM in lung cancer specifically relies on IBSP-induced macrophage-to-osteoclast differentiation. Mechanistically, IBSP regulates the Rac family small GTPase 1 (Rac1)-NFAT signaling pathway and mediates the forward shift of macrophage-to-osteoclast differentiation, thereby leading to early osteolysis. Moreover, inhibition of Rac1 by EHT-1864 or azathioprine in mice models can remarkably alleviate IBSP-induced BM of lung cancer. Overall, our study suggests that tumor-secreted IBSP promotes BM by inducing macrophage-to-osteoclast differentiation, with potential as an early diagnostic maker for BM, and Rac1 can be the therapeutic target for IBSP-promoted BM in lung cancer.

Elucidating the role of tumor-associated ALOX5+ mast cells with transformative function in cervical cancer progression via single-cell RNA sequencing

Background

Cervical cancer (CC) is the fourth most common malignancy among women globally and serves as the main cause of cancer-related deaths among women in developing countries. The early symptoms of CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there is a close relationship between mast cells (MCs) and tumor development. However, research on the role MCs played in CC is still very limited at that time. Thus, the study conducted a single-cell multi-omics analysis on human CC cells, aiming to explore the mechanisms by which MCs interact with the tumor microenvironment in CC. The goal was to provide a scientific basis for the prevention, diagnosis, and treatment of CC, with the hope of improving patients' prognoses and quality of life.

Method

The present study acquired single-cell RNA sequencing data from ten CC tumor samples in the ArrayExpress database. Slingshot and AUCcell were utilized to infer and assess the differentiation trajectory and cell plasticity of MCs subpopulations. Differential expression analysis of MCs subpopulations in CC was performed, employing Gene Ontology, gene set enrichment analysis, and gene set variation analysis. CellChat software package was applied to predict cell communication between MCs subpopulations and CC cells. Cellular functional experiments validated the functionality of TNFRSF12A in HeLa and Caski cell lines. Additionally, a risk scoring model was constructed to evaluate the differences in clinical features, prognosis, immune infiltration, immune checkpoint, and functional enrichment across various risk scores. Copy number variation levels were computed using inference of copy number variations.

Result

The obtained 93,524 high-quality cells were classified into ten cell types, including T_NK cells, endothelial cells, fibroblasts, smooth muscle cells, epithelial cells, B cells, plasma cells, MCs, neutrophils, and myeloid cells. Furthermore, a total of 1,392 MCs were subdivided into seven subpopulations: C0 CTSG+ MCs, C1 CALR+ MCs, C2 ALOX5+ MCs, C3 ANXA2+ MCs, C4 MGP+ MCs, C5 IL32+ MCs, and C6 ADGRL4+ MCs. Notably, the C2 subpopulation showed close associations with tumor-related MCs, with Slingshot results indicating that C2 subpopulation resided at the intermediate-to-late stage of differentiation, potentially representing a crucial transition point in the benign-to-malignant transformation of CC. CNVscore and bulk analysis results further confirmed the transforming state of the C2 subpopulation. CellChat analysis revealed TNFRSF12A as a key receptor involved in the actions of C2 ALOX5+ MCs. Moreover, in vitro experiments indicated that downregulating the TNFRSF12A gene may partially inhibit the development of CC. Additionally, a prognosis model and immune infiltration analysis based on the marker genes of the C2 subpopulation provided valuable guidance for patient prognosis and clinical intervention strategies.

Conclusions

We first identified the transformative tumor-associated MCs subpopulation C2 ALOX5+ MCs within CC, which was at a critical stage of tumor differentiation and impacted the progression of CC. In vitro experiments confirmed the inhibitory effect of knocking down the TNFRSF12A gene on the development of CC. The prognostic model constructed based on the C2 ALOX5+MCs subset demonstrated excellent predictive value. These findings offer a fresh perspective for clinical decision-making in CC.

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.

Designing SERS nanotags for profiling overexpressed surface markers on single cancer cells: A review

This review focuses on the chemical design and the use of Surface-Enhanced Raman Scattering (SERS)-active nanotags for measuring surface markers that can be overexpressed at the surface of single cancer cells. Indeed, providing analytical tools with true single-cell measurements capabilities is capital, especially since cancer research is increasingly leaning toward single-cell analysis, either to guide treatment decisions or to understand complex tumor behaviour including the single-cell heterogeneity and the appearance of treatment resistance. Over the past two decades, SERS nanotags have triggered significant interest in the scientific community owing their advantages over fluorescent tags, mainly because SERS nanotags resist photobleaching and exhibit sharper signal bands, which reduces possible spectral overlap and enables the discrimination between the SERS signals and the autofluorescence background from the sample itself. The extensive efforts invested in harnessing SERS nanotags for biomedical purposes, particularly in cancer research, highlight their potential as the next generation of optical labels for single-cell studies. The review unfolds in two main parts. The first part focuses on the structure of SERS nanotags, detailing their chemical composition and the role of each building block of the tags. The second part explores applications in measuring overexpressed surface markers on single-cells. The latter encompasses studies using single nanotags, multiplexed measurements, quantitative information extraction, monitoring treatment responses, and integrating phenotype measurements with SERS nanotags on single cells isolated from complex biological matrices. This comprehensive review anticipates SERS nanotags to persist as a pivotal technology in advancing single-cell analytical methods, particularly in the context of cancer research and personalized medicine.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals MMP mediated expression patterns by distinct tumor microenvironment immune profiles in cervical cancer

Background: Few studies have analyzed the effect of matrix metalloproteinase (MMP) expression patterns on the tumor microenvironment (TME) during development of cervical cancer (CC). Methods: We elucidated the landscape and score of MMP expression in CC using single-cell RNA sequencing (scRNA-seq) and RNA sequencing datasets. Further, we aimed the MMPscore to probe the infiltration of immune cells. Further, MMP expression was measured by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Results: We found MMPs were cell-type specific expressed in diverse types of CC cells, regulating the relative pathways of CC progression. Two distinct MMP expression patterns that associated infiltrated tumor microenvironment (TME) were identified. We discovered MMP expression patterns can predict the stage of tumor, subtype, stromal activity in the TME, genetic variation, and patient outcome. Patients with high MMPscore benefited from significantly better treatment and clinical outcomes. Conclusion: These results indicate high MMPscore in diverse cell types may regulate immune response and improve the survival of patients with CC, which assist in developing more effective immunization strategies.

Integrative single-cell RNA-seq and spatial transcriptomics analyses reveal diverse apoptosis-related gene expression profiles in EGFR-mutated lung cancer

In EGFR-mutated lung cancer, the duration of response to tyrosine kinase inhibitors (TKIs) is limited by the development of acquired drug resistance. Despite the crucial role played by apoptosis-related genes in tumor cell survival, how their expression changes as resistance to EGFR-TKIs emerges remains unclear. Here, we conduct a comprehensive analysis of apoptosis-related genes, including BCL-2 and IAP family members, using single-cell RNA sequence (scRNA-seq) and spatial transcriptomics (ST). scRNA-seq of EGFR-mutated lung cancer cell lines captures changes in apoptosis-related gene expression following EGFR-TKI treatment, most notably BCL2L1 upregulation. scRNA-seq of EGFR-mutated lung cancer patient samples also reveals high BCL2L1 expression, specifically in tumor cells, while MCL1 expression is lower in tumors compared to non-tumor cells. ST analysis of specimens from transgenic mice with EGFR-driven lung cancer indicates spatial heterogeneity of tumors and corroborates scRNA-seq findings. Genetic ablation and pharmacological inhibition of BCL2L1/BCL-XL overcome or delay EGFR-TKI resistance. Overall, our findings indicate that BCL2L1/BCL-XL expression is important for tumor cell survival as EGFR-TKI resistance emerges.

CD147-K148me2-Driven Tumor Cell-Macrophage Crosstalk Provokes NSCLC Immunosuppression via the CCL5/CCR5 Axis

Immunosuppression is a major hallmark of tumor progression in non-small cell lung cancer (NSCLC). Cluster of differentiation 147 (CD147), an important pro-tumorigenic factor, is closely linked to NSCLC immunosuppression. However, the role of CD147 di-methylation in the immunosuppressive tumor microenvironment (TME) remains unclear. Here, di-methylation of CD147 at Lys148 (CD147-K148me2) is identified as a common post-translational modification (PTM) in NSCLC that is significantly associated with unsatisfying survival outcomes among NSCLC sufferers, especially those in the advanced stages of the disease. The methyltransferase NSD2 catalyzes CD147 to generate CD147-K148me2. Further analysis demonstrates that CD147-K148me2 reestablishes the immunosuppressive TME and promotes NSCLC progression. Mechanistically, this modification promotes the interaction between cyclophilin A (CyPA) and CD147, and in turn, increases CCL5 gene transcription by activating p38-ZBTB32 signaling, leading to increased NSCLC cell-derived CCL5 secretion. Subsequently, CD147-K148me2-mediated CCL5 upregulation facilitates M2-like tumor-associated macrophage (TAM) infiltration in NSCLC tissues via CCL5/CCR5 axis-dependent intercellular crosstalk between tumor cells and macrophages, which is inhibited by blocking CD147-K148me2 with the targeted antibody 12C8. Overall, this study reveals the role of CD147-K148me2-driven intercellular crosstalk in the development of NSCLC immunosuppression, and provides a potential interventional strategy for PTM-targeted NSCLC therapy.

A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

Single-Cell Transcriptomic Analysis Revealed the Cell Population Changes and Cell-Cell Communication in the Liver of a Carnivorous Fish in Response to High-Carbohydrate Diet

BACKGROUND

Carnivorous fish have a low carbohydrate utilization ability, and the physiologic and molecular basis of glucose intolerance has not been fully illustrated.

OBJECTIVES

This study aimed to use largemouth bass as a model to investigate the possible mechanism of glucose intolerance in carnivorous fish with the help of single-nuclei RNA sequencing (snRNA-seq).

METHODS

Two diets were formulated, a low-carbohydrate (LC) diet and a high-carbohydrate (HC) diet. The feeding trial lasted for 6 wk, and then, growth performance, biochemical parameters, liver histology, and snRNA-seq were performed.

RESULTS

Growth performance of fish was not affected by the HC diet, while liver glucolipid metabolism disorder and liver injury were observed. A total of 13,247 and 12,848 cells from the liver derived from 2 groups were isolated and sequenced, and 7 major liver cell types were annotated by the marker genes. Hepatocytes and cholangiocytes were lower and hepatic stellate cells (HSCs) and immune cells were higher in the HC group than those in the LC group. Reclustering analysis identified 7 subtypes of hepatocytes and immune cells, respectively. The HSCs showed more cell communication with other cell types, and periportal hepatocytes showed more cell communication with other hepatocyte subtypes. Cell-cell communication mainly focused on cell junction-related signaling pathways. Uncovered by the pseudotime analysis, midzonal hepatocytes were differentiated into 2 major branches-biliary epithelial hepatocytes and hepatobiliary hybrid progenitor. Cell junction and liver fibrosis-related genes were highly expressed in the HC group. HC diet induced the activation of HSCs and, therefore, led to the liver fibrosis of largemouth bass.

CONCLUSIONS

HC diet induces liver glucolipid metabolism disorder and liver injury of largemouth bass. The increase and activation of HSCs might be the main reason for the liver injury. In adaption to HC diet, midzonal hepatocytes differentiates into 2 major branches-biliary epithelial hepatocytes and hepatobiliary hybrid progenitors.

Single-cell transcriptome analysis deciphers the CD74-mediated immune evasion and tumour growth in lung squamous cell carcinoma with chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) contributes to the incidence and prognosis of lung cancer. The presence of COPD significantly increases the risk of lung squamous cell carcinoma (LSCC). COPD may promote an immunosuppressive microenvironment in LSCC by regulating the expression of immune-inhibitory factors in T cells, although the mechanisms remain unclear. In this study, we aimed to decipher the tumour microenvironment signature for LSCC with COPD at a single-cell level.

METHODS

We performed single-cell RNA sequencing on tumour tissues from LSCC with or without COPD, then investigated the features of the immune and tumour cells. We employed multiple techniques, including multispectral imaging, flow cytometry, tissue microarray analysis, survival analysis, co-culture systems and in vitro and in vivo treatment experiments, to validate the findings obtained from single-cell analyses.

RESULTS

LSCC with COPD showed increased proportions of tumour-associated macrophages (TAMs) and higher levels of CD8+ T cell exhaustion molecules, which contributed to an immunosuppressive microenvironment. Further analysis revealed a critical cluster of CD74+ tumour cells that expressed both epithelial and immune cell signatures, exhibited a stronger capacity for tumorigenesis and predicted worse overall survival. Notably, migration inhibitory factor (MIF) secreted by TAMs from LSCC with COPD may promote the activation of CD74. MIF-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-programmed cell death-1 ligand 1 signalling pathway, facilitating tumour proliferation and immune evasion.

CONCLUSIONS

Our comprehensive picture of the tumour ecosystem in LSCC with COPD provides deeper insights into relevant immune evasion mechanisms and potential targets for immunotherapy.

HIGHLIGHT

Our results demonstrated higher proportions of tumour-associated macrophages (TAMs) and higher levels of exhaustion molecules in CD8+ T cells in the microenvironment of LSCC with COPD. CD74+tumour cells were associated with poor disease prognosis. Migration inhibitory factor (MIF)-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-PD-L1 signalling pathway, facilitating immune evasion.

The crosstalk between lung cancer and the bone marrow niche fuels emergency myelopoiesis

Modest response rates to immunotherapy observed in advanced lung cancer patients underscore the need to identify reliable biomarkers and targets, enhancing both treatment decision-making and efficacy. Factors such as PD-L1 expression, tumor mutation burden, and a 'hot' tumor microenvironment with heightened effector T cell infiltration have consistently been associated with positive responses. In contrast, the predictive role of the abundantly present tumor-infiltrating myeloid cell (TIMs) fraction remains somewhat uncertain, partly explained by their towering variety in terms of ontogeny, phenotype, location, and function. Nevertheless, numerous preclinical and clinical studies established a clear link between lung cancer progression and alterations in intra- and extramedullary hematopoiesis, leading to emergency myelopoiesis at the expense of megakaryocyte/erythroid and lymphoid differentiation. These observations affirm that a continuous crosstalk between solid cancers such as lung cancer and the bone marrow niche (BMN) must take place. However, the BMN, encompassing hematopoietic stem and progenitor cells, differentiated immune and stromal cells, remains inadequately explored in solid cancer patients. Subsequently, no clear consensus has been reached on the exact breadth of tumor installed hematopoiesis perturbing cues nor their predictive power for immunotherapy. As the current era of single-cell omics is reshaping our understanding of the hematopoietic process and the subcluster landscape of lung TIMs, we aim to present an updated overview of the hierarchical differentiation process of TIMs within the BMN of solid cancer bearing subjects. Our comprehensive overview underscores that lung cancer should be regarded as a systemic disease in which the cues governing the lung tumor-BMN crosstalk might bolster the definition of new biomarkers and druggable targets, potentially mitigating the high attrition rate of leading immunotherapies for NSCLC.

Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response.

Methods

By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts.

Results

RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups.

Conclusions

In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.

Values of circulating tumor DNA for non-small cell lung cancer patients receiving neoadjuvant therapy, progress and challenges: a narrative review

Background and Objective

The value of circulating tumor DNA (ctDNA) in neoadjuvant therapy (NAT) for lung cancer remains controversial. Therefore, we conducted a review to further investigate the role of ctDNA in non-small cell lung cancer (NSCLC) patients undergoing NAT for individualized management.

Methods

A search of online databases (PubMed, Embase, Web of Science, Science Direct, and Cochrane Library) was conducted to evaluate the value of ctDNA in predicting relapse, risk stratification, and efficacy of NAT in NSCLC. Only articles published in English within the last 25 years, between January 1st, 1998 and November 30th, 2023, were included. Additionally, the application of ctDNA in NSCLC is briefly reviewed.

Key Content and Findings

ctDNA is a non-invasive and dynamic method that plays an important role in future treatment guidance. Additionally, ctDNA successfully predicted the effect of neoadjuvant immunotherapy before surgery, and positive testing was strongly correlated with a lower major pathological response or complete pathological response rate. Sequential testing of ctDNA may serve as a secondary indicator to guide the adjustment of treatment programs. However, the application of this method has been limited by false negative results, a lack of objective indicators, and high costs. These issues must be addressed by researchers.

Conclusions

ctDNA has strong potential in NAT, based on positive preliminary studies. However, its widespread use is limited by the high cost of testing. Further research is needed to explore its value in risk stratification and treatment guidance in the future.

PPAR-γ/NF-kB/AQP3 axis in M2 macrophage orchestrates lung adenocarcinoma progression by upregulating IL-6

Aquaporin 3 (AQP3), which is mostly expressed in pulmonary epithelial cells, was linked to lung adenocarcinoma (LUAD). However, the underlying functions and mechanisms of AQP3 in the tumor microenvironment (TME) of LUAD have not been elucidated. Single-cell RNA sequencing (scRNA-seq) was used to study the composition, lineage, and functional states of TME-infiltrating immune cells and discover AQP3-expressing subpopulations in five LUAD patients. Then the identifications of its function on TME were examined in vitro and in vivo. AQP3 was associated with TNM stages and lymph node metastasis of LUAD patients. We classified inter- and intra-tumor diversity of LUAD into twelve subpopulations using scRNA-seq analyses. The analysis showed AQP3 was mainly enriched in subpopulations of M2 macrophages. Importantly, mechanistic investigations indicated that AQP3 promoted M2 macrophage polarization by the PPAR-γ/NF-κB axis, which affected tumor growth and migration via modulating IL-6 production. Mixed subcutaneous transplanted tumor mice and Aqp3 knockout mice models were further utilized, and revealed that AQP3 played a critical role in mediating M2 macrophage polarization, modulating glucose metabolism in tumors, and regulating both upstream and downstream pathways. Overall, our study demonstrated that AQP3 could regulate the proliferation, migration, and glycometabolism of tumor cells by modulating M2 macrophages polarization through the PPAR-γ/NF-κB axis and IL-6/IL-6R signaling pathway, providing new insight into the early detection and potential therapeutic target of LUAD.