Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

Single-cell RNA sequencing reveals the role of GTF2F2 in ovarian cancer oncogenesis and progression

BACKGROUND

Ovarian cancer is one of the most common malignancies of the female reproductive system and is associated with poor prognosis. This study aimed to utilize single-cell RNA sequencing to investigate the heterogeneity of malignant epithelial cells in ovarian cancer, focusing on their potential functions and the implications for treatment and prognosis.

METHODS

Single-cell RNA sequencing data were clustered using a single-cell transcriptome clustering method, and malignant epithelial cells were identified through copy number variation analysis. The interaction patterns between different malignant subpopulations and immune/stromal cells were analyzed using cell-to-cell communication analysis. A risk score (URS) model based on the UBE2C + epithelial subpopulation was then constructed through LASSO and multivariable Cox regression. High and low URS groups were compared in terms of tumor mutational burden (TMB), survival outcomes, and drug sensitivity. Finally, the role of GTF2F2 in ovarian cancer progression was validated through gene knockdown experiments in an ovarian cancer cell line (ES-2).

RESULTS

Three major malignant epithelial cell subpopulations were identified (TMSB4X + Epi, TSC22D1 + Epi, and UBE2C + Epi). The UBE2C + Epi subpopulation exhibited higher stemness and greater invasive potential. The constructed URS model effectively stratified patients into high- and low-risk groups, with the high-risk group displaying a higher TMB level (p = 0.00011). Drug sensitivity predictions indicated that osimertinib, rapamycin, and dihydrorotenone might have stronger inhibitory effects in the high-risk group, whereas ERK inhibitors were more effective in the low-risk group. Functional assays demonstrated that GTF2F2 knockdown significantly suppressed ovarian cancer cell migration and invasion. Western blot analyses further showed elevated E-cadherin and reduced N-cadherin expression, suggesting that GTF2F2 may promote epithelial-mesenchymal transition (EMT).

CONCLUSION

The risk score model established in this study offers a novel framework for patient stratification and personalized therapy. Notably, the identification of the UBE2C + Epi subpopulation and key genes such as GTF2F2 highlights potential diagnostic and therapeutic targets, shedding light on the pathogenesis of ovarian cancer and paving the way for precision medicine approaches.

The distinct roles of IL-37 and IL-38 in non-small cell lung carcinoma and their clinical implications

Lung cancer, a significant global health challenge, is primarily classified into non-small cell lung cancer (NSCLC) and small cell lung cancer. Despite advancements in targeted therapies and immunotherapies, NSCLC outcomes remain poor, with low five-year survival rates. Given the lung's constant exposure to the environment and the presence of mucosal-associated lymphoid tissues, immunity plays a crucial role in NSCLC development. Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 have shown promise. However, adverse immune events limit their efficacy. This review highlights the contrasting roles of IL-37 and IL-38 in NSCLC pathogenesis. IL-37, an anti-inflammatory cytokine, suppresses tumour growth. It achieves this by modulating macrophage polarization and dendritic cell maturation. Correlations between intra-tumoral IL-37 expression and improved survival suggest a protective role in NSCLC. This may be mediated through VEGF inhibition and immune regulation. Conversely, IL-38, while anti-inflammatory in certain contexts, exhibits a pro-tumorigenic role in NSCLC. IL-38 enhances tumour progression by increasing pro-inflammatory cytokine secretion and facilitating immune evasion, potentially through NF-κB signalling. Notably, IL-38 negatively regulates IL-37, further promoting tumorigenesis. Emerging data suggest that IL-37 has therapeutic potential in inhibiting NSCLC metastasis and supporting immune modulation. In contrast, IL-38 presents a potential target for mitigating pro-inflammatory microenvironment effects. The distinct roles of these cytokines emphasize the complex immune dynamics in NSCLC. Further exploration of their molecular mechanisms and therapeutic implications is warranted. Targeting IL-37 and IL-38 may offer novel strategies for enhancing NSCLC treatment outcomes.

Identification and Validation of Prognostic Genes Related to Histone Lactylation Modification in Glioblastoma: An Integrated Analysis of Transcriptome and Single-cell RNA Sequencing

Background: The impact of histone lactylation modification (HLM) on glioblastoma (GBM) progression is not well understood. This study aimed to identify HLM-associated prognostic genes in GBM and explore their mechanisms of action. Methods: The presence and role of lactylation in glioma clinical tissue samples and its correlation with GBM progression were analysed through immunohistochemical staining and Western blotting. Sequencing data for GBM were obtained from publicly available databases. An initial correlation analysis was performed between global HLM levels and GBM. Differentially expressed HLM-related genes (HLMRGs) in GBM were identified by intersecting differentially expressed genes (DEGs) from the TCGA-GBM dataset, key module genes derived from weighted gene coexpression network analysis (WGCNA), and previously reported HLMRGs. Prognostic genes were subsequently identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses, which formed the basis for constructing a risk prediction model. Associations between HLMRGs and GBM were further evaluated via single-cell RNA sequencing (scRNA-seq) datasets. Complementary analyses, including functional enrichment, immune infiltration, somatic mutation, and nomogram-based survival prediction, were conducted alongside in vitro experiments. Additionally, drug sensitivity and Chinese medicine prediction analyses were performed to identify potential therapeutic agents for GBM. Results: We detected a significant increase in global lactylation levels in GBM, which correlated with patient survival. We identified 227 differentially expressed HLMRGs from the intersection of 3,343 differentially expressed genes and 948 key module genes, indicating strong prognostic potential. Notably, genes such as SNCAIP, TMEM100, NLRP11, HOXC11, and HOXD10 were highly expressed in GBM. Functional analysis suggested that HLMRGs are involved primarily in pathways related to cytokine‒cytokine receptor interactions, cell cycle regulation, and cellular interactions, including microglial differentiation states. Further connections were established between HLMRGs and infiltrating immune cells, particularly type 1 T helper (Th1) cells, as well as mutations in genes such as PTEN. The potential therapeutic agents identified included ATRA and Can Sha. Conclusion: The HLM-related gene risk prediction model shows substantial promise for improving patient management in GBM, providing crucial insights for clinical prognostic evaluations and immunotherapeutic approaches in GBM.

Global research trends of tumor microenvironment in non-small cell lung cancer with epidermal growth factor receptor mutation: a bibliometric analysis from 2014 to 2023

Purpose

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and about half of the patients had mutations in the epidermal growth factor receptor (EGFR) gene. Changes in the tumor microenvironment after EGFR mutation are closely related to tumor progression and treatment efficacy.

Materials and methods

We searched the Web of Science Core Collection database to select the articles related to tumor microenvironment in non-small cell lung cancer with epidermal growth factor receptor mutation. The countries/regions, institutes, authors, journals, references, and keywords were visualized and analyzed.

Results

227 relevant studies were obtained from WoSCC. These articles came from 102 countries and 1179 institutions. After network analysis, it was found that the intensity of USA cooperation with China was the greatest (LS=13), followed by cooperation with South Korea (LS=3) and with Japan (LS=3). A total of 2267 authors participated the all 227 articles. 112 journals were covered, and Frontiers in Oncology published most papers (n=16, 14.3%). A total of 7964 co-cited references are related to TME in NSCLC with EGFR mutation. "EGFR" is the keyword with the highest centrality (C=0.31) and first appeared. The keywords that burst in the last 1 year (2022-2023) are "immunotherapy", "mechanism", "lung neoplasms", "T cells", and "multicenter".

Conclusion

Effective drug treatment of advanced NSCLC with EGFR mutations after failure of first-line chemotherapy is one of the hotspots, in which the efficacy of immune checkpoint inhibitors may be the direction of the current and future studies that need to find a breakthrough.

Integrative bioinformatics analysis of high-throughput sequencing and in vitro functional analysis leads to uncovering key hub genes in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCA) is a type of cancer that starts in the cells lining the esophagus, the tube connecting the throat to the stomach. It is known for its aggressive nature and poor prognosis. Understanding the key factors that drive this cancer is crucial for developing better diagnostic tools and treatments.

METHODS

Gene expression profiles of ESCA were analyzed using Gene Expression Omnibus (GEO) datasets (GSE23400, GSE29001, GSE92396, and GSE1420) from the GEO database. Differentially expressed genes (DEGs) were identified using the limma package, and a protein-protein interaction (PPI) network was constructed using the STRING database. Hub genes were identified based on the degree method. Further validation was performed through reverse transcription quantitative PCR (RT-qPCR), mutational and copy number variation (CNV) analysis via the cBioPortal database, promoter methylation analysis using the OncoDB and GSCA databases, survival analysis, immune infiltration analysis through the GSCA database, and functional assays, including knockdown of key genes.

RESULTS

We identified four key hub genes, COL3A1, COL4A1, COL5A2, and CXCL8 that play significant roles in ESCA. These genes were highly expressed in ESCA tissues and cell lines, with expression levels significantly (p-value < 0.001) elevated compared to normal controls. Receiver operating characteristic (ROC) curve analysis revealed exceptional diagnostic performance for all four genes, with area under the curve (AUC) values of 1.0, indicating perfect sensitivity and specificity in distinguishing ESCA from normal controls. Mutational analysis revealed that COL3A1 was altered in 67% of ESCA samples, primarily through missense mutations, while COL5A2 exhibited alterations in 50% of the samples, including splice site and missense mutations. Additionally, gene amplification patterns were observed in all four hub genes, further validating their oncogenic potential in ESCA progression. A significant (p-value < 0.05) promoter hypomethylation was detected in these genes, suggesting a potential regulatory role in their expression. Functional assays demonstrated that knocking down COL3A1 and COL4A1 led to decreased cell proliferation, colony formation, and migration, indicating their critical roles in tumor progression. Additionally, these genes were involved in pathways related to the extracellular matrix and immune system modulation.

CONCLUSION

COL3A1, COL4A1, COL5A2, and CXCL8 are crucial in ESCA development and progression, particularly in remodeling the extracellular matrix, modulating the immune system, and promoting metastasis. These findings suggest that these genes could serve as potential biomarkers for diagnosing ESCA and targets for future therapies. Future research should focus on in vivo validation of these findings and clinical testing to assess the therapeutic potential of targeting these genes in ESCA treatment.

A dataset of single-cell transcriptomic atlas of Bama pig and potential marker genes across seven tissues

The use of single-cell sequencing technology for single-cell transcriptomics studies in pigs is expanding progressively. However, the comprehensive classification of cell types across different anatomical tissues and organs of pig in multiple datasets remains relatively limited. This study employs single-cell and single-nucleus sequencing technologies in Bama pig to identify unique marker genes and their corresponding transcriptomic profiles across diverse cell types in various anatomical tissues and organs, including subcutaneous fat, visceral fat, psoas major muscle, liver, spleen, lung, and kidney. Through detailed data analyses, we observed widespread cellular diversity across various anatomical tissues and organs of Bama pig. This work contributes a comprehensive dataset that supports physiological studies and aids in the identification and prediction of potential marker genes through single-cell transcriptomics of these tissues. The methodologies and data employed in this study are designed to improve the accuracy of cell type identification and ensure consistent cell type allocation.

Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Promotion Mechanisms of Stromal Cell-Mediated Lung Cancer Development Within Tumor Microenvironment

Lung cancer, with its high incidence and mortality rates, has garnered significant attention in the medical community. The tumor microenvironment (TME), composed of tumor cells, stromal cells, extracellular matrix, surrounding blood vessels, and other signaling molecules, plays a pivotal role in the development of lung cancer. Stromal cells within the TME hold potential as therapeutic targets for lung cancer treatment. However, the precise and comprehensive mechanisms by which stromal cells contribute to lung cancer progression have not been fully elucidated. This review aims to explore the mechanisms through which stromal cells in the tumor microenvironment promote lung cancer development, with a particular focus on how immune cells, tumor-associated fibroblasts, and endothelial cells contribute to immune suppression, inflammation, and angiogenesis. The goal is to provide new insights and potential strategies for the diagnosis and treatment of lung cancer.

Investigating the Impact of B Cell-Related Genes on Colorectal Cancer Immunosuppressive Environment and Immunotherapy Evasion

We aimed to elucidate the prognostic and immunological roles of B cell-related genes in colorectal cancer (CRC). This study comprehensively integrated data from single-cell RNA-sequencing, TCGA, GEO, IMvigor210, GDSC, CancerSEA, HPA, and TISIDB databases to explore prognostic implications and immunological significance of B cell-related gene signature in CRC. We identified seven prognostically significant B cell-related genes for constructing a risk score. Clinical relevance analysis indicated that this risk score served as an independent prognostic factor, with the model accurately predicting patient outcomes. GSEA results implicated the risk score in immune function, cell cycle, and DNA replication. Immune infiltration analysis revealing lower levels of B cells, CD4+ cells, and CD8+ cells in the high-risk group, correlating with decreased immune activity and function. IMvigor210 and TIDE analysis indicated poorer prognosis among high-risk group patients receiving immune therapy. Additionally, the high-risk group exhibited lower sensitivity to immune therapy. Further analysis of drug sensitivity suggested higher resistance to common chemotherapy drugs among high-risk groups. Finally, we identified HSPA1A as the gene with the strongest association with immune and inflammatory responses. Validation of HSPA1A protein expression and prognosis demonstrated elevated expression in CRC compared to normal colorectal tissue, further reinforcing its association with poorer prognosis and higher tumor stage. The risk score exhibited substantial variations in clinical characteristics, functional mechanism, TMB, drug sensitivity, immune cell infiltration, and immune subtype. Our findings may aid in clinical decision-making by shedding light on novel and promising biomarkers for CRC prognosis and immunotherapy response prediction.

The key role of the NUDT3 gene in lung adenocarcinoma progression and its association with the manganese ion metabolism family

BACKGROUND

Lung adenocarcinoma is one of the most common malignant tumors worldwide. Its complex molecular mechanisms and high tumor heterogeneity pose significant challenges for clinical treatment. The manganese ion metabolism family plays a crucial role in various biological processes, and the abnormal expression of the NUDT3 gene in multiple cancers has drawn considerable attention. This study aims to systematically analyze the expression characteristics of the NUDT3 gene in lung adenocarcinoma and its association with tumor progression, integrating single-cell transcriptomic analysis and experimental validation using cell lines.

METHODS

This study employed a comprehensive set of analytical approaches. Single-cell transcriptomic analysis of two normal and four lung adenocarcinoma samples from the GSE149655 dataset was performed using the Seurat package to identify and annotate distinct cell populations, focusing on epithelial and macrophage subtypes. Non-negative matrix factorization (NMF) and gene set variation analysis (GSVA) were applied to assess the functional enrichment and expression profiles of the manganese ion metabolism family across 14 cancers. Quantitative PCR (qPCR) was conducted to evaluate the relative mRNA expression of NUDT3 in A549 lung adenocarcinoma cell lines compared to BEAS-2B normal bronchial epithelial cell lines. GAPDH was used as the reference gene for normalization, and the relative expression levels of NUDT3 in these cell lines were analyzed to confirm bioinformatics findings.

RESULTS

NUDT3 was found to be significantly upregulated in lung adenocarcinoma and highly correlated with tumor mutation burden (TMB) and mutation enrichment (MEs). Single-cell transcriptomic analyses demonstrated elevated NUDT3 expression in lung adenocarcinoma epithelial cells, with expression levels increasing in cells associated with advanced tumor stages. Furthermore, qPCR analysis confirmed the upregulation of NUDT3 in A549 cells compared to BEAS-2B cells, consistent with transcriptomic data. These results also highlighted significant expression differences of the manganese ion metabolism family across various cancers, including BLCA, BRCA, and COAD, with notable NUDT3 mutations enriched in these cancers.

CONCLUSION

This study underscores the critical role of NUDT3 in lung adenocarcinoma progression and its potential as a therapeutic target. These findings contribute to the understanding of the manganese ion metabolism family in cancer biology and provide a foundation for precision therapies targeting NUDT3 in lung adenocarcinoma.

Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization

Artificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In immunogenomics, AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis, thus providing strong support for personalized treatments. In radiomics, AI can analyze high-dimensional features from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) images to discover imaging biomarkers associated with tumor heterogeneity, treatment response, and disease progression, thereby enabling non-invasive, real-time assessments for personalized therapy. Pathomics leverages AI for deep analysis of digital pathology images, and can uncover subtle changes in tissue microenvironments, cellular characteristics, and morphological features, and offer unique insights into immunotherapy response prediction and biomarker discovery. These AI-driven technologies not only enhance the speed, accuracy, and robustness of biomarker discovery but also significantly improve the precision, personalization, and effectiveness of clinical treatments, and are driving a shift from empirical to precision medicine. Despite challenges such as data quality, model interpretability, integration of multi-modal data, and privacy protection, the ongoing advancements in AI, coupled with interdisciplinary collaboration, are poised to further enhance AI's roles in biomarker discovery and immunotherapy response prediction. These improvements are expected to lead to more accurate, personalized treatment strategies and ultimately better patient outcomes, marking a significant step forward in the evolution of precision medicine.

Tumor-Activated Neutrophils Promote Lung Cancer Progression through the IL-8/PD-L1 Pathway

BACKGROUND

Lung cancer remains a major global health threat due to its complex microenvironment, particularly the role of neutrophils, which are crucial for tumor development and immune evasion mechanisms. This study aimed to delve into the impact of lung cancer cell-conditioned media on neutrophil functions and their potential implications for lung cancer progression.

METHODS

Employing in vitro experimental models, this study has analyzed the effects of lung cancer cell-conditioned media on neutrophil IL-8 and IFN-γ secretion, apoptosis, PD-L1 expression, and T-cell proliferation by using techniques, such as ELISA, flow cytometry, immunofluorescence, and CFSE proliferation assay. The roles of IL-8/PD-L1 in regulating neutrophil functions were further explored using inhibitors for IL-8 and PD-L1.

RESULTS

Lung cancer cell lines were found to secrete higher levels of IL-8 compared to normal lung epithelial cells. The conditioned media from lung cancer cells significantly reduced apoptosis in neutrophils, increased PD-L1 expression, and suppressed T-cell proliferation and IFN-γ secretion. These effects were partially reversed in the presence of IL-8 inhibitors in Tumor Tissue Culture Supernatants (TTCS), while being further enhanced by IL-8. Both apoptosis and PD-L1 expression in neutrophils demonstrated dose-dependency to TTCS. Additionally, CFSE proliferation assay results further confirmed the inhibitory effect of lung cancer cell-conditioned media on T-- cell proliferation.

CONCLUSION

This study has revealed lung cancer cell-conditioned media to modulate neutrophil functions through regulating factors, such as IL-8, thereby affecting immune regulation and tumor progression in the lung cancer microenvironment.

Single-Cell RNA-Sequencing: Opening New Horizons for Breast Cancer Research

Breast cancer is the most prevalent malignant tumor among women with high heterogeneity. Traditional techniques frequently struggle to comprehensively capture the intricacy and variety of cellular states and interactions within breast cancer. As global precision medicine rapidly advances, single-cell RNA sequencing (scRNA-seq) has become a highly effective technique, revolutionizing breast cancer research by offering unprecedented insights into the cellular heterogeneity and complexity of breast cancer. This cutting-edge technology facilitates the analysis of gene expression profiles at the single-cell level, uncovering diverse cell types and states within the tumor microenvironment. By dissecting the cellular composition and transcriptional signatures of breast cancer cells, scRNA-seq provides new perspectives for understanding the mechanisms behind tumor therapy, drug resistance and metastasis in breast cancer. In this review, we summarized the working principle and workflow of scRNA-seq and emphasized the major applications and discoveries of scRNA-seq in breast cancer research, highlighting its impact on our comprehension of breast cancer biology and its potential for guiding personalized treatment strategies.

Advances in Melanoma: From Genetic Insights to Therapeutic Innovations

Advances in melanoma research have unveiled critical insights into its genetic and molecular landscape, leading to significant therapeutic innovations. This review explores the intricate interplay between genetic alterations, such as mutations in BRAF, NRAS, and KIT, and melanoma pathogenesis. The MAPK and PI3K/Akt/mTOR signaling pathways are highlighted for their roles in tumor growth and resistance mechanisms. Additionally, this review delves into the impact of epigenetic modifications, including DNA methylation and histone changes, on melanoma progression. The tumor microenvironment, characterized by immune cells, stromal cells, and soluble factors, plays a pivotal role in modulating tumor behavior and treatment responses. Emerging technologies like single-cell sequencing, CRISPR-Cas9, and AI-driven diagnostics are transforming melanoma research, offering precise and personalized approaches to treatment. Immunotherapy, particularly immune checkpoint inhibitors and personalized mRNA vaccines, has revolutionized melanoma therapy by enhancing the body's immune response. Despite these advances, resistance mechanisms remain a challenge, underscoring the need for combined therapies and ongoing research to achieve durable therapeutic responses. This comprehensive overview aims to highlight the current state of melanoma research and the transformative impacts of these advancements on clinical practice.

Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

BACKGROUND

The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states.

RESULTS

We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure.

CONCLUSIONS

Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.

The genomic landscape of the immune system in lung cancer: present insights and continuing investigations

Lung cancer is one of the most prevalent malignancies worldwide, contributing to over a million cancer-related deaths annually. Despite extensive research investigating the genetic factors associated with lung cancer susceptibility and prognosis, few studies have explored genetic predispositions regarding the immune system. This review discusses the most recent genomic findings related to the susceptibility to or protection against lung cancer, patient survival, and therapeutic responses. The results demonstrated the effect of immunogenetic variations in immune system-related genes associated with innate and adaptive immune responses, cytokine, and chemokine secretions, and signaling pathways. These genetic diversities may affect the crosstalk between tumor and immune cells within the tumor microenvironment, influencing cancer progression, invasion, and prognosis. Given the considerable variability in the individual immunegenomics profiles, future studies should prioritize large-scale analyses to identify potential genetic variations associated with lung cancer using highthroughput technologies across different populations. This approach will provide further information for predicting response to targeted therapy and promotes the development of new measures for individualized cancer treatment.

New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model

OBJECTIVE

The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients.

METHODS

Differentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights.

RESULTS

High METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1.

CONCLUSION

The random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.

Molecular mechanism of radiation tolerance in lung adenocarcinoma cells using single-cell RNA sequencing

The efficacy of radiotherapy, a cornerstone in the treatment of lung adenocarcinoma (LUAD), is profoundly undermined by radiotolerance. This resistance not only poses a significant clinical challenge but also compromises patient survival rates. Therefore, it is important to explore this mechanism for the treatment of LUAD. Multiple public databases were used for single-cell RNA sequencing (scRNA-seq) data. We filtered, normalized and downscaled scRNA-seq data based on the Seurat package to obtain different cell subpopulations. Subsequently, the ssGSEA algorithm was used to assess the enrichment scores of the different cell subpopulations, and thus screen the cell subpopulations that are most relevant to radiotherapy tolerance based on the Pearson method. Finally, pseudotime analysis was performed, and a preliminary exploration of gene mutations in different cell subpopulations was performed. We identified HIST1H1D+ A549 and PIF1+ A549 as the cell subpopulations related to radiotolerance. The expression levels of cell cycle-related genes and pathway enrichment scores of these two cell subpopulations increased gradually with the extension of radiation treatment time. Finally, we found that the proportion of TP53 mutations in patients who had received radiotherapy was significantly higher than that in patients who had not received radiotherapy. We identified two cellular subpopulations associated with radiotherapy tolerance, which may shed light on the molecular mechanisms of radiotherapy tolerance in LUAD and provide new clinical perspectives.

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.

Exploration of the prognostic prediction value of the PANoptosis-based risk score and its correlation with tumor immunity in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common cancer with high mortality worldwide. PANoptosis is a novel inflammatory programmed cell death modality with the characteristics of pyroptosis, apoptosis and necroptosis. It is necessary to explore PANoptosis-related genes in LUAD patients and offer evidence for prognosis prediction and therapeutic strategies. Single-cell RNA sequencing data and RNA expression profiles of LUAD patients from The Cancer Genome Atlas and Gene Expression Omnibus databases are used to screen PANoptosis-related differential genes for the construction of a risk model. Fifteen PANoptosis-related markers with prognostic value were identified by Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis. Kaplan-Meier analysis and receiver operating characteristic curve analysis further demonstrated the significant predictive capability. Immune infiltration, Single Nucleotide Variants (SNV) mutations, and clinical drug susceptibility were analyzed. In conclusion, a risk model of 15 PANoptosis-related genes has significant value in prognostic prediction for LUAD and has potential to direct clinical therapeutic strategies during the treatment.

Glycosylation-related genes mediated prognostic signature contribute to prognostic prediction and treatment options in ovarian cancer: based on bulk and single‑cell RNA sequencing data

BACKGROUND

Ovarian cancer (OC) is a complex disease with significant tumor heterogeneity with the worst prognosis and highest mortality among all gynecological cancers. Glycosylation is a specific post-translational modification that plays an important role in tumor progression, immune escape and metastatic spread. The aim of this work was to identify the major glycosylation-related genes (GRGs) in OC and construct an effective GRGs signature to predict prognosis and immunotherapy.

METHODS

AUCell algorithm was used to identify glycosylation-related genes (GRGs) based on the scRNA-seq and bulk RNA-seq data. An effective GRGs signature was conducted using COX and LASSO regression algorithm. The texting dataset and clinical sample data were used to assessed the accuracy of GRGs signature. We evaluated the differences in immune cell infiltration, enrichment of immune checkpoints, immunotherapy response, and gene mutation status among different risk groups. Finally, RT-qPCR, Wound-healing assay, Transwell assay were performed to verify the effect of the CYBRD1 on OC.

RESULTS

A total of 1187 GRGs were obtained and a GRGs signature including 16 genes was established. The OC patients were divided into high- and low- risk group based on the median riskscore and the patients in high-risk group have poor outcome. We also found that the patients in low-risk group have higher immune cell infiltration, enrichment of immune checkpoints and immunotherapy response. The results of laboratory test showed that CYBRD1 can promote the invasion, and migration of OC and is closely related to the poor prognosis of OC patients.

CONCLUSIONS

Our study established a GRGs signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prognosis prediction and treatment strategy for OC.

Single-cell transcriptome analysis reveals immunosuppressive landscape in overweight and obese colorectal cancer

BACKGROUND

Overweight and obesity are established risk factors for various types of cancers including colorectal cancer (CRC). However the underlying molecular mechanisms remain unclear. An in-depth understanding of the oncologic characteristics of overweight and obese CRC at the single-cell level can provide valuable insights for the development of more effective treatment strategies for CRC.

METHODS

We conducted single-cell RNA sequencing (scRNA-seq) analysis on tumor and adjacent normal colorectal samples from 15 overweight/obese and 15 normal-weight CRC patients. Immunological and metabolic differences between overweight/obese CRC and non-obese CRC were characterized.

RESULTS

We obtained single-cell transcriptomics data from a total of 192,785 cells across all samples. By evaluating marker gene expression patterns, we annotated nine main cell types in the CRC ecosystem. Specifically, we found that the cytotoxic function of effector T cells and NK cells was impaired in overweight/obese CRC compared with non-obese CRC, relating to its metabolic dysregulation. CD4+T cells in overweight/obese CRC exhibited higher expression of immune checkpoint molecules. The antigen-presenting ability of DCs and B cells is down-regulated in overweight/obese CRC, which may further aggravate the immunosuppression of overweight/obese CRC. Additionally, dysfunctional stromal cells were identified, potentially promoting invasion and metastasis in overweight/obese CRC. Furthermore, we discovered the up-regulated metabolism of glycolysis and lipids of tumor cells in overweight/obese CRC, which may impact the metabolism and function of immune cells. We also identified inhibitory interactions between tumor cells and T cells in overweight/obese CRC.

CONCLUSIONS

The study demonstrated that overweight/obese CRC has a more immunosuppressive microenvironment and distinct metabolic reprogramming characterized by increased of glycolysis and lipid metabolism. These findings may have implications for the development of novel therapeutic strategies for overweight/obese CRC patients.

Research progress on the immune microenvironment and immunotherapy in gastric cancer

The tumor microenvironment, particularly the immune microenvironment, plays an indispensable role in the malignant progression and metastasis of gastric cancer (GC). As our understanding of the GC microenvironment continues to evolve, we are gaining deeper insights into the biological mechanisms at the single-cell level. This, in turn, has offered fresh perspectives on GC therapy. Encouragingly, there are various monotherapy and combination therapies in use, such as immune checkpoint inhibitors, adoptive cell transfer therapy, chimeric antigen receptor T cell therapy, antibody-drug conjugates, and cancer vaccines. In this paper, we review the current research progress regarding the GC microenvironment and summarize promising immunotherapy research and targeted therapies.

Metabolic and senescence characteristics associated with the immune microenvironment in non-small cell lung cancer: insights from single-cell RNA sequencing

Non-small lung cancer (NSCLC) has been defined as a highly life-threatening heterogeneous disease, with high mortality and occurrence. Recent research has indicated that tumor-infiltrating lymphocytes play a key determinant role in cancer progression. Emerging single-cell RNA sequencing (also termed scRNA-seq) has been extensively applied to depict the baseline landscape of the cell composition and function phenotype in the tumor environment (TME). Herein, we dissected the cell types in NSCLC samples (including tissue and blood) and identified three types of cell marker genes including cancer cells, T cells, and macrophages by integrating two NSCLC-associated scRNA-seq datasets in GEO. Survival analysis indicated that 17 marker genes were related to tumor prognosis. Function annotation was used to scrutinize the molecular mechanism of these marker genes in different cells. Besides, we investigated the developmental trajectory and T cell receptor repertoire diversity of tumor-infiltrating T cells. Our analysis will help further understand the complexity of cell components and the heterogeneity of TME in NSCLC.

Development and validation of cuproptosis-related lncRNAs associated with pancreatic cancer immune microenvironment based on single-cell

Background

Cuproptosis, a novel mode of cell death associated with the tricarboxylic acid (TCA) cycle, is relevant to the development of cancer. However, the impact of single-cell-based Cuproptosis-associated lncRNAs on the Tumor immune microenvironment (TIME) of Pancreatic adenocarcinoma (PAAD) and its potential value for individualized immunotherapy has not been clarified.

Methods

14 immune-related CRGs were screened by exploring the interaction between differentially expressed Immune-Related Genes (IRGs) and Cuproptosis-Related Genes (CRGs) in PAAD. Next, the expression amount and expression distribution of CRGs in single-cell samples were analyzed by focusing on 7-CRGs with significant expressions. On the one hand, MAP2K2, SOD1, and VEGFA, which were significantly differentially expressed between PAAD sites and normal tissues adjacent to them, were subjected to immunohistochemical validation and immune landscape analysis. On the other hand, from these 7-CRGs, prognostic signatures of lncRNAs were established by co-expression and LASSO-COX regression analysis, and their prognostic value and immune relevance were assessed. In addition, this study not only validated the hub CRGs and the lncRNAs constituting the signature in a PAAD animal model treated with immunotherapy-based combination therapy using immunohistochemistry and qRT-PCR but also explored the potential value of the combination of targeted, chemotherapy and immunotherapy.

Results

Based on the screening of 7-CRGs significantly expressed in a PAAD single-cell cohort and their co-expressed Cuproptosis-Related lncRNAs (CRIs), this study constructed a prognostic signature of 4-CRIs named CIR-score. A Nomogram integrating the CIR-score and clinical risk factors was constructed on this basis to predict the individualized survival of patients. Moreover, high and low-risk groups classified according to the median of signatures exhibited significant differences in clinical prognosis, immune landscape, bioenrichment, tumor burden, and drug sensitivity. And the immunohistochemical and qRT-PCR results of different mouse PAAD treatment strategies were consistent with the trend of inter-group variability in drug sensitivity of hub CRGs and CIR-score. The combination of immunotherapy, targeted therapy, and chemotherapy exhibited a better tumor suppression effect.

Conclusion

CIR-score, as a Cuproptosis-related TIME-specific prognostic signature based on PAAD single cells, not only predicts the prognosis and immune landscape of PAAD patients but also provides a new strategy for individualized immunotherapy-based combination therapy.

Exo-miR-1290-induced by COX-2 overexpression promotes cancer-associated fibroblasts activation and tumor progression by CUL3-Nrf2 pathway in lung adenocarcinoma

BACKGROUND

Cancer-associated fibroblasts (CAFs) are critically involved in tumor progression by maintaining extracellular mesenchyma (ECM) production and improving tumor development. Cyclooxygenase-2 (COX-2) has been proved to promote ECM formation and tumor progression. However, the mechanisms of COX-2 mediated CAFs activation have not yet been elucidated. Therefore, we conducted this study to identify the effects and mechanisms of COX-2 underlying CAFs activation by tumor-derived exosomal miRNAs in lung adenocarcinoma (LUAD) progression.

METHODS

As measures of CAFs activation, the expressions of fibroblasts activated protein-1 (FAP-1) and α-smooth muscle actin (α-SMA), the main CAFs markers, were detected by Western blotting and Immunohistochemistry. And the expression of Fibronectin (FN1) was used to analyze ECM production by CAFs. The exosomes were extracted by ultracentrifugation and exo-miRNAs were detected by qRT-PCR. Herein, we further elucidated the implicated mechanisms using online prediction software, luciferase reporter assays, co-immunoprecipitation, and experimental animal models.

RESULTS

In vivo, a positive correlation was observed between the COX-2 expression levels in parenchyma and α-SMA/FN1 expression levels in mesenchyma in LUAD. However, PGE2, one of major product of COX-2, did not affect CAFs activation directly. COX-2 overexpression increased exo-miR-1290 expression, which promoted CAFs activation. Furthermore, Cullin3 (CUL3), a potential target of miR-1290, was found to suppress COX-2/exo-miR-1290-mediated CAFs activation and ECM production, consequently impeding tumor progression. CUL3 is identified to induce the Nuclear Factor Erythroid 2-Related Factor 2 (NFE2L2, Nrf2) ubiquitination and degradation, while exo-miR-1290 can prevent Nrf2 ubiquitination and increase its protein stability by targeting CUL3. Additionally, we identified that Nrf2 is direcctly bound with promoters of FAP-1 and FN1, which enhanced CAFs activation by promoting FAP-1 and FN1 transcription.

CONCLUSIONS

Our data identify a new CAFs activation mechanism by exosomes derived from cancer cells that overexpress COX-2. Specifically, COX-2/exo-miR-1290/CUL3 is suggested as a novel signaling pathway for mediating CAFs activation and tumor progression in LUAD. Consequently, this finding suggests a novel strategy for cancer treatment that may tackle tumor progression in the future. Video Abstract.

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma (HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells' expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancer-associated fibroblasts (CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-to-mesenchymal transition (EMT) model and the mechanism of drug resistance, as well as its clinical value.

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.

Single-cell RNA sequencing reveals enhanced antitumor immunity after combined application of PD-1 inhibitor and Shenmai injection in non-small cell lung cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have altered the clinical management of non-small cell lung cancer (NSCLC). However, the low response rate, severe immune-related adverse events (irAEs), and hyperprogressive disease following ICIs monotherapy require attention. Combination therapy may overcome these limitations and traditional Chinese medicine with immunomodulatory effects provides a promising approach. Shenmai injection (SMI) is a clinically effective adjuvant treatment for cancer with chemotherapy and radiotherapy. Therefore, the combined effects and mechanisms of SMI and programmed death-1 (PD-1) inhibitor against NSCLC was focused on this study.

METHODS

A Lewis lung carcinoma mouse model and a lung squamous cell carcinoma humanized mouse model were used to investigate the combined efficacy and safety of SMI and PD-1 inhibitor. The synergistic mechanisms of the combination therapy against NSCLC were explored using single-cell RNA sequencing. Validation experiments were performed using immunofluorescence analysis, in vitro experiment, and bulk transcriptomic datasets.

RESULTS

In both models, combination therapy alleviated tumor growth and prolonged survival without increasing irAEs. The GZMA^high and XCL1^high natural killer (NK) cell subclusters with cytotoxic and chemokine signatures increased in the combination therapy, while malignant cells from combination therapy were mainly in the apoptotic state, suggesting that mediating tumor cell apoptosis through NK cells is the main synergistic mechanisms of combination therapy. In vitro experiment confirmed that combination therapy increased secretion of Granzyme A by NK cells. Moreover, we discovered that PD-1 inhibitor and SMI combination blocked inhibitory receptors on NK and T cells and restores their antitumoral activity in NSCLC better than PD-1 inhibitor monotherapy, and immune and stromal cells exhibited a decrease of angiogenic features and attenuated cancer metabolism reprogramming in microenvironment of combination therapy.

CONCLUSIONS

This study demonstrated that SMI reprograms tumor immune microenvironment mainly by inducing NK cells infiltration and synergizes with PD-1 inhibitor against NSCLC, suggested that targeting NK cells may be an important strategy for combining with ICIs. Video Abstract.

Identification and validation of the model consisting of DDX49, EGFR, and T-stage as a possible risk factor for lymph node metastasis in patients with lung cancer

INTRODUCTION

The lymph node metastasis stage of lung cancer is an important decisive factor in the need for postoperative adjuvant treatment and the difference between stage IIIa and stage IIIB that is the necessary information to distinguish whether surgery can be performed or not. The specificity of the clinical diagnosis of lung cancer with lymph node metastasis cannot meet the requirements of preoperative evaluation of surgical indications and prediction of surgical removal range in lung cancer.

METHODS

This was an early experimental laboratory trial. The model identification data included the RNA sequence data of 10 patients from our clinical data and 188 patients with lung cancer from The Cancer Genome Atlas dataset. The model development and validation data consisted of RNA sequence data for 537 cases from the Gene Expression Omnibus dataset. We explore the predictive value of the model on two independent clinical data.

RESULTS

A higher specificity of diagnostic model for patients with lung cancer with lymph node metastases consisted of DDX49, EGFR, and tumor stage (T-stage), which were the independent predictive factors. The area under the curve value, specificity, and sensitivity for predicting lymph node metastases were 0.835, 70.4%, and 78.9% at RNA expression level in the training group, and 0.681, 73.2%, and 75.7% at RNA expression level in the validation group as shown as in result part. To verify the predictive performance of the combined model for lymph node metastases, we downloaded the GSE30219 data set (n = 291) and the GSE31210 data set (n = 246) from the Gene Expression Omnibus (GEO) database as the training group and validation group, respectively. In addition, the model had a higher specificity for predicting lymph node metastases in independent tissue samples.

CONCLUSIONS

Determination of DDX49, EGFR, and T-stage could form a novel prediction model to improve the diagnostic efficacy of lymph node metastasis in clinical application.

Prognostic subtypes of thyroid cancer was constructed based on single cell and bulk-RNA sequencing data and verified its authenticity

There has been an increase in the mortality rate of thyroid cancer (THCA), which is the most common endocrine malignancy. We identified six distinct cell types in the THAC microenvironment by analyzing single-cell RNA sequencing (Sc-RNAseq) data from 23 THCA tumor samples, indicating high intratumoral heterogeneity. Through re-dimensional clustering of immune subset cells, myeloid cells, cancer-associated fibroblasts, and thyroid cell subsets, we deeply reveal differences in the tumor microenvironment of thyroid cancer. Through an in-depth analysis of thyroid cell subsets, we identified the process of thyroid cell deterioration (normal, intermediate, malignant cells). Through cell-to-cell communication analysis, we found a strong link between thyroid cells and fibroblasts and B cells in the MIF signaling pathway. In addition, we found a strong correlation between thyroid cells and B cells, TampNK cells, and bone marrow cells. Finally, we developed a prognostic model based on differentially expressed genes in thyroid cells from single-cell analysis. Both in the training set and the testing set, it can effectively predict the survival of thyroid patients. In addition, we identified significant differences in the composition of immune cell subsets between high-risk and low-risk patients, which may be responsible for their different prognosis. Through in vitro experiments, we identify that knockdown of NPC2 can significantly promote thyroid cancer cell apoptosis, and NPC2 may be a potential therapeutic target for thyroid cancer. In this study, we developed a well-performing prognostic model based on Sc-RNAseq data, revealing the cellular microenvironment and tumor heterogeneity of thyroid cancer. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.

Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment

Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.

The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives

As an emerging sequencing technology, single-cell RNA sequencing (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and cell heterogeneity by achieving high-throughput and multidimensional analysis of individual cells and circumventing the shortcomings of traditional sequencing for detecting the average transcript level of cell populations. It has been applied to life science and medicine research fields such as tracking dynamic cell differentiation, revealing sensitive effector cells, and key molecular events of diseases. This review focuses on the recent technological innovations in scRNA-Seq, highlighting the latest research results with scRNA-Seq as the core technology in frontier research areas such as embryology, histology, oncology, and immunology. In addition, this review outlines the prospects for its innovative application in traditional Chinese medicine (TCM) research and discusses the key issues currently being addressed by scRNA-Seq and its great potential for exploring disease diagnostic targets and uncovering drug therapeutic targets in combination with multiomics technologies.

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.

Construction of a B cell-related gene pairs signature for predicting prognosis and immunotherapeutic response in non-small cell lung cancer

BACKGROUND

Accumulating evidence indicates that the B cells play important roles in anti-tumor immunity and shaping tumor development. This study aimed to explore the expression profiles of B cell marker genes and construct a B cell-related gene pairs (BRGPs) signature associated with the prognosis and immunotherapeutic efficiency in non-small cell lung cancer (NSCLC) patients.

METHODS

B cell-related marker genes in NSCLC were identified using single-cell RNA sequencing data. TCGA and GEO datasets were utilized to identify the prognostic BRGPs based on a novel algorithm of cyclically single pairing along with a 0-or-1 matrix. BRGPs signature was then constructed using Lasso-Cox regression model. Its prognostic value, associated immunogenomic features, putative molecular mechanism and predictive ability to immunotherapy were investigated in NSCLC patients.

RESULTS

The BRGPs signature was composed of 23 BRGPs including 28 distinct B cell-related genes. This predictive signature demonstrated remarkable power in distinguishing good or poor prognosis and can serve as an independent prognostic factor for NSCLC patients in both training and validation cohorts. Furthermore, BRGPs signature was significantly associated with immune scores, tumor purity, clinicopathological characteristics and various tumor-infiltrating immune cells. Besides, we demonstrated that the tumor mutational burden scores and TIDE scores were positively correlated with the risk score of the model implying immune checkpoint blockade therapy may be more effective in NSCLC patients with high-risk scores.

CONCLUSIONS

This novel BRGPs signature can be used to assess the prognosis of NSCLC patients and may be useful in guiding immune checkpoint inhibitor treatment in our clinical practice.

Integrated analysis of bulk and single-cell RNA-seq reveals the role of MYC signaling in lung adenocarcinoma

MYC is one of the well-known oncogenes, and its important role in cancer still remains largely unknown. We obtained lung adenocarcinoma (LUAD) multi-omics data including genome, transcriptome, and single-cell sequencing data from multiple cohorts. We calculated the GSVA score of the MYC target v1 using the ssGSEA method, and obtained the genes highly correlated with this score by Spearman correlation analysis. Subsequent hierarchical clustering divided these genes into two gene sets highly associated with MYC signaling (S1 and S2). Unsupervised clustering based on these genes divided the LUAD samples into two distinct subgroups, namely, the MYC signaling inhibition group (C1) and activation group (C2). The MCP counter package in R was used to assess tumor immune cell infiltration abundance and ssGSEA was used to calculate gene set scores. The scRNA-seq was used to verify the association of MYC signaling to cell differentiation. We observed significant differences in prognosis, clinical characteristics, immune microenvironment, and genomic alterations between MYC signaling inhibition and MYC signaling activation groups. MYC-signaling is associated with genomic instability and can mediate the immunosuppressive microenvironment and promote cell proliferation, tumor stemness. Moreover, MYC-signaling activation is also subject to complex post-transcriptional regulation and is highly associated with cell differentiation. In conclusion, MYC signaling is closely related to the genomic instability, genetic alteration and regulation, the immune microenvironment landscape, cell differentiation, and disease survival in LUAD. The findings of this study provide a valuable reference to revealing the mechanism of cancer-promoting action of MYC in LUAD.

T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer

The immune system is a dynamic feature of each individual and a footprint of our unique internal and external exposures. Indeed, the type and level of exposure to physical and biological agents shape the development and behavior of this complex and diffuse system. Many pathological conditions depend on how our immune system responds or does not respond to a pathogen or a disease or on how the regulation of immunity is altered by the disease itself. T-cells are important players in adaptive immunity and, together with B-cells, define specificity and monitor the internal and external signals that our organism perceives through its specific receptors, TCRs and BCRs, respectively. Today, high-throughput sequencing (HTS) applied to the TCR repertoire has opened a window of opportunity to disclose T-cell repertoire development and behavior down to the clonal level. Although TCR repertoire sequencing is easily accessible today, it is important to deeply understand the available technologies for choosing the best fit for the specific experimental needs and questions. Here, we provide an updated overview of TCR repertoire sequencing strategies, providers and applications to infectious diseases and cancer to guide researchers’ choice through the multitude of available options. The possibility of extending the TCR repertoire to HLA characterization will be of pivotal importance in the near future to understand how specific HLA genes shape T-cell responses in different pathological contexts and will add a level of comprehension that was unthinkable just a few years ago.

Cell-by-Cell: Unlocking Lung Cancer Pathogenesis

For lung cancers, cellular trajectories and fates are strongly pruned by cell intrinsic and extrinsic factors. Over the past couple of decades, the combination of comprehensive molecular and genomic approaches, as well as the use of relevant pre-clinical models, enhanced micro-dissection techniques, profiling of rare preneoplastic lesions and surrounding tissues, as well as multi-region tumor sequencing, have all provided in-depth insights into the early biology and evolution of lung cancers. The advent of single-cell sequencing technologies has revolutionized our ability to interrogate these same models, tissues, and cohorts at an unprecedented resolution. Single-cell tracking of lung cancer pathogenesis is now transforming our understanding of the roles and consequences of epithelial-microenvironmental cues and crosstalk during disease evolution. By focusing on non-small lung cancers, specifically lung adenocarcinoma subtype, this review aims to summarize our knowledge base of tumor cells-of-origin and tumor-immune dynamics that have been primarily fueled by single-cell analysis of lung adenocarcinoma specimens at various stages of disease pathogenesis and of relevant animal models. The review will provide an overview of how recent reports are rewriting the mechanistic details of lineage plasticity and intra-tumor heterogeneity at a magnified scale thanks to single-cell studies of early- to late-stage lung adenocarcinomas. Future advances in single-cell technologies, coupled with analysis of minute amounts of rare clinical tissues and novel animal models, are anticipated to help transform our understanding of how diverse micro-events elicit macro-scale consequences, and thus to significantly advance how basic genomic and molecular knowledge of lung cancer evolution can be translated into successful targets for early detection and prevention of this lethal disease.