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Identification of SAA1 as a novel metastasis marker in ovarian cancer and development of a graphene-based detection platform for early assessment.
BACKGROUND: Ovarian cancer (OC) is a prevalent gynecological malignancy with the highest mortality rate, which generally diagnosed at late stages due to the lack of effective early screening methods and the nonspecific symptoms. Hence, here we aim to identify new metastasis markers and develop a novel detection method with the characteristics of high sensitivity, rapid detection, high specificity, and low cost when compared with other conventional detection technologies. METHODS: Blood from OC patients with or without metastasis were collected and analyzed by 4D Label free LC - MS/MS. Surgically resect samples from OC patients were collected for Single cell RNA sequencing (sc-RNA seq). Short hairpin RNA (shRNA) was used to silence SAA1 expression in SKOV3 and ID8 to verify the relationship between endogenous SAA1 and tumor invasion or metastasis. The functional graphene chips prepared by covalent binding were used for SAA1 detection. RESULTS: In our study, we identified Serum Amyloid A1 (SAA1) as a hematological marker of OC metastasis by comprehensive analysis of proteins in plasma from OC patients with or without metastasis using 4D Label free LC - MS/MS and gene expression patterns from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Further validation using tumor tissues and plasma from human OC and mouse OC model confirmed the correlation between SAA1 and tumor metastasis. Importantly, sc-RNA seq of human OC samples revealed that SAA1 was specifically expressed in tumor cells and upregulated in the metastasis group. The functional role of SAA1 in metastasis was demonstrated through experiments in vitro and in vivo. Based on these findings, we designed and investigated a graphene-based platform for SAA1 detection to predict the risk of metastasis of OC patients. CONCLUSION: Our study suggests that SAA1 is a biomarker of OC metastasis, and we have developed a rapid and highly sensitive platform using graphene chips to detection of plasma SAA1 for the early assessment of metastasis in OC patients.
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Reciprocal interactions between malignant cells and macrophages enhance cancer stemness and M2 polarization in HBV-associated hepatocellular carcinoma.
Background: The tumor microenvironment of cancers has emerged as a crucial component in regulating cancer stemness and plays a pivotal role in cell-cell communication. However, the specific mechanisms underlying these phenomena remain poorly understood. Methods: We performed the single-cell RNA sequencing (scRNA-seq) on nine HBV-associated hepatocellular carcinoma (HCC) patients. The heterogeneity of the malignant cells in pathway functions, transcription factors (TFs) regulation, overall survival, stemness, as well as ligand-receptor-based intercellular communication with macrophages were characterized. The aggressive and stemness feature for the target tumor subclone was validated by the conduction of in vitro assays including sphere formation, proliferation, Annexin V apoptosis, flow cytometry, siRNA library screening assays, and multiple in vivo preclinical mouse models including mouse hepatoma cell and human HCC cell xenograft models with subcutaneous or orthotopic injection. Results: Our analysis yielded a comprehensive atlas of 31,664 cells, revealing a diverse array of malignant cell subpopulations. Notably, we identified a stemness-related subclone of HCC cells with concurrent upregulation of CD24, CD47, and ICAM1 expression that correlated with poorer overall survival. Functional characterization both in vitro and in vivo validated S100A11 as one of the top downstream mediators for tumor initiation and stemness maintenance of this subclone. Further investigation of cell-cell communication within the tumor microenvironment revealed a propensity for bi-directional crosstalk between this stemness-related subclone and tumor-associated macrophages (TAMs). Co-culture study showed that this interaction resulted in the maintenance of the expression of cancer stem cell markers and driving M2-like TAM polarization towards a pro-tumorigenic niche. We also consolidated an inverse relationship between the proportions of TAMs and tumor-infiltrating T cells. Conclusions: Our study highlighted the critical role of stemness-related cancer cell populations in driving an immunosuppressive tumor microenvironment and identified the S100A11 gene as a key mediator for stemness maintenance in HCC. Moreover, our study provides support that the maintenance of cancer stemness is more attributed to M2 polarization than the recruitment of the TAMs.
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Integrating single-cell and bulk RNA sequencing data unveils antigen presentation and process-related CAFS and establishes a predictive signature in prostate cancer.
BACKGROUND: Cancer-associated fibroblasts (CAFs) are heterogeneous and can influence the progression of prostate cancer in multiple ways; however, their capacity to present and process antigens in PRAD has not been investigated. In this study, antigen presentation and process-related CAFs (APPCAFs) were identified using bioinformatics, and the clinical implications of APPCAF-related signatures in PRAD were investigated. METHODS: SMART technology was used to sequence the transcriptome of primary CAFs isolated from patients undergoing different treatments. Differential expression gene (DEG) screening was conducted. A CD4 + T-cell early activation assay was used to assess the activation degree of CD4 + T cells. The datasets of PRAD were obtained from The Cancer Genome Atlas (TCGA) database and NCBI Gene Expression Omnibus (GEO), and the list of 431 antigen presentation and process-related genes was obtained from the InnateDB database. Subsequently, APP-related CAFs were identified by nonnegative matrix factorization (NMF) based on a single-cell seq (scRNA) matrix. GSVA functional enrichment analyses were performed to depict the biological functions. A risk signature based on APPCAF-related genes (APPCAFRS) was developed by least absolute shrinkage and selection operator (LASSO) regression analysis, and the independence of the risk score as a prognostic factor was evaluated by univariate and multivariate Cox regression analyses. Furthermore, a biochemical recurrence-free survival (BCRFS)-related nomogram was established, and immune-related characteristics were assessed using the ssGSEA function. The immune treatment response in PRAD was further analyzed by the Tumor Immune Dysfunction and Exclusion (TIDE) tool. The expression levels of hub genes in APPCAFRS were verified in cell models. RESULTS: There were 134 upregulated and 147 downregulated genes, totaling 281 differentially expressed genes among the primary CAFs. The functions and pathways of 147 downregulated DEGs were significantly enriched in antigen processing and presentation processes, MHC class II protein complex and transport vesicle, MHC class II protein complex binding, and intestinal immune network for IgA production. Androgen withdrawal diminished the activation effect of CAFs on T cells. NMF clustering of CAFs was performed by APPRGs, and pseudotime analysis yielded the antigen presentation and process-related CAF subtype CTSK + MRC2 + CAF-C1. CTSK + MRC2 + CAF-C1 cells exhibited ligand‒receptor connections with epithelial cells and T cells. Additionally, we found a strong association between CTSK + MRC2 + CAF-C1 cells and inflammatory CAFs. Through differential gene expression analysis of the CTSK + MRC2 + CAF-C1 and NoneAPP-CAF-C2 subgroups, 55 significant DEGs were identified, namely, APPCAFRGs. Based on the expression profiles of APPCAFRGs, we divided the TCGA-PRAD cohort into two clusters using NMF consistent cluster analysis, with the genetic coefficient serving as the evaluation index. Four APPCAFRGs, THBS2, DPT, COL5A1, and MARCKS, were used to develop a prognostic signature capable of predicting BCR occurrence in PRAD patients. Subsequently, a nomogram with stability and accuracy in predicting BCR was constructed based on Gleason grade (p = n.s.), PSA (p < 0.001), T stage (p < 0.05), and risk score (p < 0.01). The analysis of immune infiltration showed a positive correlation between the abundance of resting memory CD4 + T cells, M1 macrophages, resting dendritic cells, and the risk score. In addition, the mRNA expression levels of THBS2, DPT, COL5A1, and MARCKS in the cell models were consistent with the results of the bioinformatics analysis. CONCLUSIONS: APPCAFRS based on four potential APPCAFRGs was developed, and their interaction with the immune microenvironment may play a crucial role in the progression to castration resistance of PRAD. This novel approach provides valuable insights into the pathogenesis of PRAD and offers unexplored targets for future research.
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Crosstalk Between Cancer-associated Fibroblasts and Myeloid Cells Shapes the Heterogeneous Microenvironment of Gastric Cancer.
BACKGROUND: Targeted therapies have improved the clinical outcomes of most patients with cancer. However, the heterogeneity of gastric cancer remains a major hurdle for precision treatment. Further investigations into tumor microenvironment heterogeneity are required to resolve these problems. METHODS: In this study, bioinformatic analyses, including metabolism analysis, pathway enrichment, differentiation trajectory inference, regulatory network construction, and survival analysis, were applied to gain a comprehensive understanding of tumor microenvironment biology within gastric cancer using single-cell RNA-seq and public datasets and experiments were carried out to confirm the conclusions of these analyses. RESULTS: We profiled heterogeneous single-cell atlases and identified eight cell populations with differential expression patterns. We identified two cancer-associated fibroblasts (CAFs) subtypes, with particular emphasis on the role of inflammatory cancer-associated fibroblasts (iCAFs) in EMT and lipid metabolic crosstalk within the tumor microenvironment. Notably, we detected two differentiation states of iCAFs that existed in different tissues with discrepant expression of genes involved in immuno-inflammation or ECM remodeling. Moreover, investigation of tumor-infiltrating myeloid cells has revealed the functional diversity of myeloid cell lineages in gastric cancer. Of which a proliferative cell lineage named C1QC+MKI67+TAMs was recognized with high immunosuppressive capacities, suggesting it has immune suppression and cell proliferation functions in the tumor niche. Finally, we explored regulatory networks based on ligand-receptor pairs and found crucial pro-tumor crosstalk between CAFs and myeloid cells in the tumor microenvironment (TME). CONCLUSION: These findings provide insights for future cancer treatments and drug discovery.
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A single-cell transcriptomic atlas of human lens epithelium: identification and functional insights into lens stem/progenitor cells.
BACKGROUND: The existence of stem/progenitor cells in the lens epithelium has been demonstrated, but their identification remains challenging. Accurate identification requires advanced technologies and a comprehensive understanding of lens epithelial cell (LEC) subtypes, presenting a significant challenge in age-related cataract research. METHODS: Eight pairs of human donor lens epithelium samples were collected for single-cell RNA sequencing (scRNA-seq). This included four non-aged (< 65 years) and four aged (> 65 years) individuals. Subsequent analyses involved cell (sub)type characterization, trajectory inference, and cell-cell communication. Experimental validation was conducted through transcriptome sequencing and immunofluorescence on human lenses, lens organoids, rabbit regenerated lenses, mouse lenses, and cell lines. RESULTS: Six groups were identified via UMAP mapping of scRNA-seq data: four LECs, one lens fiber cells (LFCs), and one immune cells. One of the four LEC clusters exhibited a distinct gene expression profile and was identified as transient amplifying cells (TACs). TACs specifically express TOP2A and are localized at the lens equator. CytoTRACE analysis to the LEC and LFC data sets provided a differentiation trajectory. The TAC group was determined as stage 2 in the trajectory and LFCs last. The 3 sub groups were labelled early, mid and late LECs and corresponded to stage 1, 3 and 4 in the path. While cell population demographics remained stable with age, transcriptomic changes in LECs were observed, including weaker intercellular crosstalk and adhesion, and fewer TACs in S phase. Lens progenitor-like cells (LPLCs) were identified as a sub-population in early LECs and express ID1. In addition, pleiotrophin (PTN) signaling was prevalent at all differentiation stages, with a notable weakening of PTN signaling in aged LPLCs. CONCLUSIONS: This study identified four subclasses of LECs within the human lens epithelium that follow a progressive staged development pathway from progenitor cells to mature LECs. TOP2A can serve as a biomarker for TAC in the lens, and LPLCs sustain their dedifferentiated state by expressing ID1. The aging process does not appear to alter cell population demographics, but significant alteration in gene expression profile is observed. Moreover, PTN signaling emerges as a crucial factor in lens homeostasis and represents a potential target for cataract drug development.
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Cellular features of localized microenvironments in human meniscal degeneration: a single-cell transcriptomic study.
BACKGROUND: Musculoskeletal tissue degeneration impairs the life quality and function of many people. Meniscus degeneration is a major origin of knee osteoarthritis and a common threat to athletic ability, but its cellular mechanism remains elusive. METHODS: We built a cell atlas of 12 healthy or degenerated human meniscus samples from the inner and outer meniscal zones of 8 patients using scRNA-seq to investigate meniscal microenvironment homeostasis and its changes in the degeneration process and verified findings with immunofluorescent imaging. RESULTS: We identified and localized cell types in inner and outer meniscus and found new chondrocyte subtypes associated with degeneration. The observations suggested understandings on how cellular compositions, functions, and interactions participated in degeneration, and on the possible loop-like interactions among extracellular matrix disassembly, angiogenesis, and inflammation in driving the degeneration. CONCLUSIONS: The study provided a rich resource reflecting variations in the meniscal microenvironment during degeneration and suggested new cell subtypes as potential therapeutic targets. The hypothesized mechanism could also be a general model for other joint degenerations. FUNDING: The National Natural Science Foundation of China (81972123, 82172508, 62050178, 61721003), the National Key Research and Development Program of China (2021YFF1200901), Fundamental Research Funds for the Central Universities (2015SCU04A40); The Innovative Spark Project of Sichuan University (2018SCUH0034); Sichuan Science and Technology Program (2020YFH0075); Chengdu Science and Technology Bureau Project (2019-YF05-00090-SN); 1.3.5 Project for Disciplines of Excellence of West China Hospital Sichuan University (ZYJC21030, ZY2017301); 1.3.5 Project for Disciplines of Excellence - Clinical Research Incubation Project, West China Hospital, Sichuan University (2019HXFH039).
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VAMP5 promotes glioma progression through bioinformatics analysis clinical correlation and functional validation.
BACKGROUND: Glioma is the most common and aggressive primary brain tumor, characterized by high heterogeneity, invasive growth, and poor prognosis. Identifying novel molecular drivers is essential for improving diagnosis, prognosis, and treatment. This study aimed to investigate the expression pattern, clinical significance, and functional role of VAMP5 in glioma. METHODS: This study integrated data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and systematically evaluated VAMP5 expression in glioma, with validation performed via Real-time PCR, immunohistochemistry, and Western blot. Meanwhile, we analysed the prognostic value, clinicopathological features, function enrichment analysis, immune cell infiltration relevance of VAMP5. Subsequently, in vitro and in vivo experiments were conducted to further validate the role and mechanism of VAMP5 in glioma. RESULTS: VAMP5 was significantly upregulated in glioma tissues compared to normal brain tissue across multiple datasets and clinical samples. Single-cell RNA sequencing (scRNA-seq) further confirmed distinct distribution heterogeneity of VAMP5 within glioma cells. A protein-protein interaction (PPI) network was constructed to identify key hub genes, combined with pathway enrichment analysis and immune infiltration profiling to decipher VAMP5-mediated mechanisms in glioma progression. Finally, functional assays in GBM cell lines were conducted to illustrate the molecular mechanisms underlying VAMP5-mediated promotion of GBM progression. In vitro and in vivo experiments further validated that VAMP5 promotes the proliferation and migration of glioma. CONCLUSION: VAMP5 promotes glioma progression and is associated with immune modulation and adverse clinical outcomes. It may serve as a novel biomarker for prognosis and a potential therapeutic target in glioma.
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A single-cell and spatially resolved atlas of human breast cancers.
Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.
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Multi-omics analysis identifies RARRES1 as a potential biomarker linked to immunosuppressive microenvironment and its radiomics prediction in ovarian cancer.
BACKGROUND: Retinoic acid receptor responder 1 (RARRES1) is aberrantly expressed across multiple cancers, its prognostic and immune associations in ovarian cancer (OV) have been increasingly reported, but an integrated multi-omics interpretation across cellular heterogeneity, mutational landscape, and non-invasive radiomics prediction remains insufficiently established. This study aimed to systematically investigate the multi-omics role of RARRES1 in OV and to develop a noninvasive radiomics approach for predicting its expression. METHODS: Bulk RNA sequencing (RNA-seq) data from The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) cohort were used to assess associations between RARRES1, survival, and tumor microenvironment (TME) features. Single-cell RNA sequencing dissected cellular heterogeneity and macrophage subpopulations, and weighted gene co-expression network analysis identified RARRES1-associated gene modules. Whole-exome sequencing profiled tumor mutation burden and landscapes. RARRES1 and key immune markers were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR)/Western blot. A radiomics-based random forest model was constructed to non-invasively predict RARRES1 expression. RESULTS: High RARRES1 expression was associated with poor overall survival, reduced stromal and immune scores, and increased tumor purity. Functional analyses showed downregulation of antigen presentation, chemokine, interferon, and natural killer (NK) cytotoxicity pathways. Single-cell analysis identified macrophage subpopulations, with M2-like tumor-associated macrophages central and enriched in immunosuppressive and inflammatory pathways. High RARRES1 expression exhibited distinct mutational patterns and weighted gene co-expression network analysis (WGCNA) modules correlated with M1 macrophages and immune activation. Compared with the RARRES1-low group, RARRES1-high tumors showed decreased CD8 and increased CD68 and CD163 expression. The radiomics-based random forest model demonstrated good discrimination and calibration in predicting RARRES1 expression, establishing a direct link between CT-derived imaging features and molecular expression states. CONCLUSIONS: This study establishes a radiogenomic framework for non-invasive prediction of RARRES1 expression in OV. While transcriptomic and immunological findings are consistent with existing evidence, the radiomics-based prediction model offers a clinically applicable strategy to infer tumor immune-molecular states from routine imaging.
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Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in patients with LUAD. The tumor microenvironment (TME) of LUAD BrM remains incompletely characterized, and there is an urgent need to identify biologically relevant biomarkers and potential therapeutic vulnerabilities. METHODS: Single-cell transcriptomic data from LUAD BrM samples were processed through dimensionality reduction, clustering, and cell annotation. BrM-associated core genes were identified from BrM scRNA-seq datasets using inferCNV and hdWGCNA. A prognostic model for LUAD was then constructed using 101 combinatorial approaches derived from 10 machine learning algorithms and validated in LUAD cohorts. We further performed supportive analyses including enrichment, pseudotime trajectory, cell-cell communication, immune infiltration, and single-cell-based drug sensitivity prediction to provide additional biological context. Key findings were validated by immunohistochemistry (IHC) in human tissues and by qRT-PCR and IHC in a mouse BrM model. Functional assays were performed in PC9-BrM cells to assess the roles of SEC61G and CFL1 in LUAD BrM. RESULTS: Our study unveiled a comprehensive single-cell atlas of the BrM TME and established a robust 12-gene BrM-informed prognostic signature for LUAD, validated across multiple LUAD survival cohorts. Functional analysis revealed dysregulated cell cycle control in BrM progression. Immune profiling demonstrated significantly reduced CD4+ and CD8+ T-cell infiltration in high-risk subgroups. Cell-cell interaction analysis highlighted the MIF-CD74/CXCR4 and MIF-CD74/CD44 signaling axes as key regulators of TME remodeling. IHC in human tissues and validation using a mouse BrM model supported the biological relevance of SEC61G and CFL1 in LUAD BrM. qRT-PCR further showed elevated expression of SEC61G and CFL1 mRNA in brain-metastatic cells. Knockdown of SEC61G or CFL1 significantly inhibited PC9-BrM cell viability, proliferation, and migration. CONCLUSIONS: This study identified genes associated with LUAD BrM and established a machine-learning-based prognostic signature for LUAD. Further experimental validation supports SEC61G and CFL1 as candidate biomarkers with potential therapeutic relevance.