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The collagen landscape in cancer: profiling collagens in tumors and in circulation reveals novel markers of cancer-associated fibroblast subtypes.
Cancer-associated fibroblasts (CAFs) deposit and remodel collagens in the tumor stroma, impacting cancer progression and efficacy of interventions. CAFs are the focus of new therapeutics with the aim of normalizing the tumor microenvironment. To do this, a better understanding of CAF heterogeneity and collagen composition in cancer is needed. In this study, we sought to profile the expression of collagens at multiple levels with the goal of identifying cancer biomarkers. We investigated the collagen expression pattern in various cell types and CAF subtypes in a publicly available single-cell RNA sequencing (RNA-seq) dataset of pancreatic ductal adenocarcinoma. Next, we investigated the collagen expression profile in tumor samples across cancer types from The Cancer Genome Atlas (TCGA) database and evaluated if specific patterns of collagen expression were associated with prognosis. Finally, we profiled circulating collagen peptides using a panel of immunoassays to measure collagen fragments in the serum of cancer patients. We found that pancreatic stellate cells and fibroblasts were the primary producers of collagens in the pancreas. COL1A1, COL3A1, COL5A1, COL6A1 were expressed in all CAF subtypes, whereas COL8A1, COL10A1, COL11A1, COL12A1 were specific to myofibroblast CAFs (myCAF) and COL14A1 specific to inflammatory CAFs (iCAF). In TCGA database, myCAF collagens COL10A1 and COL11A1 were elevated across solid tumor types, and multiple associations between high expression and worse survival were found. Finally, circulating collagen biomarkers were elevated in the serum of patients with cancer relative to healthy controls with COL11A1 (myCAF) having the best diagnostic accuracy of the markers measured. In conclusion, CAFs express a noncanonical collagen profile with specific collagen subtypes associated with iCAFs and myCAFs in PDAC. These collagens are deregulated at the cellular, tumor, and systemic levels across different solid tumors and associate with survival. These findings could lead to new discoveries such as novel biomarkers and therapeutic targets. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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The pan-cancer landscape of crosstalk between leukocyte transendothelial migration-related genes and tumor microenvironment relevant to prognosis and immunotherapy response.
BACKGROUND: Leukocyte transendothelial migration-related genes (LTEMGs) play a crucial role in the immune response and have been extensively studied in various pathological conditions, including inflammation, infection, and cancer. In recent years, increasing attention has been given to understanding the biological mechanisms of LTEMGs in the context of tumor progression and metastasis. The potential function of LTEMGs in cancer progression remains unclear. The aim of this study is to systematically delineate the relationship between LTEMGs and tumor prognosis and immune microenvironment at the pan-cancer level, providing new biomarkers for personalized immunotherapy. METHODS: The gene alteration, messenger RNA (mRNA) expression, and prognostic value of LTEMGs in pan-cancer were evaluated using Bulk and single-cell RNA (scRNA) sequence data. The LTEMGs score was calculated by R package "GSVA". The association of LTEMGs score with tumor microenvironment and immunotherapy response were deeply explored. RESULTS: We assessed the mRNA expression of 114 LTEMGs across various cancers, finding significant upregulation in acute myeloid leukemia (LAML) and pancreatic adenocarcinoma (PAAD). Prognostic analysis indicated most LTEMGs were risk factors in low-grade glioma (LGG), PAAD, uveal melanoma (UVM), and LAML. The LTEMGs score, highest in kidney renal clear cell carcinoma (KIRC) and lowest in UVM, was higher in tumor tissues compared to normal tissues in several cancers. The score was a risk factor for overall survival (OS) in LGG, UVM, and others, but protective in KIRC and some others. LTEMGs score correlated positively with Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling, apoptosis, and immune responses. It also correlated with immune and stromal scores, and immune-related pathways. Higher LTEMGs score was linked to greater immune cell infiltration and poorer immunotherapy outcomes. Single-cell analysis revealed higher LTEMGs score in endothelial and monocyte cells, consistent with reduced immunotherapy responsiveness. CONCLUSIONS: Our results reveal that LTEMGs are closely associated with tumor microenvironment. Patients with high LTEMGs score might be resistant to immunotherapy.
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A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons.
The human basal ganglia (BG), subcortical nuclei fundamental to motor regulation and cognitive modulation, is constructed from neurons produced during gestation in the adjacent ganglionic eminences (GEs). GEs are transient structures in the ventral prenatal brain that also generate GABAergic inhibitory neurons which migrate to destinations in the BG, cortex and other destinations. This study aims to elucidate the epigenomic and 3D-genomic dynamics involved in the specification and maturation of GEs and GE-derived neurons, using single-nucleus methyl-3C sequencing (snm3C-seq), highly-multiplexed spatial transcriptomics, and chromatin+RNA single-molecule imaging. Our multi-modal data support a heterogeneous temporal progression across GE subregions, with the lateral GE (LGE) showing declining neurogenic activity in mid-gestation and caudal GE (CGE) exhibiting ongoing developmental progression through infancy. We identified regulatory programs that specify subtypes of BG principal cells, medium spiny neurons (MSN), via synchronized maturation of the 3D-epigenome. In infant brains, we found a transient short-range enriched (SE) chromatin conformation during the transition between oligodendrocyte progenitors (OPCs) and oligodendrocytes (ODCs), and a temporary shift toward Long-range Enriched (LE) chromatin conformation in projection neurons, extending previous works showing the differentiation of neurons and glial cells is associated with permanent SE and LE conformation, respectively. Lastly, we found that gene regulatory regions active in MSNs were enriched in loci associated with genetic risk for neuropsychiatric disease. Our study delineates the highly complex, lineage-specific 3D genomic dynamics in ventral progenitors and basal ganglia populations of the perinatal human brain.
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Genetic association of tertiary lymphoid structure-related gene signatures with HCC based on Mendelian randomization and machine learning and construction of prognosis model.
BACKGROUND: Tertiary lymphoid structures (TLS) are formed in numerous cancer types. However, their value and significance in hepatocellular carcinoma (HCC) is unclear. METHODS: We performed differential genes expression analysis of TLS-related Genes (TLSG) based on The Cancer Genome Atlas (TCGA) database, and performed Mendelian randomization (MR) analysis using expression quantitative trait loci, and then took their intersecting genes. A TLSG prognostic signature (TLSGPS)-based risk score was constructed using Least Absolute Shrinkage and Selection Operator (LASSO), univariate and multivariate COX regression analysis, and survival analysis was then performed. We used the International Cancer Genome Consortium for outside validation. We also performed biological function, tumor mutational burden, immune infiltration, single-cell analysis, CeRNA and drug sensitivity analysis based on TLSGPS. RESULTS: Three TLSGs (HM13, CSTB, CDCA7L) were identified to construct the TLSGPS, which showed good predictive ability and outperformed most prognostic signatures. MR suggested that HM13 (OR = 0.9997, 95 %CI: 0.9994-0.9999, P = 0.014) and CSTB (OR = 0.9997, 95 %CI: 0.9995-0.9999, P = 0.048) were negatively correlated with the risk of HCC onset, while CDCA7L (OR = 1.0004, 1.0001-1.0007, P = 0.0161) was the opposite. The differences in biological functions between the TLSGPS-based high-risk group (HRG) and low-risk group (LRG) involved cell proliferation, differentiation, and drug metabolism. HRG plus high mutations exhibited extremely poor survival. HRG had higher abundance of immune cell-oncogenic phenotypes, higher immune escape ability, and greater sensitivity to Afatinib, Dasatinib, and Gefitinib. CONCLUSION: 3 TLSGs identified by machine learning and MR can predict the onset, prognosis and clinical treatment of HCC patients, and had significant genetic association with HCC.
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Multiomics Dissection of Early-Onset Colorectal Cancer (EOCRC) Reveals an Immunosuppressive Microenvironment and Identifies FAT3 Mutation as a Biomarker of Hot-Like Immune Features.
Early-onset colorectal cancer (EOCRC) exhibits distinct clinical and biological features compared with late-onset CRC (LOCRC), yet its genomic architecture and immune microenvironmental determinants remain incompletely characterized. Here, we integrated multiomics discovery analyses of 69 EOCRC and 461 LOCRC patients from the TCGA cohort, complemented by independent validation, single-cell RNA sequencing, spatial transcriptomics, and systems-level network analysis. We identified 10 genes significantly enriched for mutations in EOCRC, including ANXA7, ZNF267, and POLQ, which displayed distinct domain-specific clustering and were validated in an external cohort. Mutational signature decomposition showed that EOCRC was associated with an SBS87-like signature, whereas LOCRC was dominated by SBS1; because TCGA lacks detailed treatment history, this SBS87-like inference should not be interpreted as direct evidence of prior thiopurine exposure. Immunologically, EOCRC displayed an immunosuppressive microenvironment characterized by elevated regulatory T cells, resting NK cells, and tighter immune-stromal coupling, as further supported by spatial transcriptomic analysis. Single-cell atlas analysis identified M2-polarized, SPP1-expressing macrophage subsets in EOCRC. Weighted gene coexpression network analysis identified a mitochondrial-ribosomal biogenesis module as the transcriptional hallmark of EOCRC. Finally, random forest analysis identified FAT3 mutations as a genomic correlate of a hot-like, immune-activated phenotype in EOCRC. Integrative single-cell analysis localized FAT3 predominantly to cancer-associated fibroblasts, especially myofibroblasts, and an in silico FAT3 perturbation analysis suggested potential downstream programs involving extracellular matrix remodeling, inflammatory signaling, chemokine response, and mitotic spindle regulation. These findings delineate age-stratified genomic and microenvironmental features of EOCRC and generate testable hypotheses for future functional and clinical validation.
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Identification of SGMS2 as a molecule involved in natural killer cell recruitment and its in-deep analysis in the liver cancer microenvironment: Evidence from large populations cohort.
BACKGROUND: Liver cancer, a common malignancy within the digestive system, presents with a particularly grim prognosis. Within the immune microenvironment, the role of natural killer (NK) cells in liver cancer remains unclear. METHODS: We sourced data on clinical parameters and gene expressions for liver cancer patients from The Cancer Genome Atlas Program database and carried out all analyses using R software and its relevant codes. RESULTS: In our research, we delved into the genes intertwined with NK cells in hepatocellular carcinoma (HCC). Leveraging the QUANTISEQ and MCPCOUNTER algorithms to quantify NK cells, we spotlighted genes vital to the recruitment of NK cells. Among these genes, GDE1, WDFY3, DNAJB14, PKD2, DGAT2, SGMS2 and MKNK2 showed a strong correlation with patient outcomes. We also mapped out the single-cell expression trajectories of these genes within the HCC milieu. From our findings, SGMS2 emerged as a key gene warranting further scrutiny. Our in-depth analysis of SGMS2 shed light on its influence over specific biological pathways, its contribution to the immune landscape and its role in genomic instability within HCC. Drawing from this, we formulated a predictive model rooted in SGMS2-associated genes. This model showcased remarkable precision across both training and validation cohorts. CONCLUSIONS: Overall, our investigation underscored the profound implications of SGMS2, a gene pivotal to NK cell infiltration, in the landscape of HCC, thereby positioning it as a potential linchpin in oncological strategies.
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The breast cancer coagulome in the tumor microenvironment and its role in prognosis and treatment response to chemotherapy.
BACKGROUND: The procoagulant phenotype in cancer is linked to thrombosis, cancer progression, and immune response. A novel treatment that reduces the risk of both thrombosis and cancer progression without excess bleeding risk remains to be identified. OBJECTIVES: Here, we aimed to broadly investigate the breast tumor coagulome and its relation to prognosis, treatment response to chemotherapy, and the tumor microenvironment. METHODS: Key coagulation-related genes (n = 35) were studied in a Norwegian cohort with tumor (n = 134) and normal (n = 189) tissue and in the Cancer Genome Atlas (n = 1052) data set. We performed gene set variation analysis in the Norwegian cohort, and in the Cancer Genome Atlas cohort, associations with the tumor microenvironment and prognosis were evaluated. Analyses were performed with cBioPortal, Estimation of Stromal and Immune cells in Malignant Tumors Using Expression Data, Tumor Immune Estimation Resource, the integrated repository portal for tumor-immune system interactions, Tumor Immune Single-cell Hub 2, and the receiver operating characteristic plotter. Six independent breast cancer cohorts were used to study the tumor coagulome and treatment response to chemotherapy. RESULTS: Twenty-two differentially expressed coagulation-related genes were identified in breast tumors. Several coagulome factors were correlated with tumor microenvironment characteristics and were expressed by nonmalignant cells in the tumor microenvironment. PLAT and F8 were independent predictors of better overall survival and progression-free survival, respectively. F12 and PLAU were predictors of worse progression-free survival. The PROCR-THBD-PLAT signature showed a promising predictive value (area under the curve, 0.75; 95% CI, 0.69-0.81; P = 3.6 × 10-17) for combination chemotherapy with fluorouracil, epirubicin, and cyclophosphamide. CONCLUSION: The breast tumor coagulome showed potential in prediction of prognosis and chemotherapy response. Cells within the tumor microenvironment are sources of coagulome factors and may serve as therapeutic targets of coagulation factors.
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Machine learning-derived natural killer cell signature predicts prognosis and therapeutic response in clear cell renal cell carcinoma.
BACKGROUND: Natural killer cells, interconnected with patient prognosis and treatment response, play a pivotal role in the tumor immune microenvironment and may serve as potential novel predictive biomarkers for renal cell carcinoma. METHODS: Clear cell renal cell carcinoma transcriptome data and the corresponding clinical data were obtained from the Cancer Genome Atlas (TCGA) database. Single-cell sequencing data were sourced from the Gene Expression Omnibus (GEO) database. A risk model was established by integrating ten different machine learning algorithms, which resulted in 101 combined models. The model with the highest average C-index was selected for further analysis, and was assessed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan-Meier survival analysis. The differences in immune infiltration fractions, clinicopathological features, and response to various targeted therapies and immunotherapy between high- and low-risk groups were investigated. Furthermore, qRT-PCR, IHC, colony formation test, CCK8 assay and flow cytometry were conducted to explore the expression pattern and function of ARHGAP9 in our own patient samples and renal cancer cell lines. RESULTS: Totally, 156 NK cell-related genes and 5189 prognosis-related genes were identified, and 36 genes of their intersection demonstrated prognostic value. A risk model with 18 genes was established by Coxboost plus plsRcox, which can accurately predict the prognosis of ccRCC patients. Significant correlations were determined between risk score and tumor malignancy and immune cell infiltration. Meanwhile, a combination of tumor mutation burden plus risk score could have higher accuracy of predicting clinical outcomes. Moreover, high-risk group patients were more likely to be responsive to targeted therapy but show no response to immunotherapy. CONCLUSIONS: Intricate signaling interactions between NK cells and various cellular subgroups were depicted and the developmental trajectory of NK cells was elucidated. A NK cells-related risk model was established, which can provide reliable prognostic information and identified patients with more probability of benefiting from therapy.
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Crosstalk Between Gastric Cancer and Adjacent Mucosa Reveals EDN1-EDNRA-Mediated Regulation of Cancer Stemness and Immunomodulation Networks.
Within the complex system of gastric cancer, the tumour microenvironment mediates a complex network of cellular interactions, yet its tissue-specific intracellular communication patterns have remained poorly understood. Leveraging cutting-edge single-cell RNA sequencing, we investigated two independent research studies (GSE183904 and GSE184198), creating an unprecedented map of cellular crosstalk across gastric cancer tissues, their adjacent normal tissue and gastric mucosa (GM). Our systematic analysis revealed two distinct patterns: 7557 distinct interactions from normal tissue to tumour cells, while gastric mucosa engaged in 7320 unique interactions with malignant conditions. Within this cellular network, the endothelin pathway emerged as a key regulator, specifically increased in gastric mucosa-to-tumour interaction. The Cancer Genome Atlas data demonstrated that patients harbouring elevated EDNRA expression faced significantly poorer outcomes. EDNRA, previously underexplored in this context, showed remarkable upregulation across diverse gastric cancer cell lines. Through experimental validation, we demonstrated that EDNRA overexpression, when stimulated by endothelin-1, dramatically accelerated the proliferation of human gastric epithelial GSE-1 cells. Conversely, pharmacological inhibition of EDNRA using ABT-627 suppressed both NCI-N87 and MKN-28 gastric cancer cells proliferation. Further mechanistic investigation revealed the molecular mechanism of ABT-627: simultaneously triggering both extrinsic and intrinsic apoptotic cascades. TISIDB analysis revealed significant positive correlations between EDNRA and multiple immunostimulators, suggesting the role of EDNRA in immunomodulation networks. These findings reveal a previously unidentified connection between gastric mucosa and tumour progression, positioning EDNRA not only as a molecular target, but also as a critical mediator of tissue-specific cancer communication. In conclusion, EDNRA functions as both a regulatory factor and therapeutic target, offering a promising therapeutic avenue for gastric cancer intervention.
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Lactate metabolism-related genes serve as potential biomarkers for predicting gastric cancer progression and immunotherapy.
BACKGROUND: Targeting lactate metabolism represents a promising therapeutic strategy to enhance anti-tumor immune responses. In this study, we developed a novel model based on lactate metabolism-related genes (LRGs) to predict survival, characterize the immune microenvironment, and assess immunotherapy response in gastric cancer (GC), with the potential to identify new biomarkers. METHODS: Data sets of GC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. LRGs were sourced from the MSigDB database. Five key prognostic LRGs (MMP11, MMP12, HBB, VSIG2, and SERPINE1) were identified using univariate COX regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Patients were classified into high-risk and low-risk groups based on a median risk score. We conducted prognostic analysis, gene set enrichment analysis (GSEA), immune microenvironment analysis, immunotherapy responsiveness evaluation, and drug screening in these groups. RESULTS: The high-risk group exhibited poorer prognosis compared to the low-risk group, as predicted by our nomogram for overall survival. Notably, the high-risk group, marked by higher stromal cell infiltration and RNA stemness scores (RNAss), showed increased susceptibility to immune evasion. In contrast, the low-risk group demonstrated better responses to immunotherapy and greater sensitivity to chemotherapy. Single-cell analysis revealed that SERPINE1 is predominantly positively correlated with immune checkpoint expression, while VSIG2 exhibits a negative correlation. CONCLUSIONS: We have developed and validated a novel lactate metabolism-associated model, providing new insights into the prognosis and immunotherapy of GC patients. The five identified LRGs offer potential as prognostic biomarkers and therapeutic targets in GC.