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Detecting Prognosis Risk Biomarkers for Colon Cancer Through Multi-Omics-Based Prognostic Analysis and Target Regulation Simulation Modeling.
BACKGROUND: Colon cancer is one of the most common health threats for humans since its high morbidity and mortality. Detecting potential prognosis risk biomarkers (PRBs) is essential for the improvement of therapeutic strategies and drug development. Currently, although an integrated prognostic analysis of multi-omics for colon cancer is insufficient, it has been reported to be valuable for improving PRBs' detection in other cancer types. AIM: This study aims to detect potential PRBs for colon adenocarcinoma (COAD) samples through the cancer genome atlas (TCGA) by integrating muti-omics. MATERIALS AND METHODS: The multi-omics-based prognostic analysis (MPA) model was first constructed to systemically analyze the prognosis of colon cancer based on four-omics data of gene expression, exon expression, DNA methylation and somatic mutations on COAD samples. Then, the essential features related to prognosis were functionally annotated through protein-protein interaction (PPI) network and cancer-related pathways. Moreover, the significance of those essential prognostic features were further confirmed by the target regulation simulation (TRS) model. Finally, an independent testing dataset, as well as the single cell-based expression dataset were utilized to validate the generality and repeatability of PRBs detected in this study. RESULTS: By integrating the result of MPA modeling, as well the PPI network, integrated pathway and TRS modeling, essential features with gene symbols such as EPB41, PSMA1, FGFR3, MRAS, LEP, C7orf46, LOC285000, LBP, ZNF35, SLC30A3, LECT2, RNF7, and DYNC1I1 were identified as PRBs which provide high potential as drug targets for COAD treatment. Validation on the independent testing dataset demonstrated that these PRBs could be applied to distinguish the prognosis of COAD patients. Moreover, the prognosis of patients with different clinical conditions could also be distinguished by the above PRBs. CONCLUSIONS: The MPA and TRS models constructed in this paper, as well as the PPI network and integrated pathway analysis, could not only help detect PRBs as potential therapeutic targets for COAD patients but also make it a paradigm for the prognostic analysis of other cancers.
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Single-cell transcriptomic profiling reveals liver fibrosis in colorectal cancer liver metastasis.
Tumor fibrosis is recognized as a malignant hallmark in various solid tumors; however, the clinical importance and associated molecular characteristics of tumor fibrosis in liver metastases (LM) from colorectal cancer (CRLM) remain poorly understood. Here we show that patients with CRLM whose liver metastases (LM) exhibited tumor fibrosis (Fibrosis+ LM) had significantly worse progression-free survival (P = 0.025) and overall survival (P = 0.008). Single-cell RNA sequencing revealed that the tumor microenvironment of the Fibrosis+ LM was characterized by T cells with an exhausted phenotype, macrophages displaying a profibrotic and suppressive phenotype and fibrosis-promoting fibroblasts. Further investigation highlighted the pivotal role of VCAN_eCAF in remodeling the tumor fibrosis in the tumor microenvironment of Fibrosis+ LM, emphasizing potential targetable interactions such as FGF23 or FGF3-FGFR1. Validation through multiplex immunohistochemistry/immunofluorescence and spatial transcriptomics supported these findings. Here we present a comprehensive single-cell atlas of tumor fibrosis in LM, revealing the intricate multicellular environment and molecular features associated with it. These insights deepen our understanding of tumor fibrosis mechanisms and inform improved clinical diagnosis and treatment strategies.
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A single-cell atlas of adult Drosophila ovary identifies transcriptional programs and somatic cell lineage regulating oogenesis.
Oogenesis is a complex developmental process that involves spatiotemporally regulated coordination between the germline and supporting, somatic cell populations. This process has been modeled extensively using the Drosophila ovary. Although different ovarian cell types have been identified through traditional means, the large-scale expression profiles underlying each cell type remain unknown. Using single-cell RNA sequencing technology, we have built a transcriptomic data set for the adult Drosophila ovary and connected tissues. Using this data set, we identified the transcriptional trajectory of the entire follicle-cell population over the course of their development from stem cells to the oogenesis-to-ovulation transition. We further identify expression patterns during essential developmental events that take place in somatic and germline cell types such as differentiation, cell-cycle switching, migration, symmetry breaking, nurse-cell engulfment, egg-shell formation, and corpus luteum signaling. Extensive experimental validation of unique expression patterns in both ovarian and nearby, nonovarian cells also led to the identification of many new cell type-and stage-specific markers. The inclusion of several nearby tissue types in this data set also led to our identification of functional convergence in expression between distantly related cell types such as the immune-related genes that were similarly expressed in immune cells (hemocytes) and ovarian somatic cells (stretched cells) during their brief phagocytic role in nurse-cell engulfment. Taken together, these findings provide new insight into the temporal regulation of genes in a cell-type specific manner during oogenesis and begin to reveal the relatedness in expression between cell and tissues types.
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High-Throughput Single-Cell Mass Spectrometry Reveals Abnormal Lipid Metabolism in Pancreatic Ductal Adenocarcinoma.
Even populations of clonal cells are heterogeneous, which requires high-throughput analysis methods with single-cell sensitivity. Here, we propose a rapid, label-free single-cell analytical method based on active capillary dielectric barrier discharge ionization mass spectrometry, which can analyze multiple metabolites in single cells at a rate of 38 cells/minute. Multiple cell types (HEK-293T, PANC-1, CFPAC-1, H6c7, HeLa and iBAs) were discriminated successfully. We found evidence for abnormal lipid metabolism in pancreatic cancer cells. We also analyzed gene expression in a cancer genome atlas dataset and found that the mRNA level of a critical enzyme of lipid synthesis (ATP citrate lyase, ACLY) was upregulated in human pancreatic ductal adenocarcinoma (PDAC). Moreover, both an ACLY chemical inhibitor and a siRNA approach targeting ACLY could suppress the viability of PDAC cells. A significant reduction in lipid content in treated cells indicates that ACLY could be a potential target for treating pancreatic cancer.
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Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer.
BACKGROUND: Breast cancer (BRCA) is a significant threat to women's health worldwide, and its progression is closely associated with the tumor microenvironment and gene regulation. Lactylation modification, as a key epigenetic mechanism in cancer biology, has not yet been fully elucidated in the context of BRCA. This study examines the regulatory mechanisms of lactylation-related genes (LRGs), specifically PRDX1, and their prognostic significance in BRCA. METHODS: We integrated data from multiple databases, including Genome-Wide Association Study (GWAS) summary statistics, single-cell RNA sequencing, spatial transcriptomics, and bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using Summary-based Mendelian Randomization (SMR) analysis, we identified LRGs associated with BRCA and comprehensively analysed the expression patterns of PRDX1, cell-cell communication networks, and spatial heterogeneity. Furthermore, we constructed and validated a prognostic model based on the gene expression profile of PRDX1-positive monocytes, evaluating it through Cox regression and LASSO regression analyses. RESULTS: PRDX1 was identified as a key LRG significantly associated with BRCA risk (p_SMR = 0.0026). Single-cell RNA sequencing analysis revealed a significant upregulation of PRDX1 expression in monocytes, with enhanced cell-cell communication between PRDX1-positive monocytes and fibroblasts. Spatial transcriptomics analysis uncovered heterogeneous expression of PRDX1 in the tumor nest regions, highlighting the spatial interaction between PRDX1-positive monocytes and fibroblasts. The prognostic model constructed based on the gene expression profile of PRDX1-positive monocytes demonstrated high accuracy in predicting patient survival in both the training and validation cohorts. High-risk patients exhibited immune-suppressive microenvironment characteristics, including reduced immune cell infiltration and upregulation of immune checkpoint gene expression. CONCLUSION: This study reveals the key role of PRDX1 in BRCA progression, mainly through the regulation of the tumor microenvironment and immune escape mechanisms. The survival prediction model based on PRDX1 shows robust prognostic potential, and future research should focus on integrating PRDX1 with other biomarkers to enhance the precision of personalised medicine.
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Comprehensive characterization of the tumor microenvironment for assessing immunotherapy outcome in patients with head and neck squamous cell carcinoma.
The tumor microenvironment (TME) constitutes a complex milieu of cells and cytokines that maintain equilibrium between tumor progression and prognosis. However, comprehensive analysis of the TME and its clinical significance in head and neck squamous cell carcinoma (HNSCC) remains to be unreported. In this study, based on large-scale RNA sequencing data pertaining to single nucleotide variants (SNVs) and copy number variations (CNVs) in HNSCC patients from The Cancer Genome Atlas database, we analysed subpopulations of infiltrating immune cells and evaluated the role of TME infiltration pattern (TME score) in assessing immunotherapy outcome. TME signature genes involved in several inflammation and immunity signalling pathways were observed in the TME score subtype, which were considered immunosuppressive and potentially responsible for significantly worse prognosis. In comparison with SNV- and CNV-mediated tumor mutation burden, TME score can significantly differentiate between high- and low-risk HNSCC and predict immunotherapy outcome. Our data provide clarity on the comprehensive landscape of interactions between clinical characteristics of HNSCC and tumor-infiltrating immune cells. TME score seems to be a useful biomarker that can predict immunotherapy outcome in HNSCC patients.
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A novel fumaric acid metabolism-related prognostic signature associated with prognosis and immune infiltration landscape in laryngeal squamous cell carcinoma.
BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) is an aggressive malignant tumor, characterized by high incidence and mortality. Metabolic pathways within cancer cells are frequently dysregulated; thus, exploring fumaric acid metabolism-related genes (FAMRGs) appears interesting. We aimed to identify a signature prognostic genetic profile to develop tailored management strategies for patients with LSCC. METHODS: Data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and GeneCards databases were used to identify differentially expressed genes related to fumaric acid (FA) metabolism in LSCC. To explore the underlying mechanisms, we conducted analyses using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, we employed Cox regression and the least absolute shrinkage and selection operator (LASSO) to develop a risk signature based on FAMRGs. This signature was validated in TCGA and GEO cohorts. The association of the risk score with clinical characteristics, microenvironmental characteristics, and drug sensitivity was explored by correlation analyses. Finally, expression of FAMRGs was validated using datasets from the Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (HPA) databases. Moreover, the robustness of our findings was further confirmed through molecular docking and single-cell sequencing. RESULTS: A FA metabolism-associated model for laryngeal cancer was constructed using seven genes (ABCC2, ADH7, AQP9, CXCL11, GPT, PAEP, and PLCG1). Functional analysis suggested that FAMRGs were strongly associated with the chemotaxis and cytokine-cytokine receptor interaction. High-risk score subgroups, as indicated by the Kaplan-Meier curves, demonstrated poorer outcomes in both TCGA and GEO cohorts. A predictive nomogram was developed for LSCC survival probability; FAMRGs were significantly associated with the immune checkpoints. Additionally, six small molecule drugs that appeared promising as therapeutic agents in combating LSCC were identified. Besides, CXCL11 and AQP9 exhibited significantly high expression in tumor tissues, while GPT showed low expression, as confirmed by the HPA and GEPIA databases. Molecular docking confirmed the interaction between the seven core genes and FA. This finding was corroborated by single-cell sequencing, which revealed significant expression differences across various cell clusters in LSCC. CONCLUSIONS: A prognostic model associated with FA metabolism was established for LSCC based on seven genes. This model can effectively predict LSCC prognosis. Additionally, six small molecule drugs with potential therapeutic value for LSCC were identified.
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Pan-cancer analysis suggests that LY6H is a potential biomarker of diagnosis, immunoinfiltration, and prognosis.
LY6H, a member of the lymphocyte antigen-6(LY6) gene family, is located on human chromosomes 6, 8, 11 and 19. This superfamily is characterized by the presence of LU domains. It has demonstrated its emerging significance in various cancers including adenocarcinoma, bladder cancer, ovarian cancer and skin cancer. However, comprehensive pan-cancer analyses have not been conducted to investigate its role in diagnosis, prognosis and immunological prediction. By conducting comprehensive analysis of patient data obtained from publicly available databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), University of Alabama at Birmingham (UALCAN), The Comparative Toxicological Genomics Database (CTD), cBioportal, cancerSEA, and UCSC, we systematically investigated the differential expression of LY6H in 33 different types of human tumors. Additionally, we thoroughly analyzed the diagnostic, prognostic, and immunoinfiltration value of LY6H. Simultaneously, we examined the correlation between LY6H and tumor stemness, methylation patterns, drug sensitivity, gene alterations as well as single cell functions. Furthermore, protein-protein interaction networks and gene-gene interaction networks for LY6H were constructed. Moreover, we also explored the network relationship between LY6H and chemical compounds or genes. The results revealed that LY6H exhibited high expression levels in most cancers which were further validated through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Immunohistochemistry (IHC) analysis using Hepatocellular carcinoma (HCC) samples. Moreover, LY6H displayed early diagnostic potential in 12 tumors while also showing positive or negative correlations with prognosis across different tumor types. Additionally, it was found that LY6H played a pivotal role in regulating immune-infiltrating cells across multiple cancers whereas the correlation between LY6H expression and immune-related genes varied depending on their specific types. Furthermore, the expression of LY6H was significantly associated with DNA methylation patterns in 21 cancers. Therefore, LY6H could serve as an adjunctive biomarker for early tumor detection as well as a prognostic indicator for diverse malignancies.
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Development and validation of a prognostic prediction model for gastric cancer based on lipophagy-related genes.
BACKGROUND: Gastric cancer (GC), as a heterogeneous disease, lacks clear clinical indications, and existing methods based on histological classification are insufficient for individualized stratified treatment of GC patients. Lipophagy-related genes (LRGs) are involved in the progression of GC, but their role in GC remains unclear. This study aims to develop and validate a lipophagy-related prognostic model to improve the predictive ability for GC prognosis. METHODS: GC-related datasets from public databases were used, including The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD, training set), Gene Expression Omnibus 183904 (GSE183904, single-cell dataset), and GSE15459 (external validation set). LRGs were retrieved from the Molecular Signatures Database (MSigDB) using "Lipophagy" as the keyword. Differential expression analysis on TCGA-STAD identified differentially expressed genes (DEGs). Weighted gene co-expression network analysis screened lipophagy-related module genes, overlapped with DEGs for candidate genes. Mendelian randomization (MR) analysis identified key genes with causal relationships to GC. Univariate and multivariate Cox regression analyses screened prognostic genes to construct and validate a risk model. Nomograms for 1-, 3-, and 5-year survival rates were built using significant clinical factors and risk scores. Immune and enrichment analyses compared high- and low-risk groups. Pseudotime processes of prognostic genes in cell clusters and inter-cluster communication were analyzed. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) verified prognostic gene expression. RESULTS: Thirty-five genes had causal relationships with GC; AKAP12, BST1, DCBLD1, PDK4, and SPART were selected as prognostic genes, mainly expressed in adipocytes, dendritic cells, and heart. A risk model and nomogram integrating risk scores and clinical features (age, gender, tumor stage) were constructed. Nomogram predicted values were consistent with observed ones. Survival curves in both sets showed significant differences (P<0.05). Area under the curve (AUC) values: TCGA-STAD (0.61 for 1 year, 0.64 for 3 years, 0.71 for 5 years); GSE15459 (0.67 for 1 year, 0.67 for 3 years, 0.69 for 5 years), indicating limited prognostic discrimination. AKAP12, BST1, DCBLD1, and PDK4 correlated positively with most immune cells. All 5 genes showed differential expression in fibroblasts between GC and normal samples and in pseudotime stages. Endothelial-intestinal epithelial cell interaction was enhanced in GC. RT-qPCR showed AKAP12, BST1, and DCBLD1 were up-regulated in GC samples. CONCLUSIONS: In this study, a risk model was constructed based on five prognostic genes (AKAP12, BST1, DCBLD1, PDK4, and SPART). A nomogram was established by integrating each patient's risk score and clinical features (age, gender, and tumor stage). This model exhibits preliminary prognostic discrimination ability in both the training set and the validation set; however, its predictive accuracy is limited and requires further optimization.
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Differentiation Stage Predicts Radiosensitivity in Mesenchymal-Like Pancreatic Cancer.
PURPOSE: To derive a genomic classifier to predict radiosensitivity of pancreatic cancer cell lines and patients with pancreatic cancer to allow genomic-guided radiation therapy. METHODS AND MATERIALS: We collected a comprehensive data set of full clonogenic cell survival curves of 45 pancreatic cancer cell lines irradiated with clinical photon and proton beams. We derived classifiers based on data from human embryonic and fetal pancreas single-cell RNA-sequencing to distinguish between epithelial and mesenchymal cells and to predict pancreas cell-line differentiation stage. Independent testing was done with an embryonic mouse pancreas single-cell RNA-sequencing data set. We then used bulk RNA-seq profiles from the Cancer Cell Line Encyclopedia to classify our pancreatic cancer cell lines using our epithelial-mesenchymal and differentiation stage classifiers. We then correlated the differentiation stage classifier with the radiosensitivity of the pancreatic cancer cell lines as well as with pancreatic cancer patient data from The Cancer Genome Atlas. RESULTS: We found wide variability in radiosensitivity to both photons and protons among pancreatic cancer cell lines. We showed that the differentiation stage is predictive of radiosensitivity of mesenchymal pancreatic cancer cell lines but not epithelial pancreatic cancer cell lines. We found that chromatin compaction is associated with the differentiation stage and showed that the less differentiated mesenchymal pancreatic cancer cell lines tend to be radioresistant and with more compact chromatin than the radiosensitive differentiated cell lines. Patients with more differentiated tumors exhibit better overall survival. CONCLUSIONS: We found that mesenchymal-like undifferentiated pancreatic cancer cell lines are more radioresistant than mesenchymal-like differentiated ones and that patients with pancreatic cancer with mesenchymal-like undifferentiated tumors treated with radiation therapy tend to have lower overall survival compared with patients with mesenchymal-like differentiated tumors. We show that it is feasibility to use the differentiation stage of mesenchymal pancreatic cancer cells to predict tumor specific radiosensitivity.