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Exploring the Molecular Mechanisms and Shared Genetic Characteristics of Monoclonal Gammopathy of Undetermined Significance and Multiple Myeloma.
BACKGROUND: Monoclonal gammopathy of undetermined significance (MGUS) is a precursor to multiple myeloma (MM), but the mechanisms of progression remain unclear. METHODS AND OBJECTIVES: Transcriptomic datasets procured from the Gene Expression Omnibus (GEO) underwent thorough analysis to ascertain disease-related modules using weighted gene co-expression network analysis. A prognostic model (MGUSscore) was constructed via least absolute shrinkage and selection operator (LASSO) regression within the GSE136337 cohort and validated across independent datasets (The Cancer Genome Atlas - multiple myeloma [TCGA-MM], GSE4581, GSE57317). Crucially, the investigation integrated original single-cell ATAC-seq profiling, immune landscape characterization, and pharmacogenomic sensitivity prediction. Protein-level disparities were validated in clinical specimens using immunohistochemistry and multiplex immunofluorescence. RESULTS: DAP3 and UBE2S were identified as central drivers of progression. The MGUSscore effectively stratified patients into risk categories, with high-risk individuals exhibiting significantly inferior survival outcomes (p < 0.001). Notably, the high-risk group was characterized by distinct immune infiltration patterns and predicted responsiveness to specific chemotherapies. Experimental validation confirmed markedly elevated DAP3 and UBE2S protein expression in MM compared to MGUS tissues. CONCLUSION: Collectively, DAP3 and UBE2S may constitute promising therapeutic targets for MM intervention, meriting additional investigative efforts.
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Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma.
In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion, LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.
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In Silico Multi-omics Analyses of Kinesin Family Member 15 in Pan-Cancer and Validation in Tissue Microarray.
INTRODUCTION: Kinesin family member 15 (KIF15) is a molecular motor protein that participates in bipolar spindle assembly and centrosome separation during metaphase in normal cells. However, accumulating evidence has shown that KIF15 is aberrantly regulated in the state of malignancy and is associated with the progression of cancers. MATERIALS AND METHODS: We analyzed the mRNA expression, protein expression, single-cell gene expression, prognoses, and immune reactivities of KIF15 using the data from various sources, including the Cancer Cell Line Encyclopedia, The Cancer Genome Atlas database, the Clinical Proteomic Tumor Analysis Consortium Confirmatory/Discovery database, and Cancer Single-cell Expression Map. Additionally, immunohistochemistry was performed to validate KIF15 protein expression in human tumor samples. RESULTS: The data from the present pan-cancer analysis elucidated that KIF15 expression was increased in more than 25 human tumors. Furthermore, it was found to be highly corre-lated with biological pathways, immune cell infiltration, and poor prognoses across multiple cancers. Additionally, tissue microarray analysis confirmed abundant cytoplasmic KIF15 expression in ovarian serous carcinoma and colorectal adenocarcinoma, with significantly higher levels compared to adjacent normal tissues. DISCUSSION: Consistent patterns of high KIF15 expression were also evident across various cancer cell lines in the CCLE dataset. Collectively, these observations suggest KIF15 is in-timately involved in the progression of multiple human cancers. By analyzing both the pro-teins that interact with KIF15 and the genes that show correlation with its expression, KEGG pathway enrichment indicated that processes such as cell cycle regulation, gap and tight junction dynamics, involvement of microRNAs in cancer, and the p53 signaling pathway may underlie the role of KIF15 in cancer development. CONCLUSION: KIF15 may serve as a potential biomarker and oncogenic factor in multiple cancer types.
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Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder.
Major depressive disorder emerges from the complex interactions of biological systems that span genes and molecules through cells, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to depression requires a multiscale approach, encompassing measures of brain structure and function as well as genetic and cell-specific transcriptional data. Here, we examine anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Brain Genomics Superstruct Project, and Enhancing NeuroImaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene expression, postmortem patient transcriptional data, depression genome-wide association study (GWAS), and single-cell gene transcription. Neuroimaging correlates of depression and negative affect were consistent across three independent datasets. Linking ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes track gene down-regulation in postmortem cortical samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and astrocytes to be consistent cell associates of depression, through both in vivo imaging and ex vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS-derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multiscale approaches, the transcriptional correlates of depression-linked brain function and structure were enriched for disorder-relevant molecular pathways. These findings bridge levels to connect specific genes, cell classes, and biological pathways to in vivo imaging correlates of depression.
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Characterization of the GATA Transcription Factor Family and Exploration of Their Relevance to Immune Infiltration and Tumor Microenvironment in Pancreatic Cancer.
BACKGROUND: Pancreatic cancer (PC) presents a phenomenal disease burden worldwide. The GATA transcription factor family is associated with a variety of human malignancies. However, the relation between GATA family members (GATAs) and PC has not been elucidated. METHODS: This study integrates large-scale bioinformatics database resources to analyze the expression patterns of GATAs in PC patients and explore their underlying function mechanism and relevance to immune infiltration and other different cell types in the tumor microenvironment in pancreatic cancer. First, the expression pattern of GATAs in pancreatic cancer was detected by the Oncomine database and the Gene Expression Profile Interaction Analysis (GEPIA2) database and verified through other datasets in the R2 platform. Then, we used the cBioPortal database and the Human Protein Atlas to assess the correlation between GATAs and clinicopathological features of PC. Then, survival analyses were performed to identify candidate prognostic factors in the GATA family in PC patients. Further, we performed gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network, immune-infiltration correlation analysis, and cell type analysis of the tumor microenvironment at the single-cell level to explain the function of GATAs in pancreatic cancer. RESULTS: We found that GATA3 and GATA6 were highly expressed in pancreatic cancer, and the expression levels of GATA4 and GATA6 correlated with the pathological stage, differentiation grade, and molecular subtype of pancreatic cancer. The survival analysis revealed that lower GATA4 of PC patients was associated with better outcomes, and higher GATA6 might be associated with longer OS. In addition, GATA3 was associated with immune cell infiltration of PC, and GATA6 was mainly distributed in the epithelial cells with ductal phenotype. CONCLUSION: This work tentatively identified GATA3, GATA4, and GATA6 in the GATA family associated with pancreatic cancer. GATA4 may serve as a prognostic factor for PC patients, and GATA6 may act as a subtype marker for PC. In addition, GATA3 may reflect the immune-infiltration status of PC.
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Establishing an oxidative stress mitochondria-related prognostic model in hepatocellular carcinoma based on multi-omics characteristics and machine learning computational framework.
Hepatocellular carcinoma (HCC) has high incidence and mortality rates worldwide. Damaged mitochondria are characterized by the overproduction of reactive oxygen species (ROS), which can promote cancer development. The prognostic value of the interplay between mitochondrial function and oxidative stress in HCC requires further investigation. Gene expression data of HCC samples were collected from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). We screened prognostic oxidative stress mitochondria-related (OSMT) genes at the bulk transcriptome level. Based on multiple machine learning algorithms, we constructed a consensus oxidative stress mitochondria-related signature (OSMTS), which contained 26 genes. In addition, we identified six of these genes as having a suitable prognostic value for OSMTS to reduce the difficulty of clinical application. Univariate and multivariate analyses verified the OSMTS as an independent prognostic factor for overall survival (OS) in HCC patients. The OSMTS-related nomogram demonstrated to be a powerful tool for the clinical diagnosis of HCC. We observed differences in biological function and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. The highest expression of the OSMTS was detected in hepatocytes at the single-cell transcriptome level. Hepatocytes in the high- and low-risk groups differed significantly in terms of biological function and intercellular communication. Moreover, at the spatial transcriptome level, high expression of OSMTS was mainly in regions enriched in hepatocytes and B cells. Potential drugs targeting specific risk subgroups were identified. Our study revealed that the OSMTS can serve as a promising tool for prognosis prediction and precise intervention in HCC patients.
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Crotonylation and the Risk of Head and Neck Cancer: Insights from a Two-Sample Mendelian Randomization Study.
BACKGROUND: Head and neck cancer (HNC) remains a major global health burden with limited improvement in survival. Crotonylation has been implicated in tumour biology, but its causal contribution to HNC risk is unclear. METHODS: We conducted a two-sample Mendelian randomization (MR) study using expression quantitative trait loci (eQTL)-instrumented expression of crotonylation-related genes. Differential expression was assessed in The Cancer Genome Atlas (TCGA). Mediation MR evaluated 1,400 metabolites and 731 immune-cell traits. Single-cell RNA sequencing (scRNA-seq) mapped cell type - specific expression, and quantitative PCR (qPCR) validated expression in tumour versus normal tissues. RESULTS: MR analysis identified that genetically predicted expression of Glutaryl-CoA Dehydrogenase (GCDH) and Acyl-CoA synthetase, short-chain family member 2 (ACSS2) were causally associated with an increased risk of HNC, and Double PHD fingers 2 (DPF2) and Histone deacetylase 7 (HDAC7) were found to have protective effects against HNC. Moreover, TCGA analysis confirmed a consistent association between GCDH and HNC risk. Mediation MR showed no significant metabolite mediators, although 16α-hydroxy DHEA 3-sulfate levels exhibited a borderline signal. In contrast, two immune-cell traits: CX3CR1 on CD14⁺CD16⁺ monocytes and CD8 on CD39⁺ CD8^br cells partially mediated the effect of GCDH on HNC. scRNA-seq demonstrated lineage-dependent expression of GCDH, and GCDH was enriched in malignant and stromal compartments, with qPCR confirming elevated GCDH expression in tumour samples compared with adjacent normal tissues. CONCLUSION: This study suggests a potential causal association between GCDH expression and increased HNC risk, partially mediated by immune-cell traits, pending further validation via colocalization and functional assays. Our findings highlight GCDH as a promising target for further mechanistic studies and potential therapeutic intervention in HNC. CLINICAL RELEVANCE: The identification of GCDH as a potential risk factor in HNC opens new avenues for targeted therapies, offering potential improvements in patient outcomes through precision medicine approaches.
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Cellular cartography reveals mouse prostate organization and determinants of castration resistance.
Inadequate response to androgen deprivation therapy (ADT) frequently arises in prostate cancer, driven by cellular mechanisms that remain poorly understood. Here, we integrated single-cell RNA sequencing, single-cell multiomics, and spatial transcriptomics to define the transcriptional, epigenetic, and spatial basis of cell identity and castration response in the mouse prostate. Leveraging these data along with a meta-analysis of human prostates and prostate cancer (PCa), we identified cellular orthologs and key determinants of ADT response and resistance. Our findings reveal that mouse prostates harbor lobe-specific luminal epithelial cell types distinguished by unique gene regulatory modules and anatomically defined androgen-responsive transcriptional programs, indicative of divergent developmental origins. Androgen-insensitive, stem-like epithelial populations-resembling human club and hillock cells-are notably enriched in the urethra and ventral prostate but are rare in other lobes. Within the ventral prostate, we also uncovered two additional androgen-responsive luminal epithelial cell types, marked by Pbsn or Spink1 expression, which align with human luminal subsets and may define the origin of distinct PCa subtypes. Castration profoundly reshaped luminal epithelial transcriptomes, with castration-resistant luminal epithelial cells activating stress-responsive and stemness programs. These transcriptional signatures are enriched in tumor cells from ADT-treated and castration-resistant PCa patients, underscoring their likely role in driving treatment resistance. Temporal tracking of cells will precisely map disease-associated cellular transitions, and our technical framework facilitates such interrogations. Collectively, our comprehensive cellular atlas of the mouse prostate illuminates the importance of lobe-specific contexts for PCa modeling and reveals potential therapeutic targets to counter castration resistance.
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MMP1 acts as a potential regulator of tumor progression and dedifferentiation in papillary thyroid cancer.
Papillary thyroid cancer (PTC) is one of the malignancies with an excellent prognosis. However, in PTC, progression or dedifferentiation into poorly differentiated thyroid cancer (PDTC) or anaplastic thyroid cancer (ATC) extremely jeopardizes patients' prognosis. MMP1 is a zinc-dependent endopeptidase, and its role in PTC progression and dedifferentiation is unclear. In this study, transcriptome data of PDTC/ATC and PTC from the Gene Expression Omnibus and The Cancer Genome Atlas databases were utilized to perform an integrated analysis of MMP1 as a potential regulator of tumor progression and dedifferentiation in PTC. Both bulk and single-cell RNA-sequencing data confirmed the high expression of MMP1 in ATC tissues and cells, and further study verified that MMP1 possessed good diagnostic and prognostic value in PTC and PDTC/ATC. Up-regulated MMP1 was found to be positively related to more aggressive clinical characteristics, worse survival, extracellular matrix-related pathways, oncogenic immune microenvironment, more mutations, higher stemness, and more dedifferentiation of PTC. Meanwhile, in vitro experiments verified the high level of MMP1 in PDTC/ATC cell lines, and MMP1 knockdown and its inhibitor triolein could both inhibit the cell viability of PTC and PDTC/ATC. In conclusion, our findings suggest that MMP1 is a potential regulator of tumor progression and dedifferentiation in PTC, and might become a novel therapeutic target for PTC, especially for more aggressive PDTC and ATC.
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Inference of age-associated transcription factor regulatory activity changes in single cells.
Transcription factors (TFs) control cell identity and function. How their activity is altered during healthy aging is critical for an improved understanding of aging and disease risk, yet relatively little is known about such changes at cell-type resolution. Here we present and validate a TF activity estimation method for single cells from the hematopoietic system that is based on TF regulons, and apply it to a mouse single-cell RNA-sequencing atlas, to infer age-associated differentiation activity changes in the immune cells of different organs. This revealed an age-associated signature of macrophage dedifferentiation, which is shared across tissue types, and aggravated in tumor-associated macrophages. By extending the analysis to all major cell types, we reveal cell-type and tissue-type-independent age-associated alterations to regulatory factors controlling antigen processing, inflammation, collagen processing and circadian rhythm, that are implicated in age-related diseases. Finally, our study highlights the limitations of using TF expression to infer age-associated changes, underscoring the need to use regulatory activity inference methods.