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Development of a Cancer-Associated Fibroblast-Related Prognostic Model in Breast Cancer via Bulk and Single-Cell RNA Sequencing.
BACKGROUND: The most numerous cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a crucial role in cancer development. Our objective was to develop a cancer-associated fibroblast breast cancer predictive model. METHODS: We acquire breast cancer (BC) scRNA-seq data from Gene Expression Omnibus (GEO), and "Seurat" was used for data processing, including quality control, filtering, principal component analysis, and t-SNE. Afterward, "singleR" software was used to annotate cells. Seurat's "FindAllMarkers" program is used to locate particular CAF markers. clusterProfiler was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The Cancer Genome Atlas (TCGA) database was utilized to provide univariate Cox regression, least absolute shrinkage operator (LASSO) analysis using bulk RNA-seq data. For model development, multivariate Cox regression studies are used. Utilizing pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms, chemosensitivity and immunotherapy response were predicted. The "rms" software was used to facilitate and simplify modeling. RESULTS: Integrating the scRNA-seq (GSE176078) dataset yielded 28 cell clusters. In addition, well-known cell types helped identify 12 cell types. We found 193 marker genes that are elevated in CAFs. In addition, a five-gene predictive model associated to CAF was created in the training set. In the training set, the validation set, and the external validation set, greater risk scores were associated with a worse prognosis. And individuals with a higher risk score were more susceptible to immunotherapy and conventional chemotherapy medicines. CONCLUSION: In conclusion, we establish a strong prognostic model comprised of 5 genes related with CAF that might serve as a potent prognostic indicator and aid clinicians in making more rational medication choices.
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Targeting galactose metabolic reprogramming overcomes immunotherapy resistance in KRAS-mutant lung adenocarcinoma: Integrative multi-omics and machine learning approaches.
BACKGROUND: KRAS-mutant lung adenocarcinoma (LUAD) is associated with aggressive phenotypes and therapy resistance, which highlights an urgent need to identify reliable biomarkers and therapeutic targets. This study aims to investigate the role of galactose metabolism (GM) reprogramming in shaping the tumor microenvironment (TME) and driving immunotherapy resistance in KRAS-mutant LUAD. METHODS: Bulk-tissue transcriptomics data were collected from public cohorts such as Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Single-cell RNA-sequencing (scRNA-seq) data, mutational profiles, and advanced bioinformatic approaches were integrated to analyze the correlation between mutation status and metabolic heterogeneity in LUAD. Analytical methods included dimensionality reduction, differential expression, intercellular communication, trajectory inference, gene co-expression network analysis, molecular subtyping, and machine learning-based prognostic modeling. Animal experiment and flow cytometry were performed to validate KDM5A inhibitor (CPI-455) can sensitize KRAS-mutant LUAD cells to immunotherapy. RESULTS: Single-cell analysis revealed reduced immune infiltration and upregulated GM in KRAS-mutant epithelial cells. Two GM-based molecular subtypes with distinct mutational profiles and immune patterns were identified. A prognostic model integrating GM-related genes demonstrated superior predictive performance. KDM5A was identified as a key predictor of ICB resistance. Targeting KDM5A can enhance the sensitivity to immunotherapy in KRAS-mutant LUAD. CONCLUSION: Our findings highlight the therapeutic potential of targeting galactose metabolism-related hub genes to sensitize KRAS-mutant LUAD to immunotherapy.
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Pan-Cancer Analysis of Glycolytic and Ketone Bodies Metabolic Genes: Implications for Response to Ketogenic Dietary Therapy.
BACKGROUND: The Warburg effect, also termed "aerobic glycolysis", is one of the most remarkable and ubiquitous metabolic characteristics exhibited by cancer cells, representing a potential vulnerability that might be targeted for tumor therapy. Ketogenic diets (KDs), composed of high-fat, moderate-protein and low carbohydrates, are aimed at targeting the Warburg effect for cancer treatment, which have recently gained considerable attention. However, the efficiency of KDs was inconsistent, and the genotypic contribution is still largely unknown. METHODS: The bulk RNA-seq data from The Cancer Genome Atlas (TCGA), single cell RNA sequencing (scRNA-seq), and microarray data from Gene Expression Omnibus (GEO) and Cancer Cell Line Encyclopedia (CCLE) were collected. A joint analysis of glycolysis and ketone bodies metabolism (KBM) pathway was performed across over 10,000 tumor samples and nearly 1,000 cancer cell lines. A series of bioinformatic approaches were combined to identify a metabolic subtype that may predict the response to ketogenic dietary therapy (KDT). Mouse xenografts were established to validate the predictive utility of our subtypes in response to KDT. RESULTS: We first provided a system-level view of the expression pattern and prognosis of the signature genes from glycolysis and KBM pathway across 33 cancer types. Analysis by joint stratification of glycolysis and KBM revealed four metabolic subtypes, which correlated extensively but diversely with clinical outcomes across cancers. The glycolytic subtypes may be driven by TP53 mutations, whereas the KB-metabolic subtypes may be mediated by CTNNB1 (β-catenin) mutations. The glycolytic subtypes may have a better response to KDs compared to the other three subtypes. We preliminarily confirmed the idea by literature review and further performed a proof-of-concept experiment to validate the predictive value of the metabolic subtype in liver cancer xenografts. CONCLUSIONS: Our findings identified a metabolic subtype based on glycolysis and KBM that may serve as a promising biomarker to predict the clinical outcomes and therapeutic responses to KDT.
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Adult Human, but Not Rodent, Spermatogonial Stem Cells Retain States with a Foetal-like Signature.
Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling, it has been proposed that undifferentiated human spermatogonia can be subclassified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc) RNA-sequencing libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat, and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate 'state 0B' with a suppressive transcriptomic programme that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells-which are mitotically arrested-can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mice (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive lifespan while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.
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uniLIVER: a human liver cell atlas for data-driven cellular state mapping.
The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://lifeome.net/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of "variant" cellular states analogous to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the "deviated" states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte "intermediate" states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues.
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Mitoribosomal defects aggravate liver cancer via aberrant glycolytic flux and T cell exhaustion.
BACKGROUND: Mitochondria are involved in cancer energy metabolism, although the mechanisms underlying the involvement of mitoribosomal dysfunction in hepatocellular carcinoma (HCC) remain poorly understood. Here, we investigated the effects of mitoribosomal impairment-mediated alterations on the immunometabolic characteristics of liver cancer. METHODS: We used a mouse model of HCC, liver tissues from patients with HCC, and datasets from The Cancer Genome Atlas (TCGA) to elucidate the relationship between mitoribosomal proteins (MRPs) and HCC. In a mouse model, we selectively disrupted expression of the mitochondrial ribosomal protein CR6-interacting factor 1 (CRIF1) in hepatocytes to determine the impact of hepatocyte-specific impairment of mitoribosomal function on liver cancer progression. The metabolism and immunophenotype of liver cancer was assessed by glucose flux assays and flow cytometry, respectively. RESULTS: Single-cell RNA-seq analysis of tumor tissue and TCGA HCC transcriptome analysis identified mitochondrial defects associated with high-MRP expression and poor survival outcomes. In the mouse model, hepatocyte-specific disruption of the mitochondrial ribosomal protein CRIF1 revealed the impact of mitoribosomal dysfunction on liver cancer progression. Crif1 deficiency promoted programmed cell death protein 1 expression by immune cells in the hepatic tumor microenvironment. A [U-13C6]-glucose tracer demonstrated enhanced glucose entry into the tricarboxylic acid cycle and lactate production in mice with mitoribosomal defects during cancer progression. Mice with hepatic mitoribosomal defects also exhibited enhanced progression of liver cancer accompanied by highly exhausted tumor-infiltrating T cells. Crif1 deficiency induced an environment unfavorable to T cells, leading to exhaustion of T cells via elevation of reactive oxygen species and lactate production. CONCLUSIONS: Hepatic mitoribosomal defects promote glucose partitioning toward glycolytic flux and lactate synthesis, leading to T cell exhaustion and cancer progression. Overall, the results suggest a distinct role for mitoribosomes in regulating the immunometabolic microenvironment during HCC progression.
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Different Glucose Metabolic Features According to Cancer and Immune Cells in the Tumor Microenvironment.
BACKGROUND: A close metabolic interaction between cancer and immune cells in the tumor microenvironment (TME) plays a pivotal role in cancer immunity. Herein, we have comprehensively investigated the glucose metabolic features of the TME at the single-cell level to discover feasible metabolic targets for the tumor immune status. METHODS: We examined expression levels of glucose transporters (GLUTs) in various cancer types using The Cancer Genome Atlas (TCGA) data and single-cell RNA-seq (scRNA-seq) datasets of human cancer tissues including melanoma, head and neck, and breast cancer. In addition, scRNA-seq data of immune cells in the TME acquired from human melanoma after immune checkpoint inhibitors were analyzed to investigate the dynamics of glucose metabolic profiles of specific immune cells. RESULTS: Pan-cancer bulk RNA-seq showed that the GLUT3-to-GLUT1 ratio was positively associated with immune cell enrichment score. The scRNA-seq datasets of various human cancer tissues showed that GLUT1 was highly expressed in cancer cells, while GLUT3 was highly expressed in immune cells in TME. The scRNA-seq data obtained from human melanoma tissues pre- and post-immunotherapy showed that glucose metabolism features of myeloid cells, particularly including GLUTs expression, markedly differed according to treatment response. CONCLUSIONS: Differently expressed GLUTs in TME suggest that GLUT could be a good candidate a surrogate of tumor immune metabolic profiles and a target for adjunctive treatments for immunotherapy.
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Comparative transcriptomic insights into the evolution of vertebrate photoreceptor types.
To explore the molecular similarities and potential evolutionary origins of vertebrate photoreceptor types, we analyzed single-cell and -nucleus transcriptomic atlases from six vertebrate species: zebrafish, chicken, lizard, opossum, ground squirrel, and human. Comparative analyses identified conserved transcriptional signatures for the five ancestral photoreceptor types: red, blue, green, and UV cones, as well as rods. We further identified and validated molecular markers of the principal and accessory members of the tetrapod double cone. Comparative transcriptomics suggests that the principal member originated from ancestral red cones, although the origin of the accessory member is less clear. The gene expression variation among cone types mirrors their spectral order (red → green → blue → UV). We find that rods are highly dissimilar to all cone types, suggesting that rods may have diverged prior to the spectral diversification of cones.
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A single cell gene expression atlas of 28 human livers.
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Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets.
Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We reveal key cell type-specific transcription factors and major gene-regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrates that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a is associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease implicates critical cell types, developmental stages, genes, and regulatory mechanisms. The single cell multi-omics atlas reveals key chromatin remodeling events and gene expression dynamics associated with kidney development.