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Spatially distinct epithelial and mesenchymal cell subsets along progressive lineage restriction in the branching embryonic mammary gland.
How cells coordinate morphogenetic cues and fate specification during development remains a fundamental question in organogenesis. The mammary gland arises from multipotent stem cells (MaSCs), which are progressively replaced by unipotent progenitors by birth. However, the lack of specific markers for early fate specification has prevented the delineation of the features and spatial localization of MaSC-derived lineage-committed progenitors. Here, using single-cell RNA sequencing from E13.5 to birth, we produced an atlas of matched mouse mammary epithelium and mesenchyme and reconstructed the differentiation trajectories of MaSCs toward basal and luminal fate. We show that murine MaSCs exhibit lineage commitment just prior to the first sprouting events of mammary branching morphogenesis at E15.5. We identify early molecular markers for committed and multipotent MaSCs and define their spatial distribution within the developing tissue. Furthermore, we show that the mammary embryonic mesenchyme is composed of two spatially restricted cell populations, and that dermal mesenchyme-produced FGF10 is essential for embryonic mammary branching morphogenesis. Altogether, our data elucidate the spatiotemporal signals underlying lineage specification of multipotent MaSCs, and uncover the signals from mesenchymal cells that guide mammary branching morphogenesis.
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Disulfidptosis-related genes of prognostic signature and immune infiltration features in hepatocellular carcinoma supported by bulk and single-cell RNA sequencing data.
BACKGROUND: Disulfidptosis is a new type of cellular death triggered in response to disulfide stress and is strongly linked to the progression of malignancies. Hepatocellular carcinoma (HCC) is a very common malignancy. Some reports have suggested a link between disulfidptosis-related genes (DRGs) and cancer; however, further research needs to be conducted. METHODS: In this study, HCC data from the Cancer Genome Atlas-Liver Hepatocellular Carcinoma and Gene Expression Omnibus data sets were collected and analyzed. A univariate Cox regression analysis, least absolute shrinkage and selection operator, and multivariate Cox regression analysis were conducted to identify the hub DRGs signature for prognosis. The HCC patients were allocated to high- and low-risk groups based on their disulfidptosis risk scores. The model was validated with a high degree of precision using both internal and external validation data sets. "ESTIMATE" and "CIBERSORT" packages were employed to assess the immunological landscapes and immune cell infiltration. The IMvigor210 cohort was chosen to validate the immunotherapy results. A drug sensitivity analysis was conducted to identify targeted medications. The expression of the hub DRGs in the HCC cells was confirmed using cytological techniques. RESULTS: The bioinformatic analysis revealed that 16 genes showed differential expression. A prognostic model was developed based on four genes: RPN1, SLC2A1, SLC2A4, and SLC7A11. A notable difference in prognosis was observed between the two risk groups. Based on the results of the immune microenvironment, tumor mutation burden, immunotherapy, and drug screening analyses, the DRGs signature can be employed in HCC immunotherapy decision making. Further, the expression levels of the hub DRGs were significantly upregulated in the HCC cells. CONCLUSIONS: Our four-DRGs signature could be used to predict HCC prognosis. Further, this study showed that the hub DRGs could serve as biomarkers for immunotherapy prediction and could potentially guide targeted therapies.
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LRMP Associates With Immune Infiltrates and Acts as a Prognostic Biomarker in Lung Adenocarcinoma.
Background: Lymphoid-restricted membrane protein (LRMP) is an endoplasmic reticulum-associated protein that is expressed in a developmentally regulated manner in both B and T cell lineages. However, the role of LRMP in the growth, prognosis and immune infiltration in lung adenocarcinoma (LUAD) remains unclear. Method: The expression levels of LRMP mRNA in tumor and normal tissues were analyzed using Tumor Immune Estimation Resource 2.0 (TIMER 2.0) and Gene Expression Profiling Interactive Analysis 2 (GEPIA 2). LRMP protein expression was examined using the Human Protein Atlas. In vitro experiments, including qRT-PCR Western blot and immunohistochemistry staining were also performed to investigate LRMP expression. GEPIA2 and Kaplan-Meier plotter databases were used to analyze the clinical prognostic significance of LRMP. To further confirm the underlying function of LRMP, the data were analyzed using gene set enrichment analysis. Moreover, we also constructed plasmids to overexpress LRMP and explored the effect of LRMP in A549 cell line. Additionally, Tumor Immune single-cell Hub was used to investigate the distribution of LRMP in the LUAD immune microenvironment; TIMER and CIBERSORT were used to investigate the relationships among LRMP, LRMP co-expressed genes, and tumor-infiltrating immune cells; Finally, the correlations between LRMP and immune checkpoints were analyzed using TIMER 2.0. Results: The expression of LRMP was significantly lower in LUAD tissues and cell lines. High LRMP expression is associated with a better prognosis in patients with LUAD. In vitro experimental studies demonstrated that overexpression of LRMP could decrease the proliferation, migration and invasion in A549 cells, and downregulated multiple oncogenic signaling pathways, including p-STAT3, p-PI3K-p-AKT, p-MEK and EMT pathways. GSEA results showed that immuno-related and cell adhesion pathways were enriched in samples with high LRMP expression. LRMP and its co-expressed genes were positively correlated with various tumor-infiltrating immune cells and their markers. Additionally, LRMP positively correlated with immune checkpoints. Conclusions: Our data suggest that LRMP may act as a tumor suppressor gene and indicates a better prognosis. Moreover, LRMP is associated with immune infiltrates which may be involved in immunotherapy response in LUAD. Further studies are needed to validate these findings.
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S100A14 as a Potential Biomarker of the Colorectal Serrated Neoplasia Pathway.
Accounting for 15-30% of colorectal cancer cases, the serrated pathway remains poorly characterized compared to the adenoma-carcinoma sequence. It involves sessile serrated lesions as precursors and is characterized by BRAF mutations (BRAFV600E), CpG island hypermethylation, and microsatellite instability (MSI). Using label-free proteomics, we compared normal tissue margins from patients with diverticular disease, sessile serrated lesions, low-grade adenomas, and high-grade adenomas. We identified S100A14 as significantly overexpressed in sessile serrated lesions compared to low-grade adenomas, high-grade adenomas, and normal tissues. This overexpression was confirmed by immunohistochemical scoring in an independent cohort. Gene expression analyses of public datasets showed higher S100A14 expression in BRAFV600E-mutated and MSI-H colorectal cancers compared to microsatellite stable BRAFwt tumors. This finding was confirmed by immunohistochemical scoring in an independent colorectal cancer cohort. Furthermore, single-cell RNA sequencing analysis from the Human Colon Cancer Atlas revealed that S100A14 expression in tumor cells positively correlated with the abundance of tumoral CD8+ cytotoxic T cells, particularly the CD8+ CXCL13+ subset, known for its association with a favorable response to immunotherapy. Collectively, our results demonstrate for the first time that S100A14 is a potential biomarker of serrated neoplasia and further suggests its potential role in predicting immunotherapy responses in colorectal cancer.
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IdentifiHR predicts homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression.
BACKGROUND: Approximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in homologous recombination (HR) DNA repair. While there are genomic and transcriptomic methods, developed for other cancers, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR status in HGSC specifically. We have built a HGSC-specific model to predict HR status using gene expression. METHODS: We separated The Cancer Genome Atlas (TCGA) cohort of HGSCs into training (n = 288) and testing (n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were used to train a penalised logistic regression model. RESULTS: IdentifiHR uses the expression of 209 genes to predict HR status in HGSC. These genes preserve the genomic damage signal, capturing known regions of HR-specific copy number alteration which impact gene expression. IdentifiHR is 85% accurate in the TCGA test set and 86% accurate in an independent cohort of 99 samples, taken from primary tumours, ascites and normal fallopian tubes. Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD. CONCLUSIONS: IdentifiHR is an accurate model to predict HR status in HGSC. It is available as an open source R package, empowering researchers to robustly classify HR status when only transcriptomic sequencing data is available.
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Deciphering lactate metabolism in colorectal cancer: Prognostic modeling, immune infiltration, and gene mutation insights.
BACKGROUND: Colorectal cancer (CRC) remains a major global health burden due to its high incidence and mortality, with treatment efficacy often hindered by tumor heterogeneity, drug resistance, and a complex tumor microenvironment (TME). Lactate metabolism plays a pivotal role in reshaping the TME, promoting immune evasion and epithelial-mesenchymal transition, making it a promising target for novel therapeutic strategies and prognostic modeling in CRC. AIM: To offer an in-depth analysis of the role of lactate metabolism in CRC, highlighting its significance in the TME and therapeutic response. METHODS: Utilizing single-cell and transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas, we identified key lactate metabolic activities, particularly in the monocyte/macrophage subpopulation. RESULTS: Seven lactate metabolism-associated genes were significantly linked to CRC prognosis and used to construct a predictive model. This model accurately forecasts patient outcomes and reveals notable distinct patterns of immune infiltration and transcriptomic profiles mutation profiles between high- and low-risk groups. High-risk patients demonstrated elevated immune cell infiltration, increased mutation frequencies, and heightened sensitivity to specific drugs (AZD6482, tozasertib, and SB216763), providing a foundation for personalized treatment approaches. Additionally, a nomogram integrating clinical and metabolic data effectively predicted 1-, 3-, and 5-year survival rates. CONCLUSION: This report underscored the pivotal mechanism of lactate metabolism in CRC prognosis and suggest novel avenues for therapeutic intervention.
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Prognostic utility of TME-associated genes in pancreatic cancer.
Background: Pancreatic cancer (PC) is a deadly disease. The tumor microenvironment (TME) participates in PC oncogenesis. This study focuses on the assessment of the prognostic and treatment utility of TME-associated genes in PC. Methods: After obtaining the differentially expressed TME-related genes, univariate and multivariate Cox analyses and least absolute shrinkage and selection operator (LASSO) were performed to identify genes related to prognosis, and a risk model was established to evaluate risk scores, based on The Cancer Genome Atlas (TCGA) data set, and it was validated by external data sets from the Gene Expression Omnibus (GEO) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Multiomics analyses were adopted to explore the potential mechanisms, discover novel treatment targets, and assess the sensitivities of immunotherapy and chemotherapy. Results: Five TME-associated genes, namely, FERMT1, CARD9, IL20RB, MET, and MMP3, were identified and a risk score formula constructed. Next, their mRNA expressions were verified in cancer and normal pancreatic cells. Multiple algorithms confirmed that the risk model displayed a reliable ability of prognosis prediction and was an independent prognostic factor, indicating that high-risk patients had poor outcomes. Immunocyte infiltration, gene set enrichment analysis (GSEA), and single-cell analysis all showed a strong relationship between immune mechanism and low-risk samples. The risk score could predict the sensitivity of immunotherapy and some chemotherapy regimens, which included oxaliplatin and irinotecan. Various latent treatment targets (LAG3, TIGIT, and ARID1A) were addressed by mutation landscape based on the risk model. Conclusion: The risk model based on TME-related genes can reflect the prognosis of PC patients and functions as a novel set of biomarkers for PC therapy.
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Effect of immune infiltration intensity on the efficacy of neoadjuvant immunotherapy for esophageal cancer.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) treatment often involves neoadjuvant therapy combining chemotherapy and immune checkpoint inhibitors. However, the effectiveness of these treatments is limited by immune infiltration in the tumor microenvironment. METHODS: We analyzed single-cell transcriptomic data from 22 patients with resectable ESCC, collected before and after neoadjuvant therapy. Differences in gene expression between patients achieving a complete pathological response (pCR) and those who did not were assessed. We further validated our findings using RNAseq data from The Cancer Genome Atlas (TCGA), and conducted quantitative qRT-PCR and Western blot analyses on tumor tissues from a clinical cohort. RESULTS: Significant differences in gene expression related to T cell activation, natural killer cell activity, and cytokine signaling were observed between pCR and non-pCR patients. Notable genes included CXCL10, CXCL11, ME1, MT1X, FAT1, OAS2, and MT2A. TCGA data confirmed a correlation between high gene expression and increased tumor mutational burden as well as improved survival rates, particularly for CXCL10. qRT-PCR revealed significant upregulation of CXCL10, CXCL11, ME1, MT1X, FAT1, OAS2, and MT2A in tumor tissues compared to normal tissues. Western blot analysis showed increased protein levels of CXCL10, CXCL11, OAS2, MT1E, and MT1X, while FAT1 was downregulated. CONCLUSION: Our study highlights the critical role of immune infiltration and associated molecular pathways in the efficacy of neoadjuvant immunotherapy for ESCC. Specific genes, such as CXCL10, are promising as predictive markers for treatment response and survival.
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Integrating multi-omics data revealing LIMK1 as a metastatic biomarker in LUAD patients with brain metastasis.
BACKGROUND: Lung adenocarcinoma (LUAD) is a major subtype of lung cancer that frequently develops brain metastases. Tumor heterogeneity substantially complicates disease management, and reliable biomarkers for metastatic progression remain lacking. LIM domain kinase 1 (LIMK1), a key regulator of cytoskeletal dynamics, has been implicated in tumor progression; however, its role in regulating brain metastasis in LUAD remains unclear. METHODS: Single-cell RNA sequencing data from 11 primary LUAD tumors and 10 brain metastases were analyzed to identify malignant epithelial subclusters associated with metastatic dissemination. Tumor evolutionary trajectories and gene regulatory networks were explored using pseudotime trajectory analysis and weighted gene co-expression network analysis. LIMK1 expression was validated using The Cancer Genome Atlas dataset, as well as RT–qPCR and Western blotting in LUAD tissues and cell lines. Functional assays, including cell proliferation, colony formation, migration, and invasion, were performed following LIMK1 knockdown in LUAD cell lines. RESULTS: Six malignant epithelial subclusters were identified, among which clusters 0 and 4 were significantly enriched in brain metastases and associated with poor survival outcomes. LIMK1 was identified as a hub gene within these metastasis-associated clusters. LIMK1 expression was markedly upregulated in LUAD tissues and cell lines, and elevated LIMK1 levels predicted an unfavorable prognosis. Functional experiments demonstrated that LIMK1 depletion significantly inhibited tumor cell proliferation, migration, and invasion, while partially reversing epithelial–mesenchymal transition (EMT). Mechanistically, LIMK1 was found to promote LUAD progression through activation of the PI3K/AKT/mTOR signaling pathway. CONCLUSIONS: LIMK1 represents a novel prognostic biomarker and metastasis-associated regulator in LUAD. By driving EMT and activating PI3K/AKT/mTOR signaling, LIMK1 facilitates malignant progression, highlighting its potential as a therapeutic target for LUAD patients with brain metastases.
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Epigenetic regulation during cancer transitions across 11 tumour types.
Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.