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Cancer-associated fibroblast-derived fibulin-5 promotes epithelial-mesenchymal transition in diffuse-type gastric cancer via cAMP response element-binding protein pathway, showing poor prognosis.
Diffuse-type gastric cancer (DGC), characterized by poorly cohesive cells within fibrotic stroma, is associated with advanced disease and poor prognosis. Here, to identify distinct biomarkers for DGC compared with intestinal-type gastric cancer, we constructed a comprehensive large-scale signaling network using RNA-sequencing data from three genomic databases (The Cancer Genome Atlas, GSE62254 and GSE26253), developed a mathematical model and conducted simulation analyses. For validation, we used tissue microarray blocks of gastric cancers with immunohistochemical staining, single-cell RNA sequencing, primary cultures of cancer-associated fibroblasts (CAFs) and organoids, and a co-culture system involving CAFs and cancer cells. Signaling network analysis identified six differentially activated signaling components across the database, including BIRC5, TTK, NEK2, FHL1, NR2F1 and FBLN5. Among the differentially activated signaling components, high tumoral expression of fibulin-5 protein encoded by FBLN5 correlated with poor overall and disease-specific survival rates in patients with DGC, even after adjusting for the tumor, node, metastases (TNM) stage. Fibulin-5, derived from CAFs within DGC stroma, promoted organoid growth and epithelial-mesenchymal transition (EMT) in DGC cell lines via the cAMP response element-binding protein (CREB) pathway in a CAF co-culture system. FBLN5 knockdown in CAFs reduced the aggressive phenotype of co-cultured DGC cells, while CREB inhibitors reversed EMT. Furthermore, levels of secreted FBLN5 in patient blood samples correlated with its expression in primary tumors. In summary, fibulin-5 secreted by CAFs and interacted with DGC cells promotes EMT and is clinically associated with poor patient outcomes. These findings suggest fibulin-5 as a potential prognostic marker and therapeutic target in patients with DGC.
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Prostate epithelial genes define therapy-relevant prostate cancer molecular subtype.
BACKGROUND AND OBJECTIVES: Transcriptomic landscape of prostate cancer (PCa) shows multidimensional variability, potentially arising from the cell-of-origin, reflected in serum markers, and most importantly related to drug sensitivities. For example, Aggressive Variant Prostate Cancer (AVPC) presents low PSA per tumor burden, and characterized by de novo resistance to androgen receptor signaling inhibitors (ARIs). Understanding PCa transcriptomic complexity can provide biological insight and therapeutic guidance. However, unsupervised clustering analysis is hindered by potential confounding factors such as stromal contamination and stress-related material degradation. MATERIALS AND METHODS: To focus on prostate epithelial cell-relevant heterogeneity, we defined 1,629 genes expressed by prostate epithelial cells by analyzing publicly available bulk and single- cell RNA sequencing data. Consensus clustering and CIBERSORT deconvolution were used for class discovery and proportion estimate analysis. The Cancer Genome Atlas Prostate Adenocarcinoma dataset served as a training set. The resulting clusters were analyzed in association with clinical, pathologic, and genomic characteristics and impact on survival. Serum markers PSA and PAP was analyzed to predict response to docetaxel chemotherapy in metastatic setting. RESULTS: We identified two luminal subtypes and two aggressive variant subtypes of PCa: luminal A (Adipogenic/AR-active/PSA-high) (30.0%); luminal S (Secretory/PAP-high) (26.0%); AVPC-I (Immune-infiltrative) (14.7%), AVPC-M (Myc-active) (4.2%), and mixed (25.0%). AVPC-I and AVPC-M subtypes predicted to be resistant to ARI and have low PSA per tumor burden. Luminal A and AVPC-M predicted to be resistant to docetaxel and have high PSA/PAP Ratio. Metastatic PCa patients with high PSA/PAP ratio (>20) had significantly shorter progression-free survival than those with low ratio (≤20) following docetaxel chemotherapy. CONCLUSION: We propose four prostate adenocarcinoma subtypes with distinct transcriptomic, genomic, and pathologic characteristics. PSA/PAP ratio in advanced cancer may aid in determining which patients would benefit from maximized androgen receptor inhibition or early use of antimicrotubule agents.
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Supervised Learning and Multi-Omics Integration Reveals Clinical Significance of Inner Membrane Mitochondrial Protein (IMMT) in Prognostic Prediction, Tumor Immune Microenvironment and Precision Medicine for Kidney Renal Clear Cell Carcinoma.
Kidney renal clear cell carcinoma (KIRC) accounts for approximately 75% of all renal cancers. The prognosis for patients with metastatic KIRC is poor, with less than 10% surviving five years after diagnosis. Inner membrane mitochondrial protein (IMMT) plays a crucial role in shaping the inner mitochondrial membrane (IMM), regulation of metabolism and innate immunity. However, the clinical relevance of IMMT in KIRC is not yet fully understood, and its role in shaping the tumor immune microenvironment (TIME) remains unclear. This study aimed to investigate the clinical significance of IMMT in KIRC using a combination of supervised learning and multi-omics integration. The supervised learning principle was applied to analyze a TCGA dataset, which was downloaded and split into training and test datasets. The training dataset was used to train the prediction model, while the test and the entire TCGA dataset were used to evaluate its performance. Based on the risk score, the cutoff between the low and high IMMT group was set at median value. A Kaplan-Meier curve, receiver operating characteristic (ROC) curve, principal component analysis (PCA) and Spearman's correlation were conducted to evaluate the prediction ability of the model. Gene Set Enrichment Analysis (GSEA) was used to investigate the critical biological pathways. Immunogenicity, immunological landscape and single-cell analysis were performed to examine the TIME. Databases including Gene Expression Omnibus (GEO), Human Protein Atlas (HPA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were employed for inter-database verification. Pharmacogenetic prediction was analyzed via single-guide RNA (sgRNA)-based drug sensitivity screening using Q-omics v.1.30. Low expressions of IMMT in tumor predicted dismal prognosis in KIRC patients and correlated with KIRC progression. GSEA revealed that low expressions of IMMT were implicated in mitochondrial inhibition and angiogenetic activation. In addition, low IMMT expressions had associations with reduced immunogenicity and an immunosuppressive TIME. Inter-database verification corroborated the correlation between low IMMT expressions, KIRC tumors and the immunosuppressive TIME. Pharmacogenetic prediction identified lestaurtinib as a potent drug for KIRC in the context of low IMMT expressions. This study highlights the potential of IMMT as a novel biomarker, prognostic predictor and pharmacogenetic predictor to inform the development of more personalized and effective cancer treatments. Additionally, it provides important insights into the role of IMMT in the mechanism underlying mitochondrial activity and angiogenesis development in KIRC, which suggests IMMT as a promising target for the development of new therapies.
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HMBOX1 reverses autophagy mediated 5-fluorouracil resistance through promoting HACE1-induced ubiquitination and degradation of ATG5 in colorectal cancer.
Chemotherapy remains the primary treatment for unresectable or advanced postoperative colorectal cancers. However, its effectiveness is compromised by chemoresistance, which adversely affects patient outcomes. Dysregulated macroautophagy/autophagy is a proposed mechanism behind this resistance, with ubiquitination playing a key regulatory role. In this study, we identify the transcription factor HMBOX1 (homeobox containing 1) as a critical regulator of chemoresistance in colorectal cancer. RNA sequencing revealed that HMBOX1 is downregulated in drug-resistant colorectal cancer cells and tissues, with its low expression linked to poor prognosis. An integrated analysis of genes associated with autophagy and 5-fluorouracil (5-FU) resistance was conducted, verified in the colorectal cancer tissues of patients by single-cell RNA sequencing and immunostaining. Mass-spectrometry-based proteomics and RNA sequencing were used to elucidate the underlying molecular mechanisms. Functionally, upregulation of HMBOX1 enhances the sensitivity of colorectal cancer cells to the first-line treatment with 5-FU by inhibiting autophagy. Mechanistically, HMBOX1 promotes the transcription of the E3 ubiquitin ligase HACE1, which in turn enhances ATG5 K63-ubiquitination and subsequent proteasome-mediated degradation. This results in decreased ATG5 levels, inhibiting autophagy and thus reducing 5-FU resistance in colorectal cancer cells both in vitro and in vivo. Furthermore, we confirm that HMBOX1 expression positively correlates with HACE1 expression and inversely correlates with autophagy levels in clinical colorectal cancer tissues. Our findings suggest that HMBOX1 downregulation drives 5-FU resistance through autophagy enhancement in colorectal cancer, highlighting HMBOX1 as a potential target for improving chemosensitivity and patient prognosis.Abbreviation: 3-MA: 3-methyladenine; 5-FU: 5-fluorouracil; ATG: autophagy related; CASP3: caspase 3; C-CASP3: cleaved caspase 3; C-PARP: cleaved PARP; CCK8: cell counting kit-8; ChIP: chromatin immunoprecipitation; CHX: cycloheximide; CNV: copy number variation; co-IP: co-immunoprecipitation; COAD: colorectal adenocarcinoma; CQ: chloroquine; CRC: colorectal cancer; CR: complete response; FHC: fetal human colon; GEO: Gene Expression Omnibus; HACE1: HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1; HMBOX1: homeobox containing 1; IHC: immunohistochemistry; LC-MS/MS: liquid chromatography-tandem mass spectrometry; mIHC: multiplexed immunohistochemistry; MUT: mutant; NC: negative control; OS: overall survival; PBS: phosphate-buffered saline; PD: progressive disease; PFA: paraformaldehyde; PFS: progression-free survival; PR: partial response; qPCR: quantitative polymerase chain reaction; RAPA: rapamycin; SD: stable disease; TCGA: The Cancer Genome Atlas; TEM: transmission electron microscopy; TF: translation factor; USP22: ubiquitin specific peptidase 22; WT: wild type.
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An atlas of healthy and injured cell states and niches in the human kidney.
Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.
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Transcriptomic analysis identifies enrichment of cAMP/PKA/CREB signaling in invasive lobular breast cancer.
OBJECTIVE: Invasive lobular breast cancer (ILC) is the most common special type of breast cancer and has unique clinicopathological and molecular hallmarks that differentiate it from the more common invasive carcinoma-no special type (NST). Despite these differences, ILC and NST are treated as a single entity and there is a lack of ILC-targeted therapies. To fill this gap, we sought to identify novel molecular alterations in ILC that could be exploited for targeted therapies. METHODS: Differential gene expression and Geneset Enrichment and Variation analyses were performed on RNA-seq data from three large public breast cancer databases-the Sweden Cancerome Analysis Network-Breast (SCAN-B; luminal A ILC N = 263, luminal A NST N = 1162), The Cancer Genome Atlas (TCGA; luminal A ILC N = 157, luminal A NST N = 307) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC; luminal A ILC N = 65, luminal A NST N = 533). Pathways enriched in overlapping differentially expressed genes from these datasets were clustered using Jaccard similarity to identify pathways enriched in ILC. The cAMP/PKA/CREB signaling was studied in ILC, ILC-like and NST cell lines and patient-derived organoids (PDOs) using forskolin, an activator of the pathway. RESULTS: Clinicopathological features of patients with ILC and NST in SCAN-B were similar to prior population-based studies. There was a consistent pattern of up-regulation of cAMP/PKA/CREB related signaling in ILC compared to NST in SCAN-B, TCGA and METABRIC. Treatment with forskolin resulted in a greater increase in phospho-CREB in ILC cell lines and organoids than NST. CRISPR deletion of CDH1 in NST cell lines did not alter response of cells to forskolin as measured by phospho-CREB. Forskolin treatment caused growth inhibition in ILC and NST, with ILC cell lines being more sensitive to forskolin-mediated growth inhibition. CONCLUSION: In three separate datasets, cAMP/PKA/CREB signaling was identified to be higher in ILC than NST. This in silico finding was validated in cell line and organoid models. Loss of CDH1 was not sufficient to mediate this phenotype. Future studies should investigate the mechanisms for differential cAMP/PKA/CREB signaling and the potential for therapeutic targeting in patients with ILC.
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Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors.
Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.
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Targeting tumor cell-to-macrophage communication by blocking Vtn-C1qbp interaction inhibits tumor progression via enhancing macrophage phagocytosis.
Background: Cancer cells are capable of evading clearance by macrophages through overexpression of anti-phagocytic surface proteins known as "don't eat me" signals. Monoclonal antibodies that antagonize the "don't-eat-me" signaling in macrophages and tumor cells by targeting phagocytic checkpoints have shown therapeutic promises in several cancer types. However, studies on the responses to these drugs have revealed the existence of other unknown "don't eat me" signals. Moreover, identification of key molecules and interactions regulating macrophage phagocytosis is required for tumor therapy. Methods: CRISPR screen was used to identify genes that impede macrophage phagocytosis. To explore the function of Vtn and C1qbp in phagocytosis, knockdown and subsequent functional experiments were conducted. Flow cytometry were performed to explore the phagocytosis rate, polarization of macrophage, and immune microenvironment of mouse tumor. To explore the underlying molecular mechanisms, RNA sequencing, immunoprecipitation, mass spectrometry, and immunofluorescence were conducted. Then, in vivo experiments in mouse models were conducted to explore the probability of Vtn knockdown combined with anti-CD47 therapy in breast cancer. Single-cell sequencing data from the Gene Expression Omnibus from The Cancer Genome Atlas database were analyzed. Results: We performed a genome-wide CRISPR screen to identify genes that impede macrophage phagocytosis, followed by analysis of cell-to-cell interaction databases. We identified a ligand-receptor pair of Vitronectin (Vtn) and complement C1Q binding protein (C1qbp) in tumor cells or macrophages, respectively. We demonstrated tumor cell-secreted Vtn interacts with C1qbp localized on the cell surface of tumor-associated macrophages, inhibiting phagocytosis of tumor cells and shifting macrophages towards the M2-like subtype in the tumor microenvironment. Mechanistically, the Vtn-C1qbp axis facilitated FcγRIIIA/CD16-induced Shp1 recruitment, which reduced the phosphorylation of Syk. Furthermore, the combination of Vtn knockdown and anti-CD47 antibody effectively enhanced phagocytosis and infiltration of macrophages, resulting in a reduction of tumor growth in vivo. Conclusions: This work has revealed that the Vtn-C1qbp axis is a new anti-phagocytic signal in tumors, and targeting Vtn and its interaction with C1qbp may sensitize cancer to immunotherapy, providing a new molecular target for the treatment of triple-negative breast cancer.
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Integrated bulk and single-cell transcriptomic analysis unveiled a novel cuproptosis-related lipid metabolism gene molecular pattern and a risk index for predicting prognosis and antitumor drug sensitivity in breast cancer.
Breast cancer is the second most prevalent malignant tumor worldwide and is highly heterogeneous. Cuproptosis, a newly identified form of cell death, is intimately connected to lipid metabolism. This study investigated breast cancer heterogeneity through the lens of cuproptosis-related lipid metabolism genes (CLMGs), with the goal of predicting patient prognosis, immunotherapy efficacy, and sensitivity to anticancer drugs. By utilizing transcriptomic data from The Cancer Genome Atlas (TCGA) for breast cancer, we identified 682 CLMGs and applied the nonnegative matrix factorization (NMF) method to categorize breast cancer patients into four distinct clusters: cluster 1, ''immune-cold and stroma-poor''; cluster 2, ''immune-infiltrated''; cluster 3, ''stroma-rich''; and cluster 4, ''moderate infiltration''. We subsequently developed a risk model based on CLMGs that incorporates ACSL1, ATP2B4, ATP7B, ENPP6, HSPH1, PIP4K2C, SRD5A3, and ULBP1. This model demonstrated excellent prognostic predictive performance in both the internal (testing and entire sets) and external (GSE20685 and Kaplan-Meier Plotter sets) validation sets. High-risk patients presented lower expression levels of immune checkpoint-related genes and lower immunophenoscores (IPSs), whereas low-risk patients presented higher CD8+ T-cell infiltration levels and IPSs. Furthermore, the risk index was positively correlated with tumor cell stemness and could predict sensitivity to anticancer drugs. We also confirmed that SRD5A3 was highly expressed in breast cancer and participated in promoting the proliferation and migration of breast cancer cells. In conclusion, the results of this study provide new insights and strategies for assessing prognosis and implementing precision treatment for breast cancer through the lens of CLMGs.
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A novel risk signature based on liquid-liquid phase separation-related genes reveals prognostic and tumour microenvironmental features in clear cell renal cell carcinoma.
BACKGROUND: Clear cell renal cell carcinoma(ccRCC) is one of the most common malignancies. However, there are still many barriers to its underlying causes, early diagnostic techniques and therapeutic approaches. MATERIALS AND METHODS: The Cancer Genome Atlas (TCGA)- Kidney renal clear cell (KIRC) cohort differentially analysed liquid-liquid phase separation (LLPS)-related genes from the DrLLPS website. Univariate and multivariate Cox regression analyses and LASSO regression analyses were used to construct prognostic models. The E-MTAB-1980 cohort was used for external validation. Then, potential functions, immune infiltration analysis, and mutational landscapes were analysed for the high-risk and low-risk groups. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) experiments as well as single-cell analyses validated the genes key to the model. RESULTS: We screened 174 LLPS-related genes in ccRCC and constructed a risk signature consisting of five genes (CLIC5, MXD3, NUF2, PABPC1L, PLK1). The high-risk group was found to be associated with worse prognosis in different subgroups. A nomogram constructed by combining age and tumour stage had a strong predictive power for the prognosis of ccRCC patients. In addition, there were differences in pathway enrichment, immune cell infiltration, and mutational landscapes between the two groups. The results of qRT-PCR in renal cancer cell lines and renal cancer tissues were consistent with the biosignature prediction. Three single-cell data of GSE159115, GSE139555, and GSE121636 were analysed for differences in the presence of these five genes in different cells. CONCLUSIONS: We developed a risk signature constructed based on the five LLPS-related genes and can have a high ability to predict the prognosis of ccRCC patients, further providing a strong support for clinical decision-making.