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PLCH1 overexpression promotes breast cancer progression and predicts poor prognosis through the ERK1/2-EGR1 axis.
BACKGROUND: Phospholipase C η1 (PLCH1), a member of the phospholipase C superfamily, has been implicated in the development of multiple cancers. However, its specific role in breast cancer progression, its association with clinicopathological features, and its prognostic significance remain unclear. METHODS: PLCH1 expression was analyzed across multiple tumor types using the TNMplot database, which integrates RNA-seq, microarray, and normalized data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO), encompassing 40,442 tumor and 15,648 normal samples. Differential expression analysis was performed using boxplots and statistical tests to assess significance. DNA methylation and survival analyses were conducted using TCGA data, with Kaplan-Meier curves and Cox regression to evaluate prognostic value. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, were performed on differentially expressed genes using the clusterProfiler package. Mutation analyses were conducted using mutation annotation format (MAF) files, and pathway activities were correlated with PLCH1 expression via single-sample GSEA (ssGSEA). Experimental validation included immunohistochemistry (IHC) on 100 breast invasive ductal carcinoma samples, real-time quantitative PCR (RT-qPCR), and Western blotting. PLCH1 knockdown functional studies assessed cell proliferation and signaling pathways. RESULTS: PLCH1 was significantly overexpressed in various cancers, including breast cancer, compared to normal tissues. PLCH1 expression was strongly correlated with the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) in breast cancer tissues, further linking PLCH1 to poor prognosis and adverse patient outcomes. Functional studies revealed that PLCH1 was highly expressed in breast cancer cell lines, and PLCH1 knockdown significantly inhibited cell proliferation, induced cell cycle arrest, and reduced cyclin-dependent kinase 1 (CDK1) expression in BT-474 cells. Mechanistically, PLCH1 silencing downregulated early growth response 1 (EGR1) expression by suppressing the extracellular signal-regulated kinases 1 and 2 (ERK1/2) signaling pathway, impairing tumor cell proliferation. CONCLUSIONS: PLCH1 was overexpressed in breast cancer and was associated with worse patient outcomes. Its role in promoting cell proliferation via the ERK1/2-EGR1 axis highlighted PLCH1 as a potential therapeutic target for breast cancer. These findings offer new insights into the molecular mechanisms underlying breast cancer progression and suggest promising avenues for targeted therapy development.
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Mechanistic study of glutamine metabolic reprogramming driving non-small cell lung cancer progression via the FGF17-FGFR4 axis mediating epithelial-mesenchymal transition.
INTRODUCTION: The reprogramming of glutamine metabolism holds a pivotal position in the energy provision and biosynthesis of tumors. However, the regulatory mechanism of this phenomenon in non-small cell lung cancer (NSCLC) is still not well-understood. NSCLC is a type of malignancy that has a high incidence and mortality rate globally. There is an urgent need to elucidate the role of glutamine metabolism in its pathological mechanism. This clarification may provide theoretical guidance for developing new therapeutic approaches. METHODS: Core targets of glutamine metabolism were screened by integrating single-cell transcriptomic and RNA sequencing data from public databases. Target expression was validated in clinical samples by immunohistochemistry (IHC) and Western blot (WB), and its association with clinical features was analyzed. Lentiviral gene silencing was employed to establish glutamine-deprived cell models and xenograft mouse models. To evaluate the effects of the target on cell proliferation, redox balance, and migratory/invasive behavior in cell culture and animal models, we utilized Transwell assays, colony formation assays, redox detection kits, and Seahorse metabolic flux analysis. Subsequently, WB and IHC served to elucidate the downstream pathways and potential synergistic effects of the drugs. RESULTS: Analysis of the single-cell atlas revealed a marked increase in epithelial (Epi) cell populations in the tumor milieu of NSCLC. By integrating weighted gene co-expression network analysis (WGCNA) with RNA sequencing, fibroblast growth factor 17 (FGF17) was pinpointed as a crucial regulatory factor. High FGF17 expression showed a strong association with poor prognosis in patient (p = 0.0078). Consistent clinical data further demonstrated that FGF17 upregulation was associated with higher TNM stages and the presence of lymph node metastasis. Functional and mechanistic analyses revealed that silencing FGF17 suppressed the FGFR4/MEK5/ERK5 signaling cascade, disturbed NRF2-dependent redox homeostasis, and consequently impaired epithelial-mesenchymal transition (EMT), leading to a marked reduction in cancer cell motility and invasiveness. In vivo, targeting FGF17 was shown to synergistically enhance cisplatin antitumor activity and reverse the EMT phenotype. CONCLUSION: As a critical driver of glutamine metabolic reprogramming, FGF17-activated under conditions of GLUL overexpression-stimulates the FGFR4/MEK5/ERK5/NRF2 signaling cascade to maintain redox homeostasis and promote invasion, thereby accelerating NSCLC progression. Targeted intervention of the pathway reverses malignant phenotypes and enhances chemosensitivity. These findings highlight FGF17 as a potential therapeutic target for NSCLC and provide new insights into tumor metabolism and EMT, thereby may paving the way for novel combination therapies.
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Integrated multi-omics analysis reveals folate metabolism-related genes as prognostic markers and therapeutic targets in clear cell renal cell carcinoma.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is an aggressive tumor with high metastatic potential and therapeutic resistance, yet the role of folate metabolism in its pathogenesis and immune evasion remains unclear. This study aims to develop and validate a folate metabolism-related gene (FMRG) scoring system to stratify patients by prognostic risk and immune phenotypes, and to explore the functional role of key FMRGs in ccRCC progression. METHODS: Using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA), we developed a folate metabolism-related gene (FMRG) scoring system via integrative machine learning and validated it in an external cohort. We analyzed associations of the FMRG score with clinicopathological features, biological pathways, immune infiltration, therapeutic responsiveness, and drug sensitivity. Single-cell RNA sequencing and spatial transcriptomics mapped candidate gene expression, and in vitro experiments validated the functional role of NGF. RESULTS: A ten-gene prognostic model based on the FMRG score stratified ccRCC patients into groups with distinct clinical outcomes, immune profiles, and therapeutic responses. NGF was upregulated in ccRCC, with heterogeneous spatial expression. Functional assays showed that NGF enhances proliferation, migration, and invasion. CONCLUSIONS: This folate metabolism-based scoring framework facilitates prognostic stratification, tumor microenvironment characterization, and prediction of immunotherapy response in ccRCC. NGF is identified as a functional mediator of tumor progression, offering potential therapeutic targets and insights into metabolic-immune crosstalk.
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High RSPO3 protein expression serves as an independent poor prognostic factor and promotes malignant progression in breast cancer.
OBJECTIVE: To elucidate the expression pattern, clinical prognostic significance, and distribution characteristics of R-spondin 3 (RSPO3) within the tumor microenvironment (TME) of breast cancer (BC), to observe its effects on the malignant biological behaviors of MCF-7 cells, and to explore the possible mechanisms underlying its function. METHODS: Integrated mRNA expression analysis was performed across multiple cohorts using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and validated by quantitative reverse transcription polymerase chain reaction (qRT PCR) in clinical samples. Immunohistochemistry (IHC) was used to detect RSPO3 protein expression in breast cancer tissue microarrays (TMAs), and the results were combined with survival data to assess prognostic value. Single‑cell transcriptomics data were interrogated to identify the primary cellular sources of RSPO3, followed by differential gene expression and pathway enrichment analyses in key subpopulations. Finally, in vitro functional assays were conducted to investigate the effects of RSPO3 on breast cancer cell proliferation, migration, and apoptosis. Specifically, a co‑culture system of human breast cancer‑associated fibroblasts (HBCFs) and MCF‑7 cells was employed to examine changes in RSPO3 expression and the proliferative and migratory capacities of breast cancer cells after co‑culture. The function of RSPO3 was verified by knocking down RSPO3 in the co‑culture system, and further validation was carried out through Western blotting, CHIR99021 activator rescue experiments, and tissue analysis. RESULTS: Compared to adjacent non-tumor tissues, RSPO3 mRNA expression was generally downregulated in BC tissues; however, RSPO3 protein levels were significantly higher in tumor tissues. High RSPO3 protein expression was an independent risk factor for reduced overall survival (OS). Single-cell analysis revealed that within the TME, RSPO3 was primarily expressed in fibroblasts, endothelial cells, and epithelial cells. Notably, among fibroblasts, RSPO3 expression was specifically enriched in the inflammatory cancer-associated fibroblast (iCAF) subpopulation. RSPO3⁺ iCAFs exhibited functional characteristics related to cell proliferation regulation, angiogenesis, Wnt signaling, and oxidative phosphorylation. In vitro experiments demonstrated that RSPO3 knockdown suppressed the proliferation, migration, and invasion of MCF-7 cells while promoting apoptosis.Furthermore, co-culture of HBCFs with MCF-7 cells significantly increased RSPO3 protein levels in MCF-7 cells, accompanied by markedly enhanced proliferation and migration abilities. Knockdown of RSPO3 significantly reversed these pro-tumorigenic effects. RSPO3 knockdown downregulated the expression of Axin2, Cyclin D1, and β-catenin. Treatment with the Wnt pathway activator CHIR99021 partially rescued the inhibition of invasion and migration induced by RSPO3 knockdown. Immunohistochemical staining of breast cancer tissues further confirmed the positive correlation between RSPO3 expression and β-catenin and Cyclin D1 expression. CONCLUSIONS: RSPO3 exhibited significant discrepancies between transcriptional and translational levels in breast cancer tissues. High RSPO3 protein expression indicated poor prognosis and served as an independent prognostic risk factor. Breast cancer-associated fibroblasts may promote tumor cell proliferation and migration by regulating RSPO3 expression and subsequently activating the Wnt/β-catenin pathway. RSPO3 represents a potential prognostic biomarker and therapeutic target.
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High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis.
BACKGROUND: Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, our understanding of the comprehensive transcriptional and phenotypic landscape of the cells within these lesions is limited. METHODS: To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology. RESULTS: We identified 25 cell populations, each with a unique multiomic signature, including macrophages, T cells, NK (natural killer) cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Among the macrophages, we identified 2 proinflammatory subsets enriched in IL-1B (interleukin-1B) or C1Q expression, 2 TREM2-positive foam cells (1 expressing inflammatory genes), and subpopulations with a proliferative gene signature and SMC-specific gene signature with fibrotic pathways upregulated. Further characterization revealed various subsets of SMCs and fibroblasts, including SMC-derived foam cells. These foamy SMCs were localized in the deep intima of coronary atherosclerotic lesions. Utilizing cellular indexing of transcriptomes and epitopes by sequencing data, we developed a flow cytometry panel, using cell surface proteins CD29, CD142, and CD90, to isolate SMC-derived cells from lesions. Lastly, we observed reduced proportions of efferocytotic macrophages, classically activated endothelial cells, and contractile and modulated SMC-derived cells, while inflammatory SMCs were enriched in plaques of clinically symptomatic versus asymptomatic patients. CONCLUSIONS: Our multimodal atlas of cell populations within atherosclerosis provides novel insights into the diversity, phenotype, location, isolation, and clinical relevance of the unique cellular composition of human carotid atherosclerosis. These findings facilitate both the mapping of cardiovascular disease susceptibility loci to specific cell types and the identification of novel molecular and cellular therapeutic targets for the treatment of the disease.
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Clinical translation of senescence-related pan-cancer multi-omics: tools for assessment and immunotherapy prediction.
Cellular senescence (CS) exerts dual roles in tumorigenesis, yet its pan-cancer molecular characteristics and clinical value remain unclear, hindering its translation to oncology and personalized therapy. To address the lack of specific and universal tools for senescence assessment and immunotherapy response prediction, this study systematically analyzed 1259 CS-related genes from the CellAge database across 31 cancer types by integrating multi-omics data, including bulk RNA-seq, single-cell/spatial transcriptomics, and CRISPR screening. We developed a rank-based algorithm SenScoreR (publicly available at https://gxhub.shinyapps.io/SenScoreR/ ) for senescence quantification, validated with 10 independent datasets, and constructed a machine learning-based predictive model CS.Sig for immunotherapy response. Results showed that tumors had significantly lower Rank-based Senescence Score (RSS) than normal tissues across 31 cancers (average diagnostic AUC = 0.895), with low RSS linked to poor survival; high RSS correlated with reduced genomic instability, enriched CD8⁺ T/NK cell/macrophage infiltration, upregulated PD-L1 expression, and elevated immune cytolytic activity. CS.Sig demonstrated robust performance in predicting ICI response (AUC = 0.716 across 10 cohorts), outperforming 13 existing signatures, while CRISPR screening identified 17 senescence-related targets (e.g., CEP55, PPP1CC) whose knockout enhanced anti-tumor immunity. Our findings clarify CS's role in maintaining tumor genomic stability and shaping immune microenvironments, and the developed SenScoreR, CS.Sig, and identified targets bridge basic CS research with clinical oncology, providing a translational resource and hypothesis basis for future experimental and clinical validation.
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Single-cell transcriptome unveils mesenchymal cell diversity in endometriosis.
Mesenchymal cells constitute the primary structural support elements within endometriotic lesions, yet their pivotal roles in endometriotic pathogenesis remain largely uncharted. This study aimed to construct a single-cell atlas of endometriosis using samples from three ovarian tissues affected by endometriosis and three normal ovarian tissues. Through the utilization of scRNA-seq, we have unveiled six distinct mesenchymal subclusters in normal and endometriosis-afflicted ovaries, elucidating the diverse functions of mesenchymal populations in endometriosis. Our comprehensive analysis has revealed that mesenchymal cells predominantly engage in three key functions: ribosome-mediated protein synthesis and processing, cell adhesion facilitating intercellular support and communication, and a range of metabolic processes. Furthermore, our findings have identified several pivotal differentially expressed genes (e.g. C3, FN1, COL3A1, COL1A1, NRXN3), primarily associated with the complement and coagulation cascades, extracellular matrix (ECM) regulation, ECM receptor interactions, and cell adhesion molecules. In essence, our study provides a comprehensive transcriptomic dataset and novel insights into adhesive molecule and integrin networks within mesenchymal subclusters in endometriosis. This, in effect, has deepened the understanding of the pathomechanisms governing this condition.
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Research into the characteristic molecules significantly affecting liver cancer immunotherapy.
BACKGROUND: The past decade has witnessed unprecedented scientific breakthroughs, including immunotherapy, which has great potential in clinical applications for liver cancer. METHODS: Public data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and analyzed with R software. RESULTS: The LASSO and SVM-RFE machine learning algorithms identified 16 differentially expressed genes (DEGs) related to immunotherapy, namely, GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2. Moreover, a logistic model (CombinedScore) was established based on these DEGs, showing an excellent prediction performance for liver cancer immunotherapy. Patients with a low CombinedScore might respond better to immunotherapy. Gene Set Enrichment Analysis showed that many metabolism pathways were activated in patients with a high CombinedScore, including butanoate metabolism, bile acid metabolism, fatty acid metabolism, glycine serine and threonine metabolism, and propanoate metabolism. Our comprehensive analysis showed that the CombinedScore was negatively correlated with the levels of most tumor-infiltrating immune cells and the activities of key steps of cancer immunity cycles. Continually, the CombinedScore was negatively associated with the expression of most immune checkpoints and immunotherapy response-related pathways. Moreover, patients with a high and a low CombinedScore exhibited diverse genomic features. Furthermore, we found that CDCA7 was significantly correlated with patient survival. Further analysis showed that CDCA7 was positively associated with M0 macrophages and negatively associated with M2 macrophages, suggesting that CDCA7 could influence the progression of liver cancer cells by affecting macrophage polarization. Next, single-cell analysis showed that CDCA7 was mainly expressed in prolif T cells. Immunohistochemical results confirmed that the staining intensity of CDCA7 was prominently increased in the nucleus in primary liver cancer tissues compared to adjacent non-tumor tissues. CONCLUSIONS: Our results provide novel insights into the DEGs and factors affecting liver cancer immunotherapy. Meanwhile, CDCA7 was identified as a potential therapeutic target in this patient population.
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TNIK depletion induces inflammation and apoptosis in injured renal proximal tubule epithelial cells.
In the aftermath of acute kidney injury (AKI), surviving proximal tubule epithelia repopulate injured tubules to promote repair. However, a portion of cells fail to repair [termed failed-repair proximal tubule cells (FR-PTCs)] and exert ongoing proinflammatory and profibrotic effects. To better understand the molecular drivers of the FR-PTC state, we reanalyzed a mouse ischemia-reperfusion injury single-nucleus RNA-sequencing (snRNA-seq) atlas to identify Traf2 and Nck interacting kinase (Tnik) to be exclusively expressed in FR-PTCs but not in healthy or acutely injured proximal tubules after AKI (2 and 6 wk) in mice. We confirmed expression of Tnik protein in injured mouse and human tissues by immunofluorescence. Then, to determine the functional role of Tnik in FR-PTCs, we depleted TNIK with siRNA in two human renal proximal tubule epithelial cell lines (primary and immortalized hRPTECs) and analyzed each by bulk RNA-sequencing. Pathway analysis revealed significant upregulation of inflammatory signaling pathways, whereas pathways associated with differentiated proximal tubules such as organic acid transport were significantly downregulated. TNIK gene knockdown drove reduced cell viability and increased apoptosis, including differentially expressed poly(ADP-ribose) polymerase (PARP) family members, cleaved PARP-1 fragments, and increased annexin V binding to phosphatidylserine. Together, these results indicate that Tnik upregulation in FR-PTCs acts in a compensatory fashion to suppress inflammation and promote proximal tubule epithelial cell survival after injury. Modulating TNIK activity may represent a prorepair therapeutic strategy after AKI.NEW & NOTEWORTHY The molecular drivers of successful and failed repair in the proximal tubule after acute kidney injury (AKI) are incompletely understood. We identified Traf2 and Nck interacting kinase (Tnik) to be exclusively expressed in failed-repair proximal tubule cells after AKI. We tested the effect of siTNIK depletion in two proximal tubule cell lines followed by bulk RNA-sequencing analysis. Our results indicate that TNIK acts to suppress inflammatory signaling and apoptosis in injured renal proximal tubule epithelial cells to promote cell survival.
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Molecular clustering and prognostic features based on integrated databases predict survival and immune status in patients with gastric cancer.
BACKGROUND: Gastric cancer (GC) remains one of the most common malignancies worldwide with high mortality rates despite advances in treatment approaches. Patients frequently develop drug resistance to current therapies, highlighting the critical need for novel prognostic biomarkers that can enhance survival rates and guide immunotherapy decisions in patients with GC. METHODS: We conducted a comprehensive bioinformatics analysis using integrated clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. GC cases were categorized into two prognostic-related gene (PRG) clusters, and differentially expressed genes were identified. We established a prognostic model based on 11 key genes, stratified patients into high-risk and low-risk groups, and developed a nomogram model for survival prediction. Expression of selected genes was validated through quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry in clinical samples. RESULTS: The identified PRGs and gene clusters strongly associated with patient survival, immune system functions, and cancer-related pathways. Risk scores significantly correlated with immune cell abundance, checkpoint expression, and responses to immunotherapy and chemotherapy. For instance, the area under the curve (AUC) values of patients at 1-year, 3-year, and 5-year survival were all greater than 0.6 in the ROC curves (p < 0.05), which makes our prediction more accurate, and the line graphs predicted a 1-year survival rate exceeding 0.907, a 3-year survival rate exceeding 0.726, and a 5-year survival rate exceeding 0.633; the calibration curves are almost close to the predicted ones (p < 0.05). This implies that patients in the high-risk group demonstrated significantly poorer prognosis. Univariate Cox (UniCox) analysis and multivariate Cox (MultiCox) analysis indicate that CTHRC1 (Collagen Triple Helix Repeat Containing 1), CST6 (Cystatin E/M), and AKR1B1 (Aldo-Keto Reductase Family 1 Member B) are independent prognostic factors, and all are associated with poor survival prognosis (HR > 1, p < 0.05). Gene set enrichment analysis (GSEA) and single-cell analysis revealed significant enrichment of multiple biological pathways and variability in expression of these genes across different cell types within the tumor microenvironment. qRT-PCR and immunohistochemistry confirmed significant differences in mRNA and protein expression of CTHRC1, CST6, and AKR1B1 between normal and GC tissues (p < 0.05). CONCLUSION: Our research establishes a robust molecular signature for predicting survival of patients with GC and characterizing the tumor immune microenvironment. It aims not only to establish a prognostic model, but also to explore immunobiological functions. The identified prognostic features and key genes (CTHRC1, CST6, and AKR1B1) offer potential as biomarkers and therapeutic targets, potentially guiding more effective personalized treatment strategies for patients with GC.