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High Expression of PDLIM2 Predicts a Poor Prognosis in Prostate Cancer and Is Correlated with Epithelial-Mesenchymal Transition and Immune Cell Infiltration.
PURPOSE: To elucidate the clinical and prognostic role of PDZ and LIM domain protein (PDLIM) genes and the association to epithelial-mesenchymal transition (EMT) and immune cell infiltration in patients with prostate cancer (PRAD). METHODS: The data of RNA-seq, DNA methylation, and clinical features of PRAD patients were collected from The Cancer Genome Atlas (TCGA) database to define the prognostic value of PDLIM gene expression and the association with EMT and immune cell infiltration. A tissue microarray including 134 radical prostatectomy specimens was served as validation by immunohistochemistry (IHC) staining analysis. RESULTS: The mRNA levels of PDLIM1/2/3/4/6/7 were significantly downregulated, while PDLIM5 was upregulated in PRAD (P < 0.05). High expression of PDLIM2 mRNA suggests poor progression free interval in PRAD patients. DNA methylation of PDLIM2 was correlated with its mRNA expression level, and that the cg22973076 methylation site in PDLIM2 was associated with shorter PFI (P < 0.05) in PRAD. Single-sample gene-set enrichment and gene functional enrichment results showed that PDLIM2 was correlated with EMT and immune processes. Spearman's test showed a significant correlation with six reported EMT signatures and several EMT signature-related genes. Tumor microenvironment analysis revealed that the PDLIM2 mRNA expression was positively correlated with the immune score, stromal score, and various tumor infiltrating immune cells. Additionally, the results showed that patients in the high-PDLIM2 mRNA expression group may be more sensitive to immune checkpoint blockade therapy. Finally, IHC analysis further implicated the protein level of PDLIM2 was upregulated in PRAD and acts as a novel potential biomarker in predicting tumor progression. CONCLUSION: Our study suggests that PDLIM family genes might be significantly correlated with oncogenesis and the progression of PRAD. PDLIM2 correlated with EMT and immune cell infiltration by acting as an oncogene in PRAD, which may serve as a potential prognostic biomarker for PRAD patients.
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A spatial and temporal transcriptomic atlas of mouse intestinal regeneration in the jejunum.
The intestinal epithelium exhibits a remarkable capacity for regeneration following injury, yet the spatial and temporal dynamics of the damage-repair cycle remain incompletely understood. We present a spatial transcriptomics atlas of ionizing radiation-induced injury and repair in the mouse jejunum, capturing mRNA from 16,921 genes across approximately 26 million bins at 2-µm resolution in 23 biological samples spanning six days post-irradiation. Analysis reveals transcriptional patterns and niche signals undetectable by bulk or single-cell approaches, including spatially restricted interferon-target gene activation, an altered differentiation trajectory in the lower villus during early repair, two transcriptionally distinct crypt regenerative cell populations, and global transcriptional upregulation consistent with hypertranscription as a hallmark of intestinal regeneration, with evidence for hypertranscription extending to human Crohn's disease. In this work, we provide a spatiotemporal atlas of mouse intestinal regeneration at subcellular 2-µm resolution that will inform future therapeutic strategies for inflammatory bowel diseases and gastrointestinal pathologies arising from injury or cancer therapy.
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A palmitoylation-associated three-gene signature predicts prognosis, immune features, and drug sensitivity in gastric cancer: functional validation of CTHRC1.
BACKGROUND: Gastric cancer (GC) is a heterogeneous malignancy that requires improved molecular tools for prognostic stratification. Protein palmitoylation regulates membrane localization and signaling, but the clinical relevance of palmitoylation-related genes (PRGs) in GC remains unclear. METHODS: Gene Expression Omnibus, The Cancer Genome Atlas, and GeneCards data were integrated to identify PRGs. Differential expression analysis, weighted gene co-expression network analysis, and three machine-learning algorithms were used to select key genes. Diagnostic and prognostic performance was evaluated in TCGA-STAD, platform-specific GEO cohorts, and an institutional cohort of 80 paired tissues. Immune infiltration, single-cell RNA sequencing, immunotherapy-related indices, and GDSC-based drug sensitivity were analyzed. CTHRC1 was further examined using clinical specimens, cell assays, and xenografts. RESULTS: Thirteen differentially expressed PRGs were identified, and SERPINE1, CTHRC1, and EGR1 were selected. The integrated diagnostic model achieved AUCs of 0.935 in TCGA-STAD, 1.000 in GSE54129, and 0.916 in GSE118916, respectively. The three-gene score stratified overall survival in the total, training, and testing TCGA-STAD cohorts and in GSE84437, GSE236522, and the institutional cohort. It remained independently associated with survival in multivariable analysis. High-risk tumors showed increased stromal and immune scores, altered immune-cell composition, higher checkpoint expression, and higher TIDE scores. Single-cell analysis localized SERPINE1 and CTHRC1 mainly to fibroblast-related compartments. CTHRC1 knockdown reduced GC-cell proliferation, clonogenicity, migration, invasion, and xenograft growth while increasing apoptosis. CONCLUSION: The SERPINE1/CTHRC1/EGR1 signature provides prognostic and immune-stromal information in GC, and the experiments support a tumor-promoting role for CTHRC1. The findings define a palmitoylation-associated transcriptional framework but do not establish a palmitoylation-dependent mechanism.
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Integrating bulk and single-cell RNA sequencing analysis to reveal characterization of mechanical stimulus-related genes and prognostic signatures in breast cancer.
OBJECTIVES: To identify molecular clusters and establish a scoring model based on mechanical stimulus-related genes (MSRGs) for predicting the prognosis of breast cancer patients and understanding the role of mechanical stimuli in the breast tumor microenvironment (TME). METHODS: We utilized bulk and single-cell RNA sequencing analysis to characterize MSRGs associated with breast cancer prognosis. Unsupervised consensus molecular clustering was applied to identify distinct clusters based on overall survival-associated MSRGs from The Cancer Genome Atlas (TCGA) database. The scoring model was constructed by LASSO-Cox method and validated. Additionally, single-cell RNA sequencing analysis, along with in vitro and in vivo experiments, were conducted to further investigate the role of the model in breast cancer. RESULTS: We identified 23 overall survival-associated MSRGs and established two molecular subgroups with distinct survival outcomes. A prognostic signature incorporating 15 MSRGs was developed and validated, demonstrating its predictive capability for overall survival of breast cancer patients. The nomogram integrating clinical characteristics and the mechanical stimulus-related risk score exhibited promising predictive accuracy. The low-risk group displayed an immune "hot" phenotype with increased immune cell infiltration, while the high-risk group exhibited resistance to conventional chemotherapy but potential sensitivity to Sepantronium bromide. By using the SCISSOR algorithm, we provide evidence at single-cell resolution for the impact of mechanical stimulation on tumor immune microenvironment. The in vivo and in vitro assays demonstrated that knockdown of TEX19 significantly suppressed breast tumor proliferation. CONCLUSION: We developed a pioneering prognostic signature incorporating MSRGs in breast cancer, with a particular focus on mechanical stimuli may influence breast cancer prognosis by remodeling the immune microenvironment. The findings highlighted the importance of personalized treatment strategies and provide new insights into the role of mechanical forces in breast tumor biology.
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An Activated Dendritic-Cell-Related Gene Signature Indicative of Disease Prognosis and Chemotherapy and Immunotherapy Response in Colon Cancer Patients.
Accumulating evidence has underscored the prognostic value of tumor-infiltrating immune cells in the tumor microenvironment of colon cancer (CC). In this retrospective study, based on publicly available transcriptome profiles and clinical data from the Gene Expression Omnibus and The Cancer Genome Atlas databases, we derived and verified an activated dendritic cell (aDC)-related gene signature (aDCRS) for predicting the survival outcomes and chemotherapy and immunotherapy response of CC patients. We quantified the infiltration abundance of 22 immune cell subtypes via the "CIBERSORT" R script. Univariate Cox proportional hazards (PHs) regression was used to identify aDC as the most robust protective cell type for CC prognosis. After selecting differentially expressed genes (DEGs) significantly correlated with aDC infiltration, we performed univariate Cox-PH regression, LASSO regression, and stepwise multivariate Cox-PH regression successively to screen out prognosis-related genes from selected DEGs for constructing the aDCRS. Receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analysis were employed to assess the discriminatory ability and risk-stratification capacity. The "oncoPredict" package, Cancer Treatment Response gene signature DataBase, and Tumor Immune Dysfunction and Exclusion algorithm were utilized to estimate the practicability of the aDCRS in predicting response to chemotherapy and immune checkpoint blockade. Gene set enrichment analysis and single-cell RNA sequencing analysis were also implemented. Furthermore, an aDCRS-based nomogram was constructed and validated via ROC curves, calibration plots and decision curve analysis. In conclusion, aDCRS and an aDCRS-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions, thus improving the survival outcomes of CC patients in the future.
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Conservation and divergence of cortical cell organization in human and mouse revealed by MERFISH.
The human cerebral cortex has tremendous cellular diversity. How different cell types are organized in the human cortex and how cellular organization varies across species remain unclear. In this study, we performed spatially resolved single-cell profiling of 4000 genes using multiplexed error-robust fluorescence in situ hybridization (MERFISH), identified more than 100 transcriptionally distinct cell populations, and generated a molecularly defined and spatially resolved cell atlas of the human middle and superior temporal gyrus. We further explored cell-cell interactions arising from soma contact or proximity in a cell type-specific manner. Comparison of the human and mouse cortices showed conservation in the laminar organization of cells and differences in somatic interactions across species. Our data revealed human-specific cell-cell proximity patterns and a markedly increased enrichment for interactions between neurons and non-neuronal cells in the human cortex.
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Spatially resolved single cell atlas deciphers SAA1 inflammatory epithelial cells.
Disruption of epithelial integrity is a pivotal event in inflammation, disease pathogenesis, and tissue homeostasis. To investigate these processes in chronic inflammatory disease, we performed spatial transcriptomics integrated with single-cell RNA sequencing (scRNA-seq) on human gingival tissue, coupled with metagenomic analysis of matched subgingival plaque. This approach allowed in situ characterization of epithelial heterogeneity and microbiome-epithelium-connective tissue crosstalk. We identified a distinct inflammatory epithelial subpopulation (SAA1+Epi), situated within the junctional epithelium, that becomes activated through the TLR2-PITX2 axis by Porphyromonas gingivalis lipopolysaccharide. These SAA1+Epi cells secrete TGFβ, which induces an inflammatory program in the connective tissue by driving the differentiation of inflammation-associated fibroblasts (C3+FB) via the PI3K/Akt pathway. Concurrently, SAA1+Epi cells express chemotactic factors such as CXCL6 to recruit NK cells, thereby sustaining the inflammatory niche. The transcription factor PITX2 emerged as a critical regulator of SAA1+Epi differentiation; targeting PITX2 suppressed C3+FB induction and natural killer (NK) cells recruitment, ultimately attenuating periodontitis progression. Our findings position SAA1+Epi as a frontline responder to dysbiotic bacteria at the inflammatory interface and underscore its essential role in regulating epithelial-connective tissue homeostasis during inflammation.
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Colorectal Liver Metastasis Pathomics Model: Integrating Single-Cell and Spatial Transcriptome Analysis With Pathomics for Predicting Liver Metastasis in Colorectal Cancer.
The liver is the primary target organ for hematologic metastasis of colorectal cancer (CRC), and CRC liver metastasis (CRLM) often precludes radical resection, making it the leading cause of death in patients with CRC. To improve the identification and prediction of liver metastasis risk, we identified a cell type of liver metastasis--triggering malignant cells (LMTMCs) through integrating single-cell RNA sequencing and spatial transcriptome analysis. Multiomics cell communication analysis indicated that the interaction between fibroblasts and LMTMCs through the COL1A1-CD44/SDC4 and LAMA4-CD44 signaling axes could promote CRLM. By applying the one-class logistic regression algorithm, we developed a CRLM scoring system in the bulk RNA-sequencing data according to the abundance of LMTMCs in each individual. Using the grouping labels derived from the CRLM scoring system in the bulk data and the corresponding whole-slide images without any manual annotations at the region or pixel level, processed via slide-level weakly supervised learning, a deep-learning model based on the ResNet18 architecture, called Colorectal Liver Metastasis Pathomics Model, was developed to predict the risk of liver metastasis in patients with CRC. The Colorectal Liver Metastasis Pathomics Model achieved an area under the curve of 0.84 at the internal test set of The Cancer Genome Atlas-CRC histology images. In the external independent validation sets, namely the Affiliated Hospital of Southwest Medical University and the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University cohorts, the areas under the curve were 0.89 and 0.72, respectively, indicating effective classification performances. This study provided new insights and tools for the early identification of CRLM and demonstrated the potential of combining multiomics with deep learning-based pathomics in cancer research.
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Artificial intelligence-based multi-modal multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: a retrospective study.
BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contributes to the complexity of PTC. Understanding these complexities is essential for precise risk stratification and therapeutic decisions. METHODS: This study involved a comprehensive analysis of 521 patients with PTC from our hospital and 499 patients from The Cancer Genome Atlas (TCGA). The real-world cohort 1 comprised 256 patients with stage I-III PTC. Tissues from 252 patients were analyzed by DNA-based next-generation sequencing, and tissues from four patients were analyzed by single-cell RNA sequencing (scRNA-seq). Additionally, 586 PTC pathological sections were collected from TCGA, and 275 PTC pathological sections were collected from the real-world cohort 2. A deep-learning multi-modal model was developed using matched histopathology images, genomic, transcriptomic, and immune cell data to predict LNM and disease-free survival (DFS). RESULTS: This study included a total of 1011 PTC patients, comprising 256 patients from cohort 1, 275 patients from cohort 2, and 499 patients from TCGA. In cohort 1, the authors categorized PTC into four molecular subtypes based on BRAF, RAS, RET, and other mutations. BRAF mutations were significantly associated with LNM and impacted DFS. ScRNA-seq identified distinct T-cell subtypes and reduced B-cell diversity in BRAF-mutated PTC with LNM. The study also explored cancer-associated fibroblasts and macrophages, highlighting their associations with LNM. The deep-learning model was trained using 405 pathology slides and RNA sequences from 328 PTC patients and validated with 181 slides and RNA sequences from 140 PTC patients in the TCGA cohort. It achieved high accuracy, with an area under the curve (AUC) of 0.86 in the training cohort, 0.84 in the validation cohort, and 0.83 in the real-world cohort 2. High-risk patients in the training cohort had significantly lower DFS rates ( P <0.001). Model AUCs were 0.91 at 1 year, 0.93 at 3 years, and 0.87 at 5 years. In the validation cohort, high-risk patients also had lower DFS ( P <0.001); the AUCs were 0.89, 0.87, and 0.80 at 1, 3, and 5 years. The authors utilized the GradCAM algorithm to generate heatmaps from pathology-based deep-learning models, which visually highlighted high-risk tumor areas in PTC patients. This enhanced clinicians' understanding of the model's predictions and improved diagnostic accuracy, especially in cases with lymph node metastasis. CONCLUSION: The artificial intelligence (AI)-based analysis uncovered vital insights into PTC molecular heterogeneity, emphasizing BRAF mutations' impact. The integrated deep-learning model shows promise in predicting metastasis, offering valuable contributions to improved diagnostic and therapeutic strategies.
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A Novel Pyroptosis-Related Gene Signature for Early-Stage Lung Squamous Cell Carcinoma.
BACKGROUND: Diagnosis of early stage lung squamous cell carcinoma (LUSC) has improved; however, a comprehensive analysis of prognostic signatures is needed. PURPOSE: To identify, establish, and validate a signature model based on pyroptosis-related genes for prognostic predictions of early stage LUSC. PATIENTS AND METHODS: Two independent cohorts were included. RNA-seq transcriptome data from patients with early stage LUSC were obtained from The Cancer Genome Atlas (TCGA) database. Thirty-three pyroptosis-related genes were analyzed between early stage LUSC and normal lung tissues. Cox regression analysis, random survival forest, and least absolute shrinkage and selection operator algorithms established a three-gene signature. Kaplan-Meier survival and receiver-operating characteristic curves assessed the prognostic efficacy of the model. Single-sample gene set enrichment analysis (ssGSEA) assessed the relationship between pyroptosis and immune cells. Patients with early stage LUSC from the GSE74777 dataset were used for validation. Pyroptosis-related genes were verified by RT-qPCR and Western blotting. RESULTS: Twenty-three differentially expressed pyroptosis-related genes were identified in the LUSC and adjacent normal tissues. Three differentially expressed pyroptosis-related genes were identified as hub genes in early stage LUSC. Patients with early stage LUSC in the TCGA cohort were classified into low- and high-risk subgroups according to the risk score. Overall survival (OS) was significantly short in the high-risk subgroup versus the low-risk subgroup. A similar result was found for the GSE74777 dataset. ssGSEA of immune cells and immune-related pathways between the low- and high-risk subgroups may explain the different OS for patients with early-stage LUSC. IL-6 expression was upregulated, which was inconsistent with the bioinformatic analysis. NOD1 and CASP4 were downregulated in LUSC (all P < 0.05) versus normal lung tissues. CONCLUSION: Differentially expressed pyroptosis-related genes may be involved in early stage LUSC. Pyroptosis-related genes are important in tumor immunity and may be potential prognostic predictors for early stage LUSC.