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The Sex and Age-Associated Infiltration of B Cells May Result in the Dimorphic Behaviors Observed in Papillary Thyroid Carcinomas.
BACKGROUND AND PURPOSE: Sex and age show a dimorphism role in the pathogenesis, lymph node metastasis, and prognostic outcomes of papillary thyroid carcinoma. This investigation endeavors to elucidate the mechanisms underlying these disparities. METHODS: The clinicopathological characteristics and risk factors of lymph node metastasis were explored by analyzing the 2261 patients. The gene expression information of 497 samples from The Cancer Genome Atlas Thyroid Cancer database was used to explore the differentially expressed genes in different phenotypes. What's more, the single-cell RNA sequencing data obtained from the Gene Expression Omnibus database was used to explore the gene expression in specific cells. RESULTS: Multivariate logistic regression analysis showed that in male patients, a larger tumor size, extrathyroidal extension, younger age, and the presence of calcification emerged as significant predictors for lymph node metastasis (LNM)(p < 0.05). Conversely, female patients exhibited a different profile, with larger tumor size, extrathyroidal extension, younger age, calcification, and bilateral tumors being identified as key risk factors (p < 0.05). Further stratification by age demonstrated distinct patterns: among the younger cohort, a larger tumor size, extrathyroidal extension, male gender, calcification, multifocality, and the presence of Hashimoto's thyroiditis held statistical significance (p < 0.05). In contrast, the older subgroup was characterized by a larger tumor size, extrathyroidal extension, male gender, calcification, bilateral tumors, and unclear margins as salient indicators of risk (p < 0.05). In the bulk gene analysis, there were two sex-age-related differentially expressed genes with a contrary trend in tissue sources and LNM status: TCL1A and CR2. The analysis of single-cell RNA sequencing showed that the infiltration of TCL1A- and CR2-related B cells varied in different clinical subtypes. CONCLUSION: Lymph node metastasis of papillary thyroid carcinoma in different sexes and ages may have distinct patterns, and the ages-sex-related B cell infiltration might explain the dimorphism biological behavior.
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S100P as a Shared Biomarker in Inflammatory Bowel Disease, Colorectal Cancer, and Pancreatic Adenocarcinoma: An Integrated Transcriptomic Analysis.
Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD), yet the molecular features shared among these diseases remain incompletely understood. This study aimed to identify common genes and biological pathways associated with IBD, CRC, and PAAD through integrated transcriptomic analysis and experimental validation. Gene expression datasets for IBD, CRC, and PAAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and differential expression analysis were performed to identify disease-associated and shared genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (analyses were used to explore enriched biological functions and pathways. Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts. Receiver operating characteristic analysis was performed to assess the diagnostic performance of common genes. Single-cell RNA sequencing analysis was conducted to examine the cellular distribution of S100P. In addition, the effects of S100P downregulation were evaluated in lipopolysaccharide (LPS)-stimulated colonic epithelial cells. A total of 162 disease-associated genes and four common genes were identified. Functional enrichment analyses indicated significant enrichment of immune- and inflammation-related pathways, including the interleukin-17 signaling pathway. Immune infiltration analysis revealed similar trends in several immune cell populations across IBD, CRC, and PAAD. Single-cell analysis showed elevated S100P expression in epithelial cells from all three diseases. Downregulation of S100P restored the proliferative capacity of LPS-stimulated colonic epithelial cells and reduced inflammatory cytokine expression. Integrated transcriptomic analysis identified S100P as a biomarker associated with IBD, CRC, and PAAD and highlighted shared immune-related features across these diseases.
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Disease characterization using a partial correlation-based sample-specific network.
A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN). By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.
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Machine Learning-based Gene Biomarker Identification for Improving Prognosis and Therapy in Hepatocellular Carcinoma.
INTRODUCTION: Traditional clinical evaluations based on pathological classification have shown limited effectiveness in predicting prognosis and guiding treatment for patients with hepatocellular carcinoma (HCC). This study aims to identify a robust molecular biomarker for improving prognosis and therapy in HCC. METHODS: The International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) provided expression data and clinicopathological information for several cohorts. First, Cox regression analysis and differentially expressed analysis were performed to identify robust prognostic genes. Next, machine learning algorithms, including 101 statistical models, were employed to pinpoint key genes in HCC. Single-cell sequencing analysis was conducted to explore the potential subcellular functions of each key gene. Based on these findings, an HCC Prognosis-Related Index (HPRI) was developed from the identified key genes, and HPRI-based nomogram models were validated across multiple cohorts. Additionally, tumor microenvironment analysis and drug sensitivity analysis were performed further to assess the clinical significance of the HPRI in HCC. RESULTS: A total of 36 robust prognostic genes with differential expression in HCC were identified, from which seven key genes-DCAF13, EEF1E1, GMPS, OLA1, PLOD2, PABPC1, and PPARGC1A-were filtered using machine learning algorithms. Except for PPARGC1A, all these genes were highly expressed in malignant cells, followed by fibroblasts. Notably, we developed the HPRI based on the key genes and validated its clinical relevance. Results demonstrated that the HPRI and HPRI-derived nomogram models had good predictive performance across multiple cohorts. Following tumor microenvironment analysis revealed that a high HPRI was linked to a higher likelihood of immune evasion. Drug sensitivity analysis suggested that patients with a high HPRI might benefit from chemotherapeutic agents like sorafenib, as well as novel compounds such as ML323 and MK-1775. CONCLUSION: Our study demonstrates a well-rounded approach by integrating gene expression, machine learning, tumor microenvironment analysis, and drug sensitivity profiling. HPRI may serve as a promising predictor for guiding prognosis and personalized treatment in HCC.
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Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium.
Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and non-smokers, we generate a comprehensive atlas of epithelial cell types and states, connect these into lineages, and define cell-specific responses to smoking. Our analysis infers multi-state lineages that develop into surface mucus secretory and ciliated cells and then contrasts these to the unique specification of submucosal gland (SMG) cells. Accompanying knockout studies reveal that tuft-like cells are the likely progenitor of both pulmonary neuroendocrine cells and CFTR-rich ionocytes. Our smoking analysis finds that all cell types, including protected stem and SMG populations, are affected by smoking through both pan-epithelial smoking response networks and hundreds of cell-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium.
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Utilization of machine learning algorithms for the identification of the RLN associated prognostic model and feature biomarkers of RLN-related subtypes in breast cancer.
BACKGROUND: Breast cancer (BC) is the most common malignancy afflicting women worldwide, yet the role of relaxin-related genes (RLN) in BC progression remains unclear. This study aims to elucidate the relationship between RLN and BC outcomes through immune microenvironment and metabolic pathway analysis. METHODS: Gene expression and clinical data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Relaxin-related genes were identified using KEGG and Genecard databases. A prognostic model, the RLN Associated Prognostic Model (TRAPM), was established using 101 combinations of 10 machine learning algorithms and validated at the single-cell level. Multi-omics analysis, including the IMvigor210 cohort, was performed to assess TRAPM's applicability in immunotherapy and drug selection. RESULTS: TRAPM, comprising nine prognostic genes (MMP1, RXFP1, PRKCZ, JUN, NFKBIA, GNAI2, NOS2, MMP9, and MMP13), showed significant associations with immune and metabolic profiles. Using TRAPM, a novel BC subtype RC3 and its key marker genes (MTHFD1L, CAVIN4, MMP1, ADGRG6, B3GNT5, SMYD2, and TFRC) were identified. Experimental validation through RT-qPCR and Western Blot confirmed the role of these markers in six BRCA cell lines. CONCLUSIONS: The identification of TRAPM and the RC3 subtype enhances our understanding of BC heterogeneity and highlights potential therapeutic targets. This study provides a foundation for personalized treatment strategies by clarifying the biological significance and clinical relevance of the RC3 subtype.
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USP31 serves as a potential biomarker for predicting prognosis and immune responses for clear cell renal cell carcinoma via single-cell and bulk RNA-sequencing.
BACKGROUND: Currently, there is no research available on the prognosis, potential effect and therapeutic value of USP31 in clear cell renal cell carcinoma (ccRCC). To address this gap, the present study aimed to shed light on its potential roles and possible mechanisms in ccRCC. METHODS: R software was utilized to conduct bioinformatics analyses with the data derived from The Cancer Genome Atlas (i.e. KIRC) and Gene Expression Omnibus datasets. The expression of USP31 in ccRCC was validated by a PCR. The independent prognostic ability of USP31 was evaluated by Cox regression analysis. We conducted gene set enrichment analysis (GSEA) to explore the potential USP31-related pathways. We also discussed the relationships between USP31 and immunity, by predicting its possible upstream transcription factors (TFs) by ChEA3. RESULTS: In ccRCC, USP31 demonstrated a high level of expression and this increased expression was correlated with a poor prognosis (p < 0.05). Through univariate and multivariate Cox regression analysis, USP31 was identified as an independent prognostic factor for ccRCC (p < 0.05). Furthermore, eight USP31-related pathways were identified by GSEA (p < 0.05). Moreover, USP31 was found to be associated with microsatellite instability, tumor microenvironment, a variety of immune cells and immune checkpoints and immune infiltration (p < 0.05). Additionally, Patients with high USP31 expression in ccRCC were shown to have better curative effects after immunotherapy (p < 0.05). Finally, we found that AR, USF1, MXI1 and CLOCK could be the potential upstream TFs of USP31. CONCLUSIONS: USP31 could serve as a potential biomarker for predicting both prognosis and immune responses, revealing its potential mechanisms of TF-USP31 mRNA networks in ccRCC.
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Integrative Genomic and Single-Cell Insights Into Efferocytosis-Mediated Immune Regulation in Clear Cell Renal Cell Carcinoma.
BACKGROUND: Efferocytosis, the phagocytic clearance of apoptotic cells, plays a key role in tumor progression and immune regulation, but its prognostic significance and molecular mechanisms in clear cell renal cell carcinoma (ccRCC) remain unclear. METHODS: Four efferocytosis-related pathways were curated, and the pathway activities were quantified in ccRCC. Prognostic genes were identified by univariate Cox regression and used to construct linear survival models with multiple algorithms, with the optimal model selected by cross-validation. Associations between the risk score and tumor mutational burden (TMB), mutation profiles, and copy number variation (CNV) were subsequently evaluated. Multiomics integration highlighted RAC1 as a key risk gene, which was further examined using single-cell and spatial transcriptomics (ST) to characterize expression patterns, tumor microenvironment interactions, and pathway enrichments. Protein-level validation was performed using immunohistochemistry (IHC) data from the Human Protein Atlas. RESULTS: Efferocytosis pathway activity was upregulated in ccRCC, increased with disease stage, and correlated with poorer survival. The ridge regression-based prognostic model demonstrated consistent predictive performance across independent datasets and was associated with higher TMB, specific mutation patterns, and increased CNV. Notably, RAC1, identified as the top weighted gene in the model, was overexpressed in association with copy number amplification, showing preferential enrichment in malignant core regions and strong links to oncogenic signaling. CONCLUSION: Efferocytosis activation characterizes aggressive ccRCC. The developed prognostic model and identification of RAC1 as a central effector link efferocytosis-related risk to immune remodeling and oncogenic signaling, providing potential biomarkers and therapeutic targets.
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An immune and epithelial-mesenchymal transition-related risk model and immunotherapy strategy for grade II and III gliomas.
Grade II and III gliomas are heterogeneous and aggressive diseases. More efficient prognosis models and treatment methods are needed. This study aims to construct a new risk model and propose a new strategy for grade II and III gliomas. The data were downloaded from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), gene set enrichment analysis (GSEA), and the EMTome website for analysis. The Human Cell Landscape website and the Genomics of Drug Sensitivity in Cancer website were used for single-cell analysis and drug susceptibility analysis. Gene set enrichment analysis, gene function enrichment analysis, univariate and multivariate Cox regression analyses, Pearson's correlation analysis, log-rank test, Kaplan-Meier survival analysis, and ROC analysis were performed. We constructed an immune-related prognostic model associated with the isocitrate dehydrogenase 1 (IDH1) mutation status. By analyzing the immune microenvironment of patients with different risk scores, we found that high-risk patients were more likely to have an inflammatory immune microenvironment and a higher programmed death ligand-1 (PD-L1) expression level. Epithelial-mesenchymal transition (EMT)-related gene sets were significantly enriched in the high-risk group, and the epithelial-mesenchymal transition phenotype was associated with a decrease in CD8+ T cells and an increase in M2 macrophages. Transforming growth factor-β (TGF-β) signaling was the most important signaling in inducing epithelial-mesenchymal transition, and TGFB1/TGFBR1 was correlated with an increase in CD8+ T cytopenia and M2 macrophages. Survival analysis showed that simultaneous low expression of TGFBR1 and PD-L1 had better survival results. Through single-cell analysis, we found that TGFB1 is closely related to microglia and macrophages, especially M2 macrophages. Finally, we discussed the sensitivity of TGFB1 inhibitors in gliomas using cell line susceptibility data. These results demonstrated a potential immunotherapy strategy in combination with the TGFB1/TGFBR1 inhibitor and PD-1/PD-L1 inhibitor for grade II and III gliomas.
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Single-Cell Profiling Identifies the SPDEF/GAS5 Axis as a Potential Driver of Tamoxifen Resistance in Estrogen Receptor-Positive Breast Cancer.
Background Estrogen receptor-positive (ER+) breast cancer is the most prevalent breast cancer subtype, and tamoxifen remains the cornerstone of adjuvant endocrine therapy. However, a significant proportion of patients eventually develop acquired tamoxifen resistance, leading to disease recurrence and progression. Methodology To characterize the cellular and molecular mechanisms underlying this resistance at single-cell resolution, we performed an integrative analysis of publicly available single-cell RNA sequencing data from 16 ER+ breast cancer specimens, comprising 13 treatment-naive primary tumors and three tamoxifen-resistant recurrent tumors. Following rigorous quality control, batch correction, and dimensionality reduction, we identified 11 major cell populations in the tumor microenvironment. Copy number variation inference using InferCNV was utilized to confirm the malignant identity of the epithelial cells. Results Gene Ontology Biological Process enrichment analysis revealed that recurrent malignant cells were significantly enriched for pathways associated with translation, chromatin remodeling, and cell division compared with primary tumor cells. Sub-clustering of malignant epithelial cells resolved nine distinct subpopulations, among which a GAS5+ (growth arrest-specific 5-positive) subpopulation was markedly expanded in tamoxifen-resistant recurrent tumors. This subpopulation exhibited distinctive metabolic reprogramming characterized by enhanced aerobic respiration and energy metabolism. Monocle2-based pseudotemporal trajectory analysis positioned GAS5+ cells at a terminally differentiated state, likely derived from CLDN3+ and CEACAM6+ tumor progenitor cells. Single-cell regulatory network inference using SCENIC identified SPDEF (SAM pointed domain-containing ETS transcription factor) as a putative key transcription factor specifically active in GAS5+ tumor cells, and SPDEF expression was highly enriched in this subcluster. Validation in The Cancer Genome Atlas breast cancer cohort demonstrated that high SPDEF expression was significantly associated with worse overall survival (log-rank p = 0.011). Conclusions These findings implicate the SPDEF-GAS5+ regulatory axis as a potential driver of tamoxifen resistance and a compelling candidate for future therapeutic targeting in ER+ breast cancer.