Background Idiopathic nephrotic syndrome (INS) is the most prevalent glomerular disease in children, and RNA modification might play a pivotal role in its pathogenesis. However, the mechanisms connecting RNA modification to INS remain poorly elucidated. This study investigated the intrinsic mechanisms of INS by comprehensively analyzing single‐cell transcriptomic data in conjunction with RNA modification‐related genes (RRGs). Methods INS‐related single‐cell transcriptome data were obtained from a public database, and RRGs were curated from the literature. Key cells and biomarkers were identified in both INS and healthy samples through single‐cell RNA sequencing and protein–protein interaction analyses. We characterized the molecular profiles of the biomarkers, constructed regulatory networks, and identified potential drugs through drug prediction and molecular docking. Furthermore, dynamic biomarker expression and intercellular interactions were explored through cell communication and pseudotime analyses, providing a theoretical basis for the clinical treatment of INS in children. Results B cells were identified as key cells in INS, and RBM25 and PRPF40A were established as biomarkers. Notably, RBM25 was primarily localized in the cytoplasm and encoded on chromosome 14, whereas PRPF40A was predominantly found in the nucleus and encoded on chromosome 2. The molecular regulatory network revealed that the highest activity scores in B cells were associated with EBF1 (+), TCF4 (+), MEF2C (+), SOX5 (+), and POU2F2 (+). Additionally, fulvestrant was identified as a drug targeting both biomarkers. Finally, the key receptor–ligand pair MIF–CD74/CXCR4 between NK and B cells was identified through cellular communication and pseudotime analyses, with biomarker expression decreasing during middifferentiation and increasing in later stages. Conclusion Based on single‐cell transcriptomics, this study has preliminarily identified B cells as key cells in INS and screened RBM25 and PRPF40A as biomarkers, providing initial theoretical insights for the exploration of subsequent targeted therapies. However, due to limitations in sample size, these findings still need to be validated in a larger, independent cohort.
Background:The crosstalk between inflammation and immunity plays a central role in tumor progression, immune evasion, and therapeutic response. Interleukin-1 receptor accessory protein (IL1RAP) is a key adaptor in inflammatory signaling, yet its immunological relevance and clinical implications in skin cutaneous melanoma (SKCM) remain largely unexplored. Methods:We performed an integrative analysis combining pan-cancer and melanoma-focused datasets. Bulk transcriptomic, single-cell, spatial transcriptomic, genomic alteration, pharmacogenomic, and clinical survival data were obtained from TCGA, GTEx, GEO, ENA, and other public resources. IL1RAP expression was evaluated across cancer types in relation to diagnostic performance, immune subtypes, survival outcomes, functional pathway activity, immune-genomic states, somatic alterations, and drug-response metrics. Melanoma-focused analyses examined immune infiltration, methylation-derived tumor-infiltrating lymphocyte (MeTIL) scores, and exploratory survival associations in five treatment cohorts; the survival groups were defined using cohort-specific optimal cutoffs rather than median splits. Results:IL1RAP expression differed between tumor and normal tissues in multiple cancers, although the direction and magnitude varied by cancer type. Pan-cancer survival associations were likewise context dependent. Single-cell and spatial transcriptomic resources indicated cell-type and spatial heterogeneity of IL1RAP expression within tumor microenvironments. Pathway, immune-genomic, and pharmacogenomic analyses identified exploratory associations with functional states, genomic features, and drug-response metrics. In SKCM, IL1RAP expression was associated with several immune-infiltration estimates and higher MeTIL scores. Across five melanoma immunotherapy cohorts, the direction and magnitude of the overall survival associations varied substantially. Conclusions:This retrospective integrative analysis suggests that IL1RAP may mark an inflammation-immunity-related state in SKCM. The heterogeneous associations across cancers and melanoma treatment cohorts support further validation but do not establish IL1RAP as a causal regulator, a treatment-response predictor, or a therapeutic target.
Background Charcot–Marie–Tooth (CMT) disease is the most prevalent and largest group of peripheral neuropathies, primarily affecting the peripheral nervous system. One of the rarest types of CMT is known as Charcot–Marie–Tooth disease, demyelinating, Type 4B1 (CMT4B1), with an autosomal recessive mode of inheritance. CMT4B1 is an axonal demyelination impacting both motor and sensory nervous systems, resulting in a slow and continuous decline in strength and wasting of muscles in both proximal and distal areas. In addition, CMT4B1 arises from inherited mutations in the MTMR2 gene. So far, only 22 MTMR2 germline variants have been reported for causing CMT4B1. Methods Here, we investigated and examined two Han Chinese patients who were clinically diagnosed with CMT4B1. However, the proband′s parents and younger brother were asymptomatic and normal. We performed whole exome sequencing and Sanger sequencing to identify the disease‐causing variant in these probands. Results Whole‐exome sequencing identified two novel homozygous nonsense variants (c.618G > A, p.Trp206∗ and c.1616C > A, p.Ser539∗) in the MTMR2 gene in these two patients, respectively. Sanger sequencing confirmed that both parents of these two patients carried these two novel variants in a heterozygous state, respectively. These two novel homozygous variants are predicted to lead to the formation of truncated MTMR2 proteins, which may cause complete loss of MTMR2 protein due to nonsense‐mediated mRNA decay (NMD). Hence, these two novel variants are classified as loss-of-function variants. Conclusion Our current research broadened the mutation profile of the MTMR2 gene linked to CMT4B1 and highlighted the significance of utilizing whole‐exome sequencing to identify pathogenic variants in CMT4B1 patients. Our study also provides, for the first time, a compilation of previously reported pathogenic variants of the MTMR2 gene associated with CMT4B1.
Bladder cancer is characterized by substantial molecular heterogeneity that complicates prognostic assessment and therapeutic decision-making. DNA damage repair (DDR) pathways play a central role in maintaining genomic stability, yet a comprehensive genomics-based stratification framework centered on DDR alterations remains insufficiently defined. In this study, we performed an integrative genomic analysis of 406 bladder cancer samples from The Cancer Genome Atlas. Weighted gene coexpression network analysis was applied to identify bladder cancer–relevant DDR modules from a curated DDR gene set. Protein–protein interaction analysis prioritized 44 core DDR genes for molecular classification. Unsupervised consensus clustering revealed two DDR-associated molecular subtypes with significantly distinct overall survival and transcriptional programs. One subtype exhibited enrichment of immune-related pathways and stromal signatures, whereas the other was characterized by enhanced cell-cycle progression and DNA replication activity. To enable individualized prognostic prediction, an eight-gene DDR-associated signature was constructed using the least absolute shrinkage and selection operator and multivariable Cox regression analyses. The model effectively stratified patients into high- and low-risk groups and demonstrated stable predictive performance, with 1-, 3-, and 5-year area under the curve values of 0.745, 0.736, and 0.754, respectively. The signature was further evaluated in an independent GEO cohort (GSE13507), in which DDR-based clustering and risk stratification showed consistent prognostic trends, supporting the generalizability of the framework. High-risk tumors displayed increased tumor mutational burden, distinct immune infiltration patterns, and differential predicted drug sensitivity profiles. Protein-level validation supported dysregulated expression of key genes in tumor tissues. Collectively, this integrative genomics study establishes a DDR-centered molecular classification and prognostic framework, providing insights into genomic instability–driven heterogeneity and potential strategies for precision stratification in bladder cancer.
Background Both anoikis resistance and lactate‐driven protein lactylation are critical mechanisms of tumor progression; however, their synergistic prognostic value and combined impact on the gastric cancer (GC) microenvironment remain poorly understood. This study is aims to develop and biologically interpret a prognostic model integrating these two biological processes to improve patient stratification. Methods We identified lactylation‐ and anoikis‐related differentially expressed genes (LARDEGs) utilizing transcriptomic data from TCGA‐STAD and GTEx. A prognostic signature was constructed via bootstrap LASSO‐Cox regression and validated across two independent external cohorts (GSE62254 and GSE84437). To dissect the tumor microenvironment, we employed consensus clustering and immune deconvolution. Additionally, single‐cell RNA sequencing (scRNA‐seq) and intercellular communication algorithms were utilized to trace cellular origins, with key findings verified by qRT‐PCR in 20 paired clinical GC samples. Results A total of 66 LARDEGs were identified, yielding an independent, four‐gene prognostic signature (comprising GPC3 , IGF2BP1, LUM , and SOX9 ). Tumors classified within the high‐risk cohort exhibited pronounced stromal infiltration and an immunosuppressive landscape. Importantly, scRNA‐seq mapping revealed that this signature is predominantly localized to a specific subpopulation of extracellular matrix (ECM)‐remodeling cancer‐associated fibroblasts (CAFs). Intercellular network analysis indicated that these ECM‐remodeling CAFs are predicted to interact with myeloid and T cells via the COLLAGEN‐ CD44 and MIF-CD74 pathways, consistent with an immunosuppressive role. Furthermore, qRT‐PCR analysis confirmed the significant upregulation of LUM , SOX9 , GPC3 , and the CAF marker POSTN in clinical GC tissues. Conclusion The proposed four‐gene signature accurately stratifies GC prognosis and mechanistically links lactylation and anoikis programs to a specific ECM‐remodeling CAF subset. These CAFs orchestrate an immunosuppressive niche, thereby providing biologically interpretable risk stratification and novel therapeutic insights for high‐risk patients.
Background:Osteoporosis and osteosarcoma impose a substantial and rising global health burden, yet their transcriptomic landscapes have not been systematically compared. We performed a multiomics characterization integrating bulk transcriptomics, coexpression network analysis, immune deconvolution, single-cell RNA sequencing, and diagnostic classifier construction. Methods:Bulk transcriptomic profiling was performed using GSE56815 (circulating blood monocytes, n = 80; 40 low-BMD/40 high-BMD) and the TARGET-OS database (n = 88 osteosarcoma tumor samples; DNA methylation n = 86; copy number n = 81). Differential expression was assessed by Welch's t-test with Benjamini-Hochberg correction. Coexpression modules were identified by hierarchical clustering of the Top 2000 most variable probes with dynamic tree cutting. A logistic regression classifier was constructed from the Top 20 differentially expressed probes using fivefold-stratified cross-validation. Single-cell RNA sequencing of an osteosarcoma tumor specimen (GEO accession GSM9030790) was processed with SVD-based dimensionality reduction and K-means clustering (k = 8; n = 3658 cells passing QC). In vitro validation was conducted by qRT-PCR in MC3T3-E1 osteoblast precursor cells and RAW264.7 osteoclast precursor cells. Results:Differential expression analysis identified 4347 probe sets at nominal p < 0.05, of which 604 remained significant after Benjamini-Hochberg correction. Eight coexpression modules were identified; module M0 showed a weak positive association with low-BMD status (r = 0.184), which did not reach statistical significance. Six hub genes-SP7/Osterix, RUNX2, BMP2, WNT5A, DKK1, and SOST-were examined by qRT-PCR: SP7, RUNX2, BMP2, and WNT5A showed significant downregulation (p < 0.01), whereas DKK1 showed significant upregulation (p < 0.01) and SOST showed a nonsignificant trend toward upregulation under osteoporosis-simulating conditions. The diagnostic classifier achieved a moderate AUC of 0.693 (fivefold CV range: 0.48-0.91), with the lower bound falling below the random classifier baseline (AUC = 0.5), indicating limited discriminative capacity in some data partitions. scRNA-seq resolved eight transcriptionally distinct cell clusters in the osteosarcoma specimen (3658 cells passing QC). Conclusions:This integrative multiomics study provides a comprehensive transcriptomic comparison of osteoporosis and osteosarcoma, identifies BMD-associated coexpression modules with experimentally validated hub genes, and establishes a reusable analytical framework for biomarker discovery in metabolic and malignant bone disease. However, the weak module-trait correlations, subthreshold classifier performance in some cross-validation folds, and the use of circulating blood monocytes to study bone-specific gene expression warrant cautious interpretation of the findings.
Background:Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study is aimed at establishing an ubiquitin-proteasome system (UPS)-related prognostic model and investigating its link to immune infiltration and therapy response. Materials and Methods:GBM datasets were obtained from public databases. Ubiquitin-proteasome system-related genes (UPSGs) were identified from literature. Consensus clustering defined UPS-based GBM subtypes. Differentially expressed genes (DEGs) were screened, and a prognostic model was constructed using univariate Cox, least absolute shrinkage and selection operator (LASSO), and stepwise regression. The model's performance was validated using survival analysis and time-dependent receiver operating characteristic (ROC) curves. Immune infiltration was assessed using single-sample gene set enrichment analysis (ssGSEA), TIMER, and ESTIMATE. Drug sensitivity was assessed by correlating the half-maximal inhibitory concentration (IC50) of candidate drugs with the risk score. Single-cell RNA sequencing data were used to characterize UPSG expression across distinct cell subpopulations in GBM. For in vitro validation, key UPSGs were silenced in GBM cell lines, and cell proliferation, migration, and invasion were measured using Cell Counting Kit-8 (CCK-8), wound healing, and Transwell assays, respectively. Results:Two UPS-related GBM subtypes were identified. Six genes (IGFBP6, CTSD, SPAG4, ZNF560, COL22A1, and HOXC13) formed the prognostic model, where high Riskscore indicated poor survival. High Riskscore correlated with greater immune infiltration, including CD8+ T cells and macrophages. IC50 values of 24 drugs were significantly associated with Riskscore. Single-cell analysis revealed seven GBM subpopulations; notably, COL22A1 was enriched in MES-like cells, and CTSD in macrophages. IGFBP6 promoted GBM cell proliferation, migration, and invasion. Conclusion:This study establishes a UPS-based prognostic model for GBM that links immune infiltration and drug sensitivity, providing potential biomarkers and therapeutic targets for GBM.
Background:Postinfarction cardiac repair is orchestrated by macrophages that undergo sequential polarization from proinflammatory M1 to reparative M2 states. These macrophage subsets regulate inflammation resolution, angiogenesis, and fibrotic remodeling in the infarcted myocardium. Tumor-associated macrophages (TAMs) in breast cancer share transcriptional features with cardiac M2 macrophages, providing an opportunity for cross-disease comparison of shared immune programs. Understanding the molecular programs common to reparative macrophages across pathological contexts may identify candidate regulatory nodes for future therapeutic investigation. Methods:Single-cell RNA sequencing data were obtained from GEO: GSE136088 (murine post-AMI cardiac macrophages), GSE176078 and GSE167036 (human primary breast cancer). Analyses included quality control, UMAP/t-SNE dimensionality reduction, graph-based clustering, diffusion pseudotime trajectory reconstruction, ligand-receptor communication analysis, principal component analysis, and cross-dataset correlation analysis. All analyses were performed in Python using scanpy with cross-species ortholog mapping in R (biomaRt, 14,892 one-to-one mouse-human orthologs) with downstream analyses in Python (scanpy). Results:Post-QC datasets (GSE136088: ~14,900 cells; GSE176078: 100,064 cells; and GSE167036: 49,141 cells) were analyzed. UMAP and t-SNE resolved multiple macrophage and immune subpopulations across all three datasets. Diffusion pseudotime analysis reconstructed a sequential activation trajectory with an M1/M2 fate bifurcation. A 7-gene coexpression module (Spp1, Mif, Tgfb1, Arg1, Il10, Tnf, and Mrc1) was identified in cardiac macrophages and showed partially conserved expression patterns in breast cancer macrophage populations (GSE176078: Pearson r = 0.72; GSE167036: 6 of 7 genes detected, ARG1 not detected). Ligand-receptor communication analysis identified MIF-ACKR3, SPP1-CD44, and TGFB1-TGFBR2 as shared ligand-receptor pairs mediating macrophage-stromal communication in both cardiac and tumor microenvironments. Cross-dataset Pearson correlation of the 7-gene module between cardiac macrophages and breast cancer macrophage populations reached r = 0.72 (GSE176078) and r = -0.07 (GSE167036, n.s.). Conclusion:This single-cell transcriptomic analysis characterizes macrophage polarization programs in postinfarction cardiac repair and evaluates their partial conservation in breast cancer. The identified 7-gene module showed moderate cross-dataset correlation with GSE176078 (r = 0.72) but was not replicated in GSE167036, where M2-polarized macrophages were not detected. The MIF-ACKR3 signaling axis represents a computationally derived hypothesis for future experimental validation. These findings highlight both shared features and context-dependent differences in macrophage polarization across disease states, underscoring the need for functional studies in paired cardiac and tumor models.
Background:Osteosarcoma is the most prevalent primary malignant bone tumor predominantly affecting children and adolescents, yet prognosis for metastatic disease remains dismal. Understanding the cellular complexity within the tumor microenvironment is essential for developing targeted therapeutic strategies. Methods:We performed comprehensive single-cell RNA sequencing analysis on an osteosarcoma tissue sample (GSM4952363) using the Seurat pipeline (v4.3.0). Following rigorous quality control (200-6000 genes per cell, < 15% mitochondrial reads), cells were filtered for downstream analysis. Dimensionality reduction (PCA and UMAP) and unsupervised clustering (Louvain algorithm, resolution = 0.8) identified seven distinct cellular clusters. Differential expression analysis (Wilcoxon rank-sum test, |log₂FC| > 0.25, adjusted p < 0.05) identified cluster-specific markers, while Gene Ontology and KEGG pathway enrichment analyses (clusterProfiler, adjusted p < 0.05) revealed functional programs. Four candidate genes (F11, ACRP2, LEPR, and POSTN) were selected for validation by quantitative real-time PCR (mRNA level) and ELISA (protein level) in MG-63 osteosarcoma cells compared to hFOB 1.19 normal osteoblasts. Results:Single-cell transcriptomic profiling identified seven distinct cellular clusters within the osteosarcoma microenvironment, including macrophages (Cluster 0, 28.0%), osteoblasts (Cluster 1, 24.2%), fibroblasts (Cluster 2, Fibro_COMP, 14.7%), proliferating cells (Cluster 3, 12.3%), osteoclasts (Cluster 4, 11.6%), monocytes (Cluster 5, 6.3%), and T cells (Cluster 6, 2.9%). Functional enrichment analysis highlighted activation of PI3K-Akt signaling, focal adhesion, and extracellular matrix organization as core pathways. qRT-PCR validation (mRNA level) demonstrated that F11 was significantly downregulated (0.31 ± 0.04 vs. 1.00 ± 0.07, 69% reduction, p < 0.001), whereas ACRP2 (2.87 ± 0.33-fold), LEPR (4.52 ± 0.48-fold), and POSTN (6.23 ± 0.57-fold) were significantly upregulated (all p < 0.001). ELISA validation (protein level) confirmed consistent trends: F11 protein decreased by 65% (0.35 ± 0.05 vs. 1.00 ± 0.08, p < 0.001), whereas ACRP2 (2.64 ± 0.29-fold), LEPR (4.18 ± 0.44-fold), and POSTN (5.89 ± 0.53-fold) protein levels were elevated (all p < 0.001). Among the four candidates, POSTN exhibited the most pronounced changes at both mRNA and protein levels, suggesting its involvement in osteosarcoma matrix remodeling. Conclusions:This study provides a single-cell transcriptomic atlas of the osteosarcoma microenvironment, revealing substantial cellular heterogeneity. The differentially expressed genes F11, ACRP2, LEPR, and POSTN represent candidate biomarkers that warrant further investigation for their potential roles in osteosarcoma biology and as putative therapeutic targets.
BackgroundGastric cancer (GC) remains one of the most prevalent malignancies worldwide, with cancer stem cells (CSCs) emerging as critical drivers of tumor progression, therapeutic resistance, and recurrence. Sevoflurane, a commonly used volatile anesthetic in surgical oncology, has been implicated in modulating cancer cell biology through various molecular mechanisms. However, the effects of sevoflurane exposure on GC stem cell gene expression profiles and the tumor microenvironment remain poorly understood at single-cell resolution. Therefore, this study is aimed at characterizing the transcriptional landscape of the GC microenvironment and directly assessing sevoflurane effects on GC cell phenotype through in vitro experiments using single-cell RNA sequencing (scRNA-seq) technology.MethodsscRNA-seq data from the publicly available GEO dataset GSE134520, comprising 13 gastric antral mucosa biopsies from 9 patients across premalignant and early malignant disease stages, were reanalyzed. Comprehensive quality control, normalization, batch effect correction (Harmony), and dimensionality reduction were performed using the Seurat package. Cell type annotation was conducted through marker gene expression analysis and reference-based approaches. Differential expression analysis was performed to identify genes significantly altered during GC progression, with particular focus on pathways known to be modulated by volatile anesthetics including sevoflurane. Cell-cell communication analysis, trajectory inference, gene regulatory network analysis, and pathway enrichment analysis were conducted to investigate molecular mechanisms relevant to GC progression and anesthetic-cancer interactions. Specific focus was placed on epithelial-mesenchymal transition (EMT), stemness signatures, and immune cell interactions. In addition, flow cytometry was performed in AGS GC cells to validate sevoflurane-associated changes in CD44 and E-cadherin expression.ResultsThrough comprehensive single-cell analysis, we identified 14 distinct cellular populations including tumor epithelial cells, cancer-associated fibroblasts (CAFs), CD4+ T cells, CD8+ T cells, macrophages, B cells, NK cells, endothelial cells, and normal epithelial cells within the GC microenvironment. Quality control metrics demonstrated high-quality data with appropriate filtering parameters. Principal component analysis revealed that approximately 15 principal components captured 80% of the variance. Differential expression analysis identified key genes including AREG, MUC5AC, S100P, CLDN18, and GPX2 significantly upregulated in GC epithelial cells, overlapping with pathways previously reported to be modulated by sevoflurane. Pathway enrichment analysis revealed significant associations with interferon response, EMT, hypoxia response, PI3K-AKT signaling, and Wnt signaling pathways. Trajectory analysis uncovered dynamic transitions of cell states along differentiation pathways, with distinct branching patterns indicating cell fate decisions. Cell-cell communication analysis revealed complex interaction networks involving VEGFA-KDR, TGFB1-TGFBR2, and CD274-PDCD1 signaling axes. Gene regulatory network analysis identified transcription factors including MYC, TP53, FOXO1, and STAT1 as key regulators of GC progression-associated gene programs. Flow cytometry further showed that sevoflurane exposure increased CD44-positive cells and decreased E-cadherin-positive cells in AGS cells, supporting stemness- and EMT-related phenotypic alterations. ConclusionIn conclusion, we have characterized the transcriptional landscape of the GC microenvironment and identified EMT, stemness, and immune signatures relevant to the perioperative anesthetic context of sevoflurane exposure, with EMT activation, stemness enhancement, and immune modulation emerging as critical features. The flow cytometry results provide preliminary experimental support for sevoflurane-associated modulation of CD44- and E-cadherin-related cellular phenotypes. These findings provide insights into the molecular mechanisms underlying anesthetic-cancer cell interactions and identify potential therapeutic considerations for perioperative cancer management. The identified signaling pathways may serve as targets for optimizing anesthetic strategies in GC surgery. Additionally, we speculate that modulating sevoflurane-induced pathways may influence tumor behavior and treatment outcomes in GC patients.
Background:Colorectal cancer (CRC), the third most common cancer globally, is a leading cause of cancer-related mortality. Helicobacter pylori infection, a risk factor for CRC, promotes carcinogenesis via inflammation and microbiota disruption. Long noncoding RNAs (lncRNAs), such as ATP11A-AS1, regulate key processes in CRC progression, including proliferation and invasion. However, the role of ATP11A-AS1 in CRC and its association with H. pylori infection remain underexplored. This study quantified ATP11A-AS1 expression in CRC tumors compared with adjacent nontumor tissues and investigated its relationship with H. pylori infection and clinicopathological features. Methods:Total RNA was extracted from 100 paired tumor and adjacent nontumor tissues collected from CRC patients (53 males, 47 females, mean age: 56 ± 6.72). Following cDNA synthesis, ATP11A-AS1 expression was measured via qRT-PCR. Associations with clinicopathological factors (age, gender, tumor site, histology, stage, lymph node metastasis, H. pylori status, family history of CRC, alcohol consumption, and diabetes) were analyzed using Mann-Whitney tests. Multivariate binary logistic regression, adjusted for age, sex, and tumor site, was applied to clinicopathological outcomes. Results:ATP11A-AS1 expression was significantly upregulated in tumor tissues compared with nontumor tissues (p < 0.0001). Among 59 H. pylori-positive patients, expression was higher (p = 0.036). No significant associations were found with other features. Multivariate analysis confirmed no independent associations (all p > 0.05). ROC curve analysis yielded an AUC of 0.69, with sensitivity of 63% and specificity of 68%, indicating that ATP11A-AS1 is a weak biomarker for CRC detection. Conclusion:ATP11A-AS1 is overexpressed in CRC and associated with H. pylori infection, suggesting a potential role in CRC pathogenesis. Further studies are needed to elucidate its molecular mechanisms and clinical significance in H. pylori-related CRC.
Background: Cardiac rhabdomyomas (CRs) are one of the typical phenotypes of tuberous sclerosis complex (TSC) diseases. Patients with CR could present various phenotypes and different severities. TSC1 and TSC2 are candidate genes for TSC. The genotype and phenotype relationship of the CR phenotype in TSC is unknown. Methods: TSC1 and TSC2 pathogenic and likely pathogenic variants from the HGMD, ClinVar, and LOVD databases were identified (last date: 2024.12.01). After critical exclusion criteria and pathogenicity reanalyses, statistical analyses were performed for enrichment evaluation. Results: In this study, 1250 variants of TSC1 were finally included, and 26 variants (20.8%) were reported to cause the CR phenotype. In Exon 15, 5.1% of them were CR-related (p < 0.001), and 7.1% of them were enriched in Exon 18 (p = 0.008). After adjusting by the Benjamini-Hochberg FDR method, the FDR-adjusted p values were still significant for Exon 15 (p = 0.006) and Exon 18 (p = 0.028). Considering the size of each exon, there is no significant enrichment by Poisson model analysis. We included 2690 variants of TSC2; 117 variants (4.3%) were reported to cause the CR phenotype. In Exon 41, 12% of variants were CR-related (p = 0.003). The FDR-adjusted p value was not significant for Exon 41 (p = 0.11). Considering the size of each exon, in Poisson model analysis, there are significant enrichments detected in Exon 37 (IRR = 2.72 [1.42, 5.19], p = 0.003), Exon 38 (IRR = 3.26 [1.65, 6.44], p = 0.001), and Exon 41 (IRR = 5.79 [3.11, 10.77], p < 0.001). After adjusting by the Benjamini-Hochberg FDR method, significant differences remained in these three exons. Conclusion: CR phenotypes demonstrated partial enrichment in specific exons, highlighting the importance of exon-level interpretation during genetic counseling.
BackgroundGallbladder cancer (GBC) exhibits a complex interplay with immune infiltration. This study is aimed at identifying exploratory immune-associated candidate genes in GBC.MethodsWe analyzed gene expression datasets (GSE76633 and GSE74048) to identify differentially expressed genes (DEGs). Feature genes were selected through weighted gene coexpression network analysis (WGCNA), intersection with immune-related gene sets, and machine learning approaches. A nomogram was constructed, and its performance was evaluated using receiver operating characteristic (ROC) analysis. Alterations in candidate genes were validated by qRT-PCR in clinical samples.ResultsAmong 3076 DEGs, we identified 11 immune-related DEGs, which were further narrowed down to three feature genes: UNC93B1, MARCO, and PIK3R1. The resulting nomogram demonstrated high apparent predictive accuracy in the training cohort. UNC93B1 and PIK3R1 showed high apparent accuracy in the training cohort, with ROC AUCs reaching 1.000 for UNC93B1, whereas validation cohort AUCs reached up to 0.970 for PIK3R1. UNC93B1 expression correlated positively with T helper 2 (Th2) cells and negatively with monocytes, T follicular helper (Tfh) cells, and neutrophils, suggesting a potential association with a Th2-skewed and myeloid/Tfh-imbalanced tumor microenvironment, although these associations require further validation. In contrast, MARCO and PIK3R1 exhibited opposing correlation patterns. PIK3R1 showed database-derived associations with several compounds. qRT-PCR in three paired clinical samples showed expression patterns consistent with the GEO datasets.ConclusionUNC93B1, MARCO, and PIK3R1 represent exploratory immune-associated candidate genes in GBC and are associated with immune cell infiltration. Their distinct immune-related correlation patterns, including the association of UNC93B1 with Th2 enrichment and myeloid/Tfh imbalance, are hypothesis-generating and require further biological and functional validation.
Medicinal plants serve as invaluable sources of bioactive compounds, yet the molecular basis of their secondary metabolite biosynthesis remains largely unexplored. Piper chaba Hunter, an important but understudied member of the Piperaceae family, is known for its pharmacologically active alkaloids, particularly piperine. To our knowledge, this is the first de novo transcriptomic profiling of spike, leaf, and root tissues of P. chaba to uncover the genetic pathways regulating its metabolite production. Piperine, a major bioactive compound, was quantified using UPLC. The highest concentration was observed in the spike (331.3 mg/g), followed by the root (10.3 mg/g) and leaves (2.82 mg/g). High-quality RNA sequencing of leaves, roots, and spikes using next-generation sequencing (NGS) generated 228,481 transcripts, and 184,574 unigenes were identified after redundancy removal. Coding sequences (CDSs) derived from these unigenes were annotated using BLASTX and KEGG databases, which highlighted significant metabolic pathways, including those related to piperine biosynthesis. Thirteen candidate genes potentially associated with the piperine biosynthetic pathway were identified based on transcriptome annotation and pathway analysis. Validation of nine selected genes, including farnesyl pyrophosphate synthase and piperic acid synthase, was performed through qRT-PCR using the 2-ΔΔCt method, supporting their expression patterns potentially associated with piperine and related metabolite biosynthesis. Functional annotation categorized the CDS into Gene Ontology domains, with transcription factors such as bHLH and NAC families playing prominent roles in metabolic regulation. Additionally, 5050 SSRs were identified, offering potential markers for genetic studies. This pioneering study establishes a molecular framework for understanding the biosynthetic pathways of P. chaba, providing valuable insights for its application in sustainable medicine and agriculture.
Background:Hepatocellular carcinoma (HCC) is a genomically heterogeneous malignancy with substantial variability in prognosis and therapeutic response. Pseudouridine (Ψ) modification is an evolutionarily conserved RNA modification involved in RNA structure stabilization and translational regulation; however, the genomic and functional relevance of pseudouridine modification-related genes (PDGs) in HCC remains poorly defined. Methods:Transcriptomic datasets and matched clinical information were collected from TCGA, GEO, and ICGC. Using feature-selection procedures and survival modeling, we established a pseudouridine-related risk signature. We next compared risk groups for genomic change, pathways, immune traits, and predicted drug response. Selected genes were tested in HCC cells by qPCR and CCK-8. Results:The PDGs signature split HCC patients into high- and low-risk groups with different survival and remained prognostic after adjustment. The groups also differed in mutation, pathways, immunity, and chemotherapy response. DKC1 and PUS1 knockdown reduced proliferation. Conclusions:This study identifies a PDGs signature linked with prognostic heterogeneity in HCC, supported by computational and experimental evidence.
Background:Early-onset high myopia (eoHM) is a highly heritable ocular disorder, with pathogenic variants in the X-linked ARR3 gene (which encodes cone arrestin) being associated with a female-limited form of eoHM. However, the clinical manifestations of early truncating variants in ARR3 and their potential impact on the classic sex-limited pattern remain insufficiently understood. Methods:This study examined individuals with eoHM from a multigenerational Chinese family, utilizing whole-exome sequencing, Sanger validation, and segregation analysis to identify candidate variants. In silico predictions and protein structure modeling were conducted to assess the variant's pathogenicity. Furthermore, in cultured cells, quantitative real-time PCR, Western blotting, and fluorescence microscopy were used to evaluate ARR3 transcript expression and ARR3-related immunodetected signal after transfection with wild-type or mutant ARR3 constructs, thereby providing a preliminary functional assessment of the variant. Results:We discovered a novel frameshift variant, c.721delT (p.Y241Ifs∗3), which cosegregated with eoHM in the family and was absent in public population databases. Affected individuals, including hemizygous males, exhibited early-onset, high-degree myopia and fundus changes consistent with pathologic myopia. Functional assays in ARPE-19 cells showed that the mutant construct was associated with markedly reduced ARR3 mRNA expression and decreased ARR3 immunoreactivity compared with the wild-type construct. Because no direct cellular injury assay was performed, these in vitro findings were interpreted as preliminary expression-level evidence supporting a likely loss-of-function mechanism. Conclusion:The novel ARR3 frameshift variant c.721delT (p.Y241Ifs∗3) is likely pathogenic for eoHM and may alter the previously understood female-limited inheritance pattern. These findings expand the known mutation spectrum of ARR3 and enhance understanding of the role of cone arrestin dysfunction in the development of eoHM.
BackgroundHead and neck squamous cell carcinoma (HNSC) exhibits substantial prognostic and microenvironmental heterogeneity. However, the integrated prognostic relevance of synergistic immune and inflammatory signatures in HNSC remains fully elucidated.MethodsWeighted gene coexpression network analysis (WGCNA) was integrated with curated immune- and inflammation-related gene sets to identify key tumor-associated candidate genes. A crucial phenotypic module exhibiting the strongest positive correlation with tumor status was prioritized, yielding six overlapping candidate genes. Utilizing the TCGA-HNSC, GSE65858, and GSE41613 cohorts, we systematically compared multiple machine learning algorithms to construct a robust immune-inflammation score (IIS), subsequently evaluating its prognostic efficacy and biological relevance.ResultsThe random survival forest model outperformed other algorithms and was utilized to establish the IIS. An elevated IIS was consistently predictive of inferior survival and served as an independent prognostic indicator. Furthermore, the IIS significantly correlated with specific immune infiltration patterns, immune checkpoint expressions, TIDE-related features, tumor microenvironment scores, and distinct genomic mutation profiles, including tumor mutation burden. Notably, CSF2, IL1R2, and IL20RB were identified as pivotal model constituents, displaying cell type-specific and spatially discrete expression trajectories.ConclusionsThe proposed IIS constitutes a robust, clinically relevant prognostic biomarker for HNSC, capturing the profound immune and genomic heterogeneity inherent in the disease.
Exosomes play a crucial role in tumor progression. However, reliable exosome-related biomarkers for predicting prognosis in gastric cancer (GC) remain scarce. This study is aimed at developing an exosome-related gene risk signature (ERGRS) to predict the survival outcomes and immunotherapy sensitivity in GC patients. RNA sequencing data from exoRbase and TCGA datasets were analyzed to identify differentially expressed exosome-related genes associated with survival in GC. A multivariate Cox regression model was used to construct the ERGRS and a prognostic nomogram. The ERGRS ' s predictive performance was assessed using the Kaplan-Meier and receiver operating characteristic (ROC) curve analyses in both training and validation datasets. Furthermore, the relationship between the ERGRS risk score and clinical features, copy number variation, and immunotherapy sensitivity was explored through data mining. Western blotting was performed to validate protein expression levels of key exosome-related genes in GC tissues. Four exosome-related genes-TRAF2, ASCL2, NOX4, and MMRN1-were identified as independent prognostic factors. GC Patients with low-risk scores showed significantly better survival outcomes. Univariate and multivariate Cox regression analyses confirmed the ERGRS as an independent predictor of survival in GC. A prognostic nomogram incorporating risk score, age, and tumor stage was developed to effectively predict patient survival. Immune checkpoint analysis suggested that patients with low-risk scores may respond better to immunotherapy. The ERGRS represents a promising tool for prognostic prediction and guides the clinical care of GC patients.
The E2F family gene is one of the most important G1-S checkpoint regulatory transcription factors to monitor and regulate the accurate DNA replication and progression of the cell cycle in eukaryotes. Expression of this gene in multiple environmental factors has been reported in diversified plant species. However, characterization and investigation of the putative influential role of this family gene in Brassica species were not reported. In addition to expression profiling and mRNA target analysis, we have comprehensively identified and characterized 14 BrE2F/DP transcription factors from the data in available databases of Brassica rapa and compared them with five other species. Six pairs of segmentally duplicated BrE2F/DP paralogous genes were identified in this study, with Ka/Ks values of < 1.0. The findings indicated their evolution through segmental duplication across the chromosome after the Brassica splits from Arabidopsis and functional similarities with Brassica oleracea proteins. Most of the stress-responsive microRNA (miRNA) targeted BrE2F/DP5, BrE2F/DP12, BrE2F/DP1, and BrE2F/DP8. Cis-element composition and diversified functional expressions indicated their involvement in biological pathways for adaptation under multiple stress resistances, including cell cycle progression during growth and development. The findings provided comprehensive information on the multifunctional roles of the E2F/DP family in Brassica spp., importantly, biotic and abiotic stress resistance.
The cytochrome P450 (CYP) enzyme superfamily is essential for xenobiotic metabolism, detoxification pathways, and the regulation of cellular homeostasis. Genetic variability in CYP genes, together with environmental factors, contributes substantially to interindividual and interethnic differences in drug metabolism and therapeutic response. However, Indigenous populations remain largely underrepresented in pharmacogenomic and population genomics studies, limiting the generalizability of current knowledge. This study is aimed at characterizing genetic variation within CYP genes in Native American populations from the Brazilian Amazon and comparing the observed patterns with those of the five continental populations represented in the 1000 Genomes Project. Whole-exome sequencing data were generated for 64 Indigenous individuals belonging to 12 distinct Amazonian ethnic groups. Variants were identified and annotated across CYP family genes, and their predicted functional impacts were assessed. A total of 506 genetic variants were detected across 63 CYP genes. Variants predicted to have modifying effects constituted the majority (n = 300), followed by variants of moderate (n = 90), low (n = 95), and high impact (n = 21). Importantly, 11 variants appear to be novel, with no previous annotation in dbSNP or other publicly available variant databases. Comparative analyses revealed distinct variant distributions relative to global reference populations. To our knowledge, this is the first comprehensive exome-based characterization of CYP gene variation in Indigenous populations from Northern Brazil. These findings provide an important genomic resource for an underrepresented population and offer a foundational framework for future pharmacogenomic research, with potential implications for the development of personalized and culturally appropriate precision medicine strategies in the Brazilian Amazon.
