Background Immunoglobulin A nephropathy (IgAN) is one of the most prevalent primary glomerular diseases globally. This study is aimed at identifying candidate biomarkers associated with vascular calcification‐related signaling pathways in IgAN and at investigating their potential biological associations. Methods This was an observational bioinformatics study based on public transcriptomic datasets. Vascular calcification‐related genes (VCRGs) were defined based on selected KEGG signaling pathways. Data were obtained from public databases. Candidate biomarkers were identified via differential analysis, machine learning, and cross‐compartment external expression assessment. Enrichment analysis, immune infiltration, regulatory networks, computational drug prediction, and computational N6‐methyladenosine (m6A) site prediction were used to explore the potential biological context of the candidate biomarkers. Results Differential analysis identified 348 differentially expressed genes (DEGs). By intersecting these DEGs with VCRGs, 30 candidate genes were obtained. JUN , FOS , and COL1A2 were finally identified as candidate biomarkers, and these genes were mainly associated with the “Nagashima EGF signaling” and “Nagashima NRG1 signaling.” Six differential immune cell types (e.g., M2 macrophages) differed significantly between the IgAN and control groups. Spearman analysis showed that COL1A2 was negatively correlated with resting natural killer (NK) cells (cor = −0.57, p < 0.05); and FOS was positively correlated with resting NK cells (cor = 0.72, p < 0.01). BRD4, MAZ, and CTCF were computationally predicted as shared candidate transcription factors (TFs). A regulatory association involving hsa‐miR‐139‐5p and FOS was also predicted. Troglitazone was predicted to target JUN. High‐confidence m6A modification sites were computationally predicted in all three candidate biomarkers. These findings were considered exploratory and hypothesis generating. Conclusion JUN , FOS , and COL1A2 were identified as IgAN‐associated candidate biomarkers linked to vascular calcification‐related signaling pathway genes, providing exploratory biological insights into the potential molecular links between IgAN and calcification‐related signaling.
Background Periodontitis (PD) and oral squamous cell carcinoma (OSCC) have been investigated in relation to inflammation, oral microbial alterations, and iron/redox biology. Whether ferroptosis‐associated expression patterns overlap across independently sampled periodontal and cancer cohorts remains uncertain. We characterized these patterns without presuming that the available datasets represent matched individuals or a temporal disease sequence. Methods We analyzed gingival single‐cell RNA‐sequencing data (GSE171213), an OSCC discovery expression cohort (GSE30784), and an independent oral tongue squamous cell carcinoma validation cohort (GSE13601). Periodontal microbial functional gene profiles (GSE54290) and a cross‐study OSCC 16S abundance resource were examined separately. Analyses included cell annotation, KEGG ferroptosis gene‐set scoring, sample‐ and cell type‐level comparisons, differential expression, external transcriptomic signature validation, microbial diversity and abundance analyses, and LPS‐stimulated cell‐line RT‐qPCR and ELISA measurements. The transcriptomic and microbial cohorts were not linked at the individual‐patient level. Results Quality control retained 34,342 cells from 12 gingival samples (four healthy controls [HCs], five PD, and three treated periodontitis [PDT]), assigned to 11 annotated cell populations. Mean sample‐level ferroptosis gene‐set scores were 0.081 in HC, 0.114 in PD, and 0.076 in PDT; these descriptive scores do not measure ferroptotic cell death. SAT1 showed a positive PD‐versus‐HC association in cell type pseudobulk analysis (log2 fold change, 0.546; false discovery rate [FDR], 0.012) and positive effects across all three transcriptomic cohorts. A discovery‐derived ferroptosis‐associated OSCC signature achieved an area under the receiver operating characteristic curve of 0.933 in GSE13601. Periodontal functional profiling identified 25 advanced PD‐associated annotations (FDR < 0.05), including lower ferrochelatase signals. Cross‐study OSCC microbiome analysis found 293 diagnosis‐associated features after project adjustment; project‐held‐out classification yielded an AUC of 0.674. Following lipopolysaccharide stimulation, GPX4, SLC7A11, and FTH1 increased and ACSL4 decreased at the measured mRNA and protein levels in gingival fibroblasts and THP‐1‐derived macrophages. Conclusions Independently sampled periodontal and OSCC cohorts show ferroptosis‐associated transcriptomic changes, with SAT1 emerging as a cross‐cohort candidate. Parallel microbial datasets identify disease‐associated functional gene and taxonomic differences but do not demonstrate within‐patient host–microbe interactions or causal mediation. LPS‐exposed cell‐line measurements show expression changes in selected ferroptosis‐associated markers, not ferroptotic cell death or functional resistance. The results are exploratory associations; in the absence of direct ferroptosis assays, paired longitudinal cohorts, and prospective clinical validation, they cannot establish a disease‐progression mechanism, clinically deployable biomarker or therapeutic efficacy.
Background Melanoma is characterized by high malignancy and complex immune microenvironment heterogeneity. A robust prognostic framework that integrates tumor cell state diversity with clinical outcome remains lacking. Methods Integrative scRNA‐seq analysis of public melanoma datasets was performed using Harmony batch correction, unsupervised clustering, and Monocle 3 pseudotime trajectory reconstruction to characterize tumor cell state transitions. Pseudotime‐dynamic genes were subjected to univariate Cox screening across four independent cohorts (GSE19234, GSE22153, GSE65904, TCGA), followed by LASSO‐Cox regression to construct a multigene prognostic model. Validation encompassed immune infiltration estimation, pathway activity scoring, tumor mutational burden (TMB) analysis, and protein‐level expression assessment. Results scRNA‐seq identified 14 major cell populations, within which tumor cell reclustering resolved seven functional states: melanocytic, neural crest‐like, MHC‐II high, stress/transitional, proliferating, IFN‐stimulated, and invasive. Pseudotime analysis reconstructed a directional evolution trajectory originating from the melanocytic state, with the stress/transitional state acting as a central hub. Gene module enrichment further delineated the core transcriptional programs of each state. A seven‐gene prognostic model (GBP4, ITGB4, GBP2, TNFSF13B, TSC22D3, CIITA, TYRP1) achieved consistent survival stratification across all four cohorts (all p < 0.01) and outperformed 76 published signatures by C‐index ranking. High‐risk patients exhibited an immunosuppressive microenvironment, activation of protumorigenic pathways, and broad suppression of the cancer–immunity cycle. Risk score and TMB were statistically independent, yet their combined stratification significantly refined prognostic resolution ( p < 0.001). TYRP1, the sole risk gene in the model, demonstrated tumor‐specific protein upregulation by IHC and consistently predicted poor overall survival across three independent transcriptomic cohorts. Conclusion This study presents an integrated single‐cell and multicohort prognostic framework for melanoma, revealing the mechanistic link between tumor cell state evolution and immune microenvironment remodeling, and establishing TYRP1 as an independent prognostic biomarker to guide precision immunotherapy decision‐making.
Background Lung cancer remains a leading cause of cancer‐related mortality worldwide. This study provides a comprehensive characterization of the transcriptomic landscape, cellular composition, and immune architecture of lung adenocarcinoma using multiomics approaches. Methods Gene expression data were obtained from GEO (GSE131907) and TCGA‐LUAD ( n = 526). LRCD gene sets were curated from GeneCards, MSigDB, and published literature. Analyses included differential expression profiling, single‐cell RNA sequencing (GSE131907, n = 11,376 cells), GSEA, and cell‐cell communication mapping. A LASSO‐based prognostic model with 10‐fold cross‐validation was built and validated across three independent cohorts using ROC and Kaplan–Meier analyses. Results Fourteen prognostically relevant genes were identified, including protective factors (BTK, CLNK, CTSG; HR < 1) and risk factors (CTSV, HMOX1; HR > 1). Single‐cell analysis profiled 11,376 cells from one primary tumor and one normal lung sample. The prognostic model achieved moderate‐to‐good discrimination (AUC : 0.545–0.692) and effectively stratified patients by risk. Model genes correlated significantly with immune cell infiltration and checkpoint expression across risk groups, and functional enrichment highlighted immune trafficking, cytokine signaling, and cell adhesion pathways. Conclusions This multiomics study identifies reliable LRCD‐based prognostic markers and offers mechanistic insights into lung adenocarcinoma biology, with potential utility for clinical risk stratification.
Background Endometrial cancer (EC) has a heterogeneous immune microenvironment, and response‐associated biology remains incompletely characterized. We used the retained EC single‐cell dataset to describe immune‐cell states and complemented it with TCGA‐UCEC, independent gut microbiome cohorts, and RL95‐2 proof‐of‐concept experiments. All responder versus nonresponder scRNA‐seq comparisons are descriptive and hypothesis generating because only one sample was available per clinical response group. Methods Single‐cell RNA sequencing (scRNA‐seq) data from EC patients undergoing anti–PD‐1 immunotherapy (GEO: GSE251923; one responder, GSM7990051, and one nonresponder, GSM7990052; 17,546 cells retained after quality control) were analyzed using Scanpy, Leiden clustering, and over‐representation analysis with GO biological process, KEGG, and MSigDB Hallmark gene sets using Enrichr with Benjamini–Hochberg correction. Bulk RNA‐seq from TCGA‐UCEC (554 tumor and 35 normal samples) was used for complementary transcriptomic analysis. Gut microbiome 16S rRNA datasets (SRA: PRJNA897842 and PRJNA833670) were analyzed as independent cohorts, and RL95‐2 qRT‐PCR experiments were interpreted as proof‐of‐concept. Results Ten major cell populations were identified in the EC immune microenvironment. In this single responder/nonresponder sample pair, the nonresponder sample showed patterns consistent with higher M2‐associated macrophage marker expression, increased T‐cell checkpoint/exhaustion marker expression, and lower cytotoxic activity scores. These observations are descriptive and cannot establish patient‐level significance. TCGA‐UCEC analyses and the independent microbiome and RL95‐2 experiments provide complementary, hypothesis‐generating evidence; no unverified classifier‐performance estimate is reported. Conclusions This study provides a hypothesis‐generating description of immune patterns in one EC responder and one nonresponder sample, complemented by TCGA‐UCEC, independent microbiome datasets, and RL95‐2 proof‐of‐concept experiments. The findings nominate candidate immune, hormonal, and microbiome‐associated pathways for further investigation but do not establish patient‐level response differences, causality, or clinically validated biomarkers.
