Atherosclerosis is a chronic inflammatory disease, and the role of mitophagy in its pathogenesis remains unclear. To address this, this study integrated atherosclerosis‐related transcriptome data and single‐cell sequencing data from the GEO database, obtained mitophagy‐related genes from MSigDB, and assessed mitophagy activity via GSEA and ssGSEA; WGCNA was used to screen disease‐ and mitophagy‐associated module genes, and their intersection with differentially expressed genes yielded 286 candidate genes. A protein–protein interaction (PPI) network and cytoHubba algorithms were subsequently used to prioritize key genes, and dual machine learning algorithms (LASSO regression and SVM‐RFE) to screen potential biomarkers, which were designated as CCR5, CD163, CSF1R, and FCGR3A. The expression patterns of all four candidates were further assessed in the independent GSE43292 dataset and were concordant with those observed in the discovery dataset. GSEA of samples stratified by candidate‐gene expression identified enrichment of gene sets related to myogenesis, allograft rejection, and inflammatory responses; candidate drug‐target associations were predicted using DSigDB, and exploratory AutoDock analysis suggested a feasible interaction between sunitinib and CSF1R. Additionally, exploratory single‐cell analysis of three atherosclerosis patients identified seven major cell populations, with CD163, CSF1R, and FCGR3A showing macrophage‐enriched expression within this dataset. Collectively, this study provides hypothesis‐generating evidence for an association between mitophagy‐related transcriptional patterns and atherosclerosis. Four candidate biomarkers (CCR5, CD163, CSF1R, and FCGR3A) were prioritized, and a potential CSF1R‐sunitinib interaction was proposed for future experimental validation.
Background Chemotherapy is the mainstay of systemic treatment for colorectal cancer (CRC), but drug resistance and tumor heterogeneity markedly limit survival. Mitochondrial metabolism is increasingly recognized as a regulator of tumor progression, immune contexture, and therapeutic response. However, the prognostic relevance of mitochondrial metabolism–related transcriptional patterns and their relationships with chemotherapy‐response signatures and immune features in CRC remain incompletely understood. Methods Clinical and transcriptomic data from The Cancer Genome Atlas colorectal cancer cohort (TCGA‐CRC) and Gene Expression Omnibus cohorts GSE17537 and GSE104645 were analyzed. A curated set of mitochondrial metabolism–related genes (MMRGs) were used to calculate an MMRG score, whereas genes upregulated in chemotherapy‐sensitive cases in GSE104645 were used to derive a drug‐sensitivity gene‐set score by single‐sample gene set enrichment analysis (ssGSEA). Weighted gene coexpression network analysis (WGCNA), differential expression analysis, univariate Cox regression, least absolute shrinkage and selection operator regression, and multivariate Cox regression were used to construct a five‐gene RiskScore. Prognostic associations were assessed using Kaplan–Meier curves, time‐dependent receiver operating characteristic curves, external validation, and subgroup analyses. Immune‐cell enrichment and pRRophetic‐predicted half‐maximal inhibitory concentration values were evaluated, and CD36 was examined using in vitro phenotypic assays. Results Tumors showed lower MMRG scores than normal tissues, and higher MMRG scores were associated with better overall survival (OS). The drug‐sensitivity gene‐set score was higher in tumors but was not significantly associated with OS. A five‐gene signature (CD36, CPA3, FCRL2, POU2F2, and S100B) generated a RiskScore that separated OS outcomes in the TCGA training, testing, and overall cohorts, as well as GSE17537, although its time‐dependent discrimination varied across cohorts and follow‐up periods. Similar survival associations were observed within subgroups defined by stage, age, and sex; however, independent or incremental prognostic value was not evaluated. High‐risk tumors exhibited altered immune‐cell enrichment, and the RiskScore was correlated with pRRophetic‐predicted drug IC50 values after Benjamini–Hochberg correction. CD36 expression was higher in SW1116 than in NCM460 cells, and its knockdown inhibited proliferation, migration, and invasion. Mitochondrial function was not directly measured in these experiments. Conclusion The five‐gene RiskScore was associated with OS, immune‐cell enrichment, and in silico drug‐sensitivity estimates in CRC. It may serve as a candidate tool for prognostic stratification, but its incremental clinical value and treatment‐predictive utility require prospective validation. The CD36 knockdown experiments support a tumor‐promoting phenotype in SW1116 cells but do not establish dependence on mitochondrial metabolic reprogramming.
To investigate the genetic causes of non‐retinal developmental eye disorders in 14 Chinese different families, we subjected participants to a specific hereditary eye disease enrichment panel (HEDEP) or whole exome sequencing. We further validated disease‐causing variants and performed co‐segregation analyses on the available family members by Sanger sequencing. We classified sequence changes according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology variant interpretation guidelines. Fourteen probands with diverse clinical diagnoses were analyzed, including microphthalmia (with or without microcornea and congenital cataract or congenital corneal opacification (CCO), four cases), anterior segment dysgenesis (ASD, six cases)—including Peter′s anomaly (PA, one case), PA and microphthalmia (one case), iridocoloboma or aniridia (with microcornea and/or congenital cataract, two cases), and primary congenital glaucoma (PCG, two cases)—congenital cataract (with CCO or microcornea, three cases), and Rubinstein–Taybi syndrome (RSTS, one case). Initial inheritance patterns for 1, 5, and 8 cases were autosomal recessive, autosomal dominant, and sporadic, respectively. We identified 17 (likely) pathogenic variants and one variant of uncertain significance (VUS) in 16 genes, including BMP4 , BCOR , PITX3 , and PAX6 . Among them, eight were novel variants, and two were de novo . We confirmed the pathogenicity of PAX6 intronic mutation using in vitro splice assays and redefined the pathogenicity of the variants of uncertain significance as likely pathogenic. Patients with developmental eye diseases typically have complex ocular and systemic phenotypes, possibly leading to significant visual impairment and high blindness rates. Identifying the genetic causes facilitates early diagnosis and effective treatment and rehabilitation strategies, thereby providing valuable insights for genetic and clinical counseling of these patients.
USP26 gene variants have been reported to be associated with male infertility, such as non‐obstructive azoospermia (NOA), oligozoospermia, and asthenoteratozoospermia, and may be associated with altered sperm sex chromosome segregation patterns. Among the reported variants, USP26 cluster variants (c.370_371insACA/c.494 T > C/c.1423C > T) represent a frequently reported variant pattern in certain populations. Rapid and convenient detection of USP26 cluster variants may serve as a powerful tool for male infertility‐associated cohorts. Here, we develop a CRISPR/Cas12a‐based assay to rapid detection USP26 variants in human samples. Candidate CRISPR RNAs (crRNAs) and RPA primers were screened and optimized, followed by incorporation of restriction enzymes to reduce wild‐type–derived amplification interference. Using controlled artificial DNA mixtures, the assay detected mutant‐template fractions down to 5% under the tested conditions. The complete workflow could be performed within 60 min using basic laboratory equipment. The results of the CRISPR/Cas12a system are consistent with Sanger sequencing in 91 male infertility research samples. Overall, our study establishes a rapid molecular detection method for detecting USP26 cluster variants and provides an alternative approach for genetic studies of male infertility‐associated cohorts.
Hepatocellular carcinoma (HCC) is characterized by extensive somatic mutation heterogeneity, yet the cellular contexts in which recurrent driver mutation–associated transcriptional programs operate remain incompletely defined. In this study, we applied a mutation‐informed multiomics framework centered on TP53 to connect somatic mutation status, bulk transcriptional programs, and hepatocyte‐like cell states at single‐cell resolution. Somatic mutation profiles and transcriptomic data from the TCGA‐LIHC cohort were used to construct directional mutation‐associated transcriptional signatures, with CTNNB1 retained as a reference driver signature and AXIN1 and ARID1A retained for descriptive comparison. These bulk‐derived transcriptional programs were then projected into single‐cell RNA sequencing data to evaluate inferred TP53 ‐associated transcriptional activity across hepatocyte‐like populations. TP53 mutation‐associated transcriptional programs were enriched in proliferation‐related pathways, DNA repair processes, and stress–response programs and were associated with adverse clinical outcomes. At the single‐cell level, inferred TP53 ‐associated transcriptional activity showed heterogeneous distribution across hepatocyte‐like populations and was preferentially enriched in putative malignant hepatocyte‐like states. Pseudobulk and functional program analyses further supported the association between TP53 ‐high signature states, cell‐cycle activity, DNA repair, stress programs, and altered tumor‐cell communication patterns. Integrated prioritization identified CDCA8 , MCM10 , and TRIP13 as representative genes associated with TP53 ‐related transcriptional programs; these genes should be interpreted as components of a TP53 ‐associated proliferative transcriptional state rather than as experimentally validated drivers. These findings provide a mutation‐informed view of transcriptional architecture in HCC and illustrate how bulk mutation‐associated signatures can be integrated with single‐cell data to improve biological interpretation of somatic variation.
