Background: Vimentin (VIM), a cytoskeletal protein implicated in tumor progression, has been associated with poor prognosis in B cell lymphomas. Alternative polyadenylation (APA), a posttranscriptional mechanism that modulates mRNA isoforms via 3⁣′UTR length changes, is frequently dysregulated in cancer. The interaction between VIM and APA in B cell lymphoma remains poorly understood.Methods: We performed RNA-seq, APA analysis (DaPars), and proteomic profiling in wild-type and VIM-knockout (VIM-KO) B cell lymphoma cells (A20 and M12). Functional assays including CCK-8, EdU, Western blot, and ARVCF overexpression were used to explore the regulatory axis involving APA and mTOR signaling.Results: VIM deletion in B cell lymphoma cells triggered widespread transcriptome remodeling, inducing 4089 APA shortening events that preferentially targeted prosurvival pathways, for example, mTORC1, G2-M checkpoint. Strikingly, RRAGA—a critical mTOR activator—underwent 3⁣′UTR shortening, concomitant with ARVCF downregulation in proteomic profiles. Functional rescue experiments demonstrated ARVCF's dual role in maintaining RRAGA 3⁣′UTR length and suppressing mTOR-EIF4G1 signaling, ultimately inhibiting lymphoma proliferation.Conclusion: This study reveals a novel VIM–ARVCF–RRAGA–mTOR axis in B cell lymphoma, linking cytoskeletal disruption to APA-mediated oncogenic signaling. VIM loss drives APA shortening and mTOR activation via ARVCF downregulation, promoting lymphoma progression. These findings offer mechanistic insight and potential targets for therapeutic intervention.

Pancreatitis is a severe and increasingly prevalent disease that affects the digestive system. Early detection and accurate diagnosis of this condition are crucial for reducing mortality rates and improving patient outcomes. Therefore, the development of novel diagnostic markers is essential for enhancing clinical management and advancing the understanding of pancreatitis. The initial phase involved applying the ssGSEA method to extract hypoxia scores from these samples. Subsequently, a thorough differential expression analysis was performed, complemented by functional assessments and various machine learning techniques designed to pinpoint critical diagnostic genes relevant to pancreatitis. From this, a robust diagnostic model was constructed and validated using a series of machine learning strategies. To further validate our results, molecular docking studies were conducted to determine the binding affinities between the identified markers and standard medications such as omeprazole and lansoprazole. Additionally, the ssGSEA methodology was leveraged to compute immune cell scores within the pancreatitis samples, thus enriching the analysis of the relationships between significant diagnostic genes and various immune cell types. Finally, the experiments of ELISA and qRT-PCR were used to verify the expression of key target genes. Through WGCNA, we identified a total of 50 genes associated with hypoxic conditions within the pancreatitis samples. Further investigations, including differential expression analysis and machine learning techniques, revealed six significant diagnostic markers for pancreatitis: RAP1GDS1, TOP2A, ADK, POLL, CD44, and CD4. The diagnostic model we developed exhibited a high accuracy level in predicting pancreatitis onset, while molecular docking analyses indicated that these six key diagnostic genes hold promise as drug targets. Moreover, the ssGSEA algorithm confirmed the relationships between these diagnostic markers and a range of immune cell populations. Ultimately, the expression levels of the identified key genes were rigorously validated through experimental techniques, reinforcing the credibility of our findings.

This study was aimed at assessing the diagnostic utility of whole genome sequence analysis in a well-characterised research cohort of individuals referred with a clinical suspicion of Cornelia de Lange syndrome (CdLS) in whom prior genetic testing had not identified a causative variant. Short-read whole genome sequencing was performed on 195 individuals from 105 families, 108 of whom were affected. 100/108 of the affected individuals had prior relevant genetic testing, with no pathogenic variant being identified. The study group comprised 42 trios in which both parental samples were available for testing (42 affected individuals and 126 unaffected parents), 61 singletons (unrelated affected individuals), and two families with more than one affected individual. The results showed that 32 unrelated probands from 105 families (30.5%) had likely causative coding region-disrupting variants. Four loci were identified in > 1 proband: NIPBL (10), ANKRD11 (6), EP300 (3), and EHMT1 (2). Single variants were detected in the remaining genes (EBF3, KMT2A, MED13L, NLGN3, NR2F1, PHIP, PUF60, SET, SETD5, SMC1A, and TBL1XR1). Possibly causative variants in noncoding regions of NIPBL were identified in four individuals. Single de novo variants were identified in five genes not previously reported to be associated with any developmental disorder: ARID3A, PIK3C3, MCM7, MIS18BP1, and WDR18. The clustering of de novo noncoding variants implicates a single upstream open reading frame (uORF) and a small region in Intron 21 in NIPBL regulation. Causative variants in genes encoding chromatin-associated proteins, with no defined influence on cohesin function, appear to result in CdLS-like clinical features. This study demonstrates the clinical utility of whole genome sequencing as a diagnostic test in individuals presenting with CdLS or CdLS-like phenotypes.

Peutz–Jeghers syndrome (PJS) is a rare autosomal dominant disorder hallmarked by mucocutaneous melanocytic macules and gastrointestinal hamartomatous polyposis associated with germline/somatic pathogenic variants in the tumor suppressor STK11. PJS is clinically heterogeneous; however, the relationship between clinical phenotype and genotype remains elusive. Here, we report a family with PJS that harbors a heterozygous STK11 whole gene deletion combined with a heterozygous variant in TP53AIP1 that segregates with mucocutaneous pigmentation in the family. RNA-seq analysis followed by qRT-PCR confirmed that the expression of STK11, TP53, and TP53AIP1 and a large fraction of p53 signaling pathway components are significantly reduced, while Wnt signaling pathway effectors are upregulated in cells from an affected individual. Our findings shed light on transcriptome-level pathway dysregulation in PJS with germline deletion of STK11. Further evaluation of mutational burden across relevant signaling pathways can likely inform disease prognosis.

BackgroundCancers are characterized by high heterogeneity. This study seeks to identify the factors driving hepatocellular carcinoma (HCC) heterogeneity to aid in prognostic stratification and inform personalized treatment approaches.MethodsWe used a computational pipeline to integrate multiomics data from HCC patients, applying 10 clustering algorithms. These results were combined with a machine learning framework to identify high-resolution molecular subtypes (MSs) and to create a robust molecular subtype–related risk score (MSRRS). Subsequent integrated bioinformatics algorithms further analyzed the heterogeneity of HCC at the level of molecular pathways, therapeutic response, and tumor microenvironment, thereby assessing potential clinical value.ResultsThrough multiomics clustering, we identified two heterogeneous MSs associated with prognosis, with MS2 exhibiting a more favorable prognostic outcome. Subsequently, we applied bootstrap resampling-based univariate Cox regression and Boruta algorithm to screen for more clinically relevant genes from the marker genes of each MS. Next, we benchmarked seven survival-related machine learning algorithms for overall survival (OS) using nested cross-validation. The hyperparameter-tuned Ridge survival model outperforms other tuned models and was therefore used to develop a robust MSRRS. MSRRS demonstrated superior performance in predicting patient OS in multiple independent HCC cohorts. Downstream analysis suggested that MSRRS has the potential to guide individualized targeted therapy, chemotherapy, and immunotherapy for HCC and to assess the tumor microenvironment. Pathway enrichment analysis identified the cell cycle as a crucial driver of heterogeneity differences between the two subtypes. Finally, we confirmed that KIF2C may be the most central MSRRS gene and demonstrated by in vitro experiments that KIF2C could promote G2/M transition in HCC cells by targeting CDK1/CCNB1/PLK1 signaling.ConclusionThe novel MSs and robust MSRRS we identified effectively exposed the heterogeneity of HCC and have the potential to predict prognosis and guide individualized precision therapy.

Cardiac arrest (CA) prevention continues to be a substantial hurdle for global public health. Although dyslipidemia and 25-hydroxyvitamin D (25(OH)D) insufficiency are recognized contributing factors for cardiovascular disease (CVD), their causal relationship with CA risk is still uncertain. Here, we explored these correlations and pinpointed possible therapeutic targets for CA prevention though Mendelian randomization (MR). Both two-sample and multivariable MR analysis methods were conducted to assess how serum lipid traits and 25(OH)D influence the susceptibility to develop CA. Nine thousand nine hundred eighty-eight participants in total from the National Health and Nutrition Examination Survey (NHANES) engaged in validating the relationship between the concentrations of 25(OH)D and cardiovascular mortality in individuals with dyslipidemia. The integration of MR with expression quantitative trait locus (eQTL) analysis enabled the identification of druggable targets, and molecular docking was used to screen small molecules, which were subsequently validated in animal models. The MR results revealed that both elevated levels of low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (ApoB), as well as triglycerides (TGs), significantly contributed to an increased CA risk (p < 0.05). Conversely, higher amounts of apolipoprotein A1 (ApoA1), high-density lipoprotein cholesterol (HDL-C), and 25(OH)D were causally contributing to a decreased risk of CA (p < 0.05). A bidirectional causal relationship was observed among LDL-C, TG, ApoB, and 25(OH)D levels. Mediation MR suggests that dyslipidemia and low 25(OH)D status could potentially elevate the CA risk through pathways involving myocardial infarction, diabetes, and hypertension. NHANES data confirmed that higher 25(OH)D were tied to decreased risks of all-cause and CVD death among those with dyslipidemia (p < 0.01). Notably, chromobox 6 (CBX6), negatively associated with CA risk (OR = 0.87, 95% CI: 0.78–0.99, p = 0.029), was determined to be a target of both sanguinarine and lycorine, which improved lipid profiles and 25(OH)D in mice. In conclusion, dyslipidemia and low 25(OH)D status are causally related to CA risk, they appear to interact, and their coexistence may confer a higher risk of CVD mortality. Compounds targeting specific genes can both improve dyslipidemia and elevate 25(OH)D levels, thereby exhibiting potential therapeutic effects for preventing CA. Overall, this study enhances our understanding of the underlying mechanisms linking dyslipidemia, 25(OH)D deficiency, and CA and offers new perspectives for prevention.

Three nonsynonymous single nucleotide variations (SNVs), rs1042602 in TYR (p.S192Y), rs1426654 in SLC24A5 (p.A111T), and rs16891982 in SLC45A2 (p.L374F), were associated with human skin pigmentation variation and may have recently undergone positive natural selection. Furthermore, these three SNVs have been reported to correlate with the risk and prognosis of melanoma. To simultaneously determine these three SNVs, a triplex fluorescent probe-based melting curve assay (FMCA) was developed. The method was validated by analyzing genomic DNA from subjects with known genotypes. For rs16891982, triplex FMCA did not allow good separation of genotypes with the initial polymerase enzyme mix used, but by changing the enzyme mix used, the three genotypes could be clearly distinguished. Using this method, we definitively genotyped these three SNVs in 93 European, 58 Tamil, 54 Sinhalese, and 52 Bangladeshi subjects. This method allows genotyping of rs1042602, rs1426654, and rs16891982 in a relatively large number of samples to perform association studies on skin pigmentation variation or melanoma risk.

BackgroundClear cell renal cell carcinoma (ccRCC) is a highly aggressive cancer with a poor prognosis. Palmitoylation, a posttranslational modification, plays a key role in regulating cancer progression and immune responses. However, its influence on ccRCC prognosis and immune therapy efficacy remains underexplored.MethodsMultiple publicly available ccRCC datasets were integrated and harmonized through batch effect correction. A prognostic model based on palmitoylation-related genes was constructed using a combination of 101 machine learning algorithms. Single-cell RNA sequencing was employed to analyze cellular heterogeneity within the tumor microenvironment. Genomic profiling, including tumor mutational burden (TMB), copy number variation (CNV), and tumor stemness, was conducted to identify genomic differences between the high- and low-risk groups. Immune infiltration levels were assessed using various algorithms to compare immune profiles across patient subgroups, while immune therapy responses were predicted using multiple prediction models. Experimental validation of ZDHHC18, a key gene in the prognostic model, was performed in ccRCC cell lines (786-O and Caki-1) to evaluate its impact on cell proliferation, migration, and invasion.ResultsThe palmitoylation-related prognostic model effectively stratified ccRCC patients into the high- and low-risk groups, with distinct differences in survival outcomes. Genomic analysis demonstrated higher TMB and CNV alterations in the high-risk group. Immune response predictions indicated that low-risk patients were more likely to benefit from immunotherapy. Additionally, ZDHHC18 was significantly upregulated in ccRCC tumor tissues, and its knockdown notably inhibited cell proliferation, migration, and invasion.ConclusionPalmitoylation-related genes, particularly ZDHHC18, serve as promising prognostic biomarkers and predictive indicators for immune therapy in ccRCC. These findings offer new insights into ccRCC biology and highlight potential therapeutic targets for improving patient outcomes.

Background: Bladder urothelial carcinoma (BLCA) is a prevalent malignant tumor known for its high recurrence rates and limited therapeutic efficacy. Cellular senescence has been extensively shown to inhibit tumorigenesis via cell cycle arrest. Consequently, the identification of senescence-associated biomarkers is essential for enhancing the diagnosis, prognosis, and immunotherapeutic outcomes of BLCA.Method: This study integrated the TCGA-BLCA and GSE13507 datasets to analyze senescence-related genes. ConsensusClusterPlus was employed for cluster analysis, while immune infiltration was assessed using CIBERSORT. A diagnostic and prognostic model for BLCA was developed and validated through the combination of various machine learning algorithms. Experimental validation was conducted using qRT-PCR, colony formation, and Transwell assays to evaluate the functional role of PAQR4.Result: Our study revealed that aging-related samples demonstrated improved survival rates and a lower incidence of high-grade tumors. Cluster analysis identified two distinct subgroups of BLCA characterized by unique immune infiltration profiles and varying responses to immune checkpoint blockade. The diagnostic and prognostic models developed from aging-related genes were subsequently validated. Notably, PAQR4 was identified as a critical aging-related gene linked to poor prognosis and an immunosuppressive microenvironment. The knockdown of PAQR4 significantly inhibited the proliferation and metastasis of BLCA cells.Conclusion: PAQR4 is a novel biomarker and therapeutic target for BLCA, influencing cellular senescence, immune evasion, and metastasis. This study provides insights into the senescence-related mechanisms and offers tools for precision diagnosis and the optimization of immunotherapy in BLCA.

Noninvasive prenatal testing (NIPT) has been widely used in various monogenic recessive disorders based on relative haplotype dosage (RHDO) analysis. We accepted a congenital adrenal hyperplasia (CAH) pedigree with highly similar parental pathogenic haplotypes. The initial monogenic NIPT attempt was unsuccessful due to a paucity of informative single-nucleotide polymorphisms (SNPs), prompting improvement of the current method. With a refined algorithm that deduces the fetal genotype based on dosage changes at SNPs located on a specific parental haplotype, while also effectively sidestepping allele bias introduced by hybrid capture, monogenic NIPT was successfully carried out in this family, yielding results consistent with invasive prenatal diagnosis. Theoretically, this algorithm can be employed in scenarios involving consanguineous marriages or when parents share a highly homologous haplotype, thereby broadening its applicability. Detailed methodology is described, and the advantages of our algorithm are discussed.