Human Mutation最新文献

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.

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.

Amelogenesis is the process of tooth enamel formation, and genetic variants disrupting it cause the Mendelian inherited disorder amelogenesis imperfecta (AI). AI patients have weak, discoloured or brittle enamel, caused by reduced enamel quantity or mineralisation. AI can occur in isolation or, less commonly, as part of a syndrome. Pathogenic variants in at least 38 genes have been shown to cause AI. Current genetic screening studies typically use exome sequencing, but this is expensive and involves complex data analysis workflows. Target enrichment using smMIPs (single molecule molecular inversion probes) provides a flexible alternative, allowing the creation of a disease-specific reagent for low cost, robust, high-throughput screening. Here, we describe the development of an smMIP reagent targeting 19 genes implicated in isolated AI and assess its use in screening a cohort of 181 UK probands with nonsyndromic AI. While this was intended only as a prescreen to prioritise exome sequencing more efficiently, it nevertheless led to molecular diagnoses for 63 probands (35%). Cost per sample screened was approximately £40. Variants in three genes, COL17A1, FAM83H (both dominant) and MMP20 (recessive), accounted for approximately half of solved cases. There is scope to further improve the smMIP reagent by adding additional probes targeting regions of low coverage or additional genes, including those involved in syndromic AI, as well as accommodating new information about the genetic basis of AI. The smMIP reagent provides a robust, flexible, high-throughput, low-cost approach to AI screening, and it is available as a resource to the international AI research community.

Numerous studies have emphasized the importance of the ubiquitin–proteasome system (UPS) in the malignant progression of ovarian cancer (OC). However, whether ubiquitination-related genes (UbRGs) can be used to predict the prognosis of OC remains to be revealed. Patients with OC were divided into two clusters based on the expression of UbRGs, and prognosis was compared between the two clusters. A prognostic model was established based on UbRGs, and its predictive efficiency was validated using Kaplan–Meier (K–M) curves, receiver operating characteristic (ROC) curves, and a nomogram. Immune infiltration and gene mutation analyses were used to examine the effects of UbRGs on the prognosis of OC. The prognostic model served as a valid and independent predictor of OC prognosis. Immune infiltration revealed that the unique immune microenvironment of OC was regulated by UbRGs. Gene mutation analysis indicates that UbRGs likely influence OC malignant behavior by modulating gene mutation patterns. In addition, Ube2j1 was found to play an important role in regulating the malignant progression of OC. Furthermore, the mechanism by which Ube2j1 modulates the OC phenotype and reshapes its immune microenvironment via the JAK2/STAT3/PD-L1 pathway was elucidated, providing novel insights into the potential for ubiquitination-based immunotherapy in OC. This study provides novel insights into precision immunotherapy based on UbRGs in OC. The UbRGs-based prognostic model may help to provide novel insights for the application of ubiquitination-based immunotherapy in OC.

Background: Heterogenized sphingolipid metabolism (SM) drives osteosarcoma tumorigenesis and its tumor-promoting microenvironment. State-of-the-art bioinformatic tools, such as machine learning, are essential for dissecting the prognostic value of SM by investigating its molecular and cellular mechanisms.

Methods: A tailored machine learning pipeline was established by integrating Cox regression, 5-fold cross-validation, Elastic Net, eXtreme Gradient Boosting (XGBoost), and Bayesian optimization (for hyperparameters tuning) to foster an SM Elastic Net-XGBoost (SNEX) prognostic model, interpreted by the Shapley additive explanations (SHAP) algorithm. The alterations in molecular pathways and immune microenvironment–driven unfavorable prognosis of SNEX-identified high-risk osteosarcoma were further investigated. The SNEX predicted results have also been clinically and experimentally validated.

Results: We identified 22 critical SM prognostic genes for Bayesian-optimized SNEX. This model provided outstanding estimates of the prognoses of osteosarcoma patients (C-index of 1.000). Its robustness was confirmed in the independent test set with a high area under the curve (AUC) of 0.875 at 1 year, 0.930 at 3 years, and 0.930 at 5 years. SNEX also significantly outperformed all previous genetic prognostic signatures with a significantly higher net benefit of decision curves and higher AUCs. ACTA2 was the most pivotal gene critical to the negative prediction of SNEX, while BNIP3 was for positive prediction. Mechanistically, SNEX-identified high-risk osteosarcoma suffered unfavorable prognoses due to dysregulation of many critical metabolic/inflammatory/immune biologic processes and immunosuppressive microenvironment, with reduced infiltration of 14 types of immune cells (macrophages, CD8+ T cells, NK cells, etc.). Notably, SNEX highlighted TERT as the most remarkable SM prognostic gene. Clinical osteosarcomas with high expression of TERT exhibited more significant malignant characteristics than others, as evidenced by their higher proliferation efficiency. In addition, all the experiments in vitro and in vivo validated that inhibiting TERT abundance reduces the proliferation, invasion, and migration capabilities of osteosarcoma cells.

Conclusions: This study is a first-hand report employing a tailored machine-learning pipeline for dissecting the prognostic value and roles of SM in osteosarcoma. The present study fostered a SNEX for risk-stratification with outstanding accuracy and offered deep insights into SM-mediated pathways and microenvironment dysregulation in osteosarcoma.

Sepsis is a life-threatening condition triggered by infection, resulting in widespread inflammation, immune dysfunction, and multiorgan failure. Despite advances in medical science, early detection, treatment, and prognosis remain significant challenges. In this study, we investigated the expression and functional role of ZDHHC19, a palmitoyltransferase enzyme, in sepsis. Our results demonstrate that ZDHHC19 is significantly upregulated in neutrophils during sepsis and correlates with inflammatory pathways critical to disease progression. High ZDHHC19 expression was associated with increased activation of immune-inflammatory processes such as collagen metabolism, myeloid cell activation, and cell adhesion, while suppressing antigen presentation. Additionally, ZDHHC19 expression was linked to poor patient prognosis, with higher levels correlating with increased mortality and sepsis shock. Using advanced computational tools, including XGBoost for machine learning-based core gene selection and CellChat for cell communication analysis, we identified key regulatory networks modulated by ZDHHC19, revealing its pivotal role in immune dysregulation. These findings suggest that ZDHHC19 may serve as a potential biomarker for the diagnosis and prognosis of sepsis and open new avenues for targeted therapeutic interventions aimed at modulating immune responses in this condition.

Wilson disease is a disorder of copper (Cu) homeostasis caused by the malfunction of Cu transporter ATP7B and associated Cu accumulation in tissues. The existence of over 700 disease-associated variants in the ATP7B gene and a broad spectrum of disease manifestations complicate the analysis of genotype–phenotype correlations and the development of better treatments for this disorder. To assist such studies, we screen 101 variants of ATP7B for expression and Cu transport activity in human fibroblasts lacking active ATP-dependent Cu transporters. The ClinVar database classified 59 of these as variants of uncertain significance or having conflicting pathogenicity classifications; six variants were not in the database. Thirty-three of the variants have been previously characterized by other assays. Only three variants (S657R, G1061E, and G1266R) resulted in the complete inactivation of Cu transport. The in silico analysis of these mutants was used to rationalize this drastic effect on ATP7B activity. The remaining ATP7B variants showed a range of Cu transport activities. Coexpression of variants with different properties yielded activity values different from the simple average. The advantages and limitations of this functional screen are discussed.

Pulmonary arterial hypertension (PAH) is a rare disease that can be caused by pathogenic variants, most frequently in the bone Morphogenetic Protein Receptor Type 2 (BMPR2) gene. We formed a ClinGen variant curation expert panel to devise guidelines for the clinical interpretation of BMPR2 variants identified in PAH patients. The general ACMG/AMP variant classification criteria were refined for PAH and adapted to BMPR2 following ClinGen procedures. Subsequently, these specifications were tested independently by three members of the curation expert panel on 28 representative BMPR2 variants selected from ClinVar and then presented and discussed in the plenum. Application of the final BMPR2 variant specifications resolved six of nine variants (66%) where multiple ClinVar classifications included a variant of uncertain significance, with all six being reclassified as Benign or Likely Benign. Four splice site variants underwent clinically consequential reclassification based on the presence or absence of supporting mRNA splicing data. These variant specifications provide an international framework and a valuable tool for BMPR2 variant classification that can be applied to increase confidence and consistency in BMPR2 interpretation for diagnostic laboratories, clinical providers, and patients.

Enzalutamide is classified as a novel antiandrogen medication; however, the majority of patients ultimately develop resistance to it. Consequently, conducting an in-depth investigation into potential targets of enzalutamide is essential for addressing the drug resistance observed in patients and for facilitating the discovery of new therapeutic targets. The SwissTargetPrediction database was used to identify targets linked to enzalutamide and to assess these targets in the prostate adenocarcinoma (PRAD) dataset sourced from the TCGA database. By employing various datasets and applying different machine learning methods for clustering, researchers constructed and validated both diagnostic and prognostic models for PRAD. A correlation analysis with the androgen receptor revealed TDP1 as the gene most significantly associated with enzalutamide. In addition, this study examined the relationship between TDP1 and immune infiltration. The expression levels of TDP1 and its prognostic correlation in PRAD patients were validated through immunofluorescence staining of 60 PRAD tissue specimens. Cluster analysis revealed a notable correlation among the 24 genes related to enzalutamide with regard to both prognosis and immune infiltration in PRAD patients. The diagnostic model, which incorporates various machine learning techniques, exhibits robust predictive ability for PRAD diagnosis, while the prognostic model employing the LASSO algorithm has also shown encouraging outcomes. Among the various prognostic genes linked to enzalutamide, TDP1 stands out as an important indicator of prognosis. Furthermore, immunofluorescence experiments confirmed that an increased expression of TDP1 is associated with a worse prognosis in patients with PRAD. Our results underscore the substantial potential of TDP1 as a novel diagnostic and prognostic biomarker for individuals diagnosed with PRAD.

Bloom syndrome (BS) is a rare genetic disorder associated with an elevated risk of cancer. In a national multicentre study, nine paediatric patients with BS and cancer were analysed. Median age at cancer diagnosis was 12 years. Four of the nine patients were diagnosed with BS prior to cancer detection. Six presented with solid tumours, whilst three had haematological malignancies. Six received polychemotherapy, often with dose reductions. Complications included prolonged aplasia, sepsis and early treatment discontinuation. Two patients received radiotherapy. Four relapsed, and four died, including one toxic death. However, five achieved remission, highlighting the possibility of curative treatment despite significant toxicities.