Gene Set Analysis Research Articles

Hormone Receptor Positive HER2-negative/MammaPrint High-2 Breast Cancers Closely Resemble Triple Negative Breast Cancers.

The MammaPrint prognostic assay categorizes breast cancers into high- and low-risk subgroups, and the high-risk group can be further subdivided into high 1 (MP-H1), and very high-risk high-2 (MP/H-2). The aim of this analysis was to assess clinical and molecular differences between the hormone receptor positive/HER2-negative (HR+) MP-H1, -H2, and triple negative (TN) MP-H1 and -H2 cancers. Pre-treatment gene expression data from 742 HER2 negative breast cancers enrolled in the I-SPY2 neoadjuvant trial was used. Prognostic risk categories were assigned using the MammaPrint assay. Transcriptional similarities across the 4 receptor and prognostic groups were assessed using principal component analyses and by identifying differentially expressed genes. We also examined pathologic complete response (pCR) rates and event-free survivals (EFS) by risk group. Principal component analysis showed that HR+/MP-H2 tumors clustered with TN/MP-H2 cancers. Only 125 genes showed differential expression between the HR+/MP-H2 and TN/MP-H2 cancers while 1,465 genes were differentially expressed between HR+/MP-H2 and -H1. Gene set analysis revealed similarly high expression of cell cycle, DNA repair, and immune-infiltration related pathways in HR+/MP-H2 and TN/MP-H2 cancers. HR+/MP-H2 cancers also showed low estrogen receptor (ER)-related gene expression. pCR rates were similarly high in TN/MP-H2 and HR+/MP-H2 cancers (42% vs 30.5%, p=0.11), and MP-H2 cancers with residual cancer had similarly poor EFS regardless of ER status. In conclusion, HR+/MP-H2 cancers closely resemble TN breast cancers in transcriptional and clinical features and benefit from similar treatment strategies.

Abstract 4124655: Multi-trait analysis identifies 307 genomic loci for thoracic aortic diameter and disease

Introduction: Thoracic aortic aneurysm and dissection (TAAD) is a morbid cardiovascular disease with 21 known common-variant genomic risk loci. Aneurysm represents an extreme of diameter, so we sought to jointly analyze TAAD and aortic diameter to enhance discovery power. Methods: Genome-wide association studies (GWAS) were performed for UK Biobank MRI-derived aortic diameter measured at the aortic root (N=62,919), ascending aorta (N=61,156), and descending aorta (N=62,412). Separately, TAAD GWAS summary statistics from UKB (1,076 cases, 416,263 controls), FinnGen (3,880 cases, 381,977 controls), and the Million Veteran Program (8,626 cases, 453,043 controls) were meta-analyzed. Genetic correlation was assessed with ldsc . Multi-trait analysis was then performed for the aortic measurements and TAAD with MTAG . A 1.1-million-variant polygenic risk score (PRS) was produced and tested in the All of Us biobank. Results: The genetic correlation between ascending aortic diameter and TAAD was 0.83 (P=6.6E-129). The TAAD meta-analysis had an effective sample size of 53,516, augmented to 147,377 after multi-trait analysis. We identified 59 risk loci for TAAD (189 in the multi-trait analysis). These loci included one on the X-chromosome near MIR222HG , previously reported to be regulated by angiopoietin-2; others were near TGF-β-superfamily members ( FBN1 , FBN2 , GDF6 , GDF7 , LTBP4 , TGFB2 , SMAD3 ); genes involved in vasoconstriction ( ADRA1D , ADRB1 , ANGPT1 , EDN1 , EDN2 , EDNRA , PDE3A ); and cell cycle regulators ( CCND2 , CCNE1 , CDC27 , CDK6 , CDKN1A , CDKN1B , CENPW , DSCC1 , MAD2L1 , TP53 ). 307 distinct loci were significantly associated with at least one of the four traits; all but 7 TAAD loci were genome-wide significant for one of the three aortic measurements, and all loci had at least sub-significant signal in those traits. Gene-set analyses highlighted contributions from fibroblasts, pericytes, and vascular smooth muscle cells. In All of Us , a one standard deviation (SD) greater PRS was associated with a hazard ratio (HR) of 1.88 for TAAD (P=1.2E-78) and 1.62 for thoracic aortic dissection (P=5.7E-05). Individuals in the top 5% for the PRS had HR 3.77 for TAAD (P=2.7E-48) and HR 2.87 for thoracic aortic dissection (P=3.3E-03). Discussion: The multi-trait analysis yielded an eight-fold expansion of loci for TAAD risk. The findings underscore that sporadic TAAD is a complex, common disease—intricately linked with the processes associated with normal variation in aortic diameter.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

RAC2 as a Tumor-Suppressive Biomarker Associated with T Cell Infiltration in Breast Cancer.

Background: RAC2 is critical in regulating the homeostasis of hematopoietic stem cells. Nonetheless, its role in breast cancer (BC) remains unclear, necessitating further investigation. Methods: The expression of RAC2 in the BC and healthy tissues was acquired from The Cancer Genome Atlas. Its validity was further assessed using datasets from the gene expression omnibus database. The Tumor Immune Single-cell Hub database was used to collect and analyze the single-cell RNA sequencing datasets of BC. The diagnostic relevance of RAC2 was evaluated using receiver operating characteristic curves. Further assessment was carried out via enrichment analyses; Gene Set Analysis, immune scoring, single-cell sequencing, and immunohistochemical analysis were conducted to confirm the relationship between RAC2 expression and immune infiltration. Results: RAC2 expression was notably heightened in BC (p < 0.001). It was observed that a better prognosis was linked to heightened expression of RAC2 (p < 0.01), with the diagnostic efficacy of the marker noted to be good (area under the curve = 0.858). We found a lower percentage of protumor immune cells and a greater proportion of antitumor immune cells in the high RAC2. Our analysis revealed alterations in gene expression and an enriched network of immune pathways influenced by RAC2. Notably, cytotoxic genes, chemokines, chemokine receptors, immunostimulators, and immunosuppressive molecules positively correlated with RAC2 expression. RAC2 expression reliably predicted how patients would respond to two different therapeutic approaches in BC. Conclusions: The RAC2 was found to be a key biomarker in BC in the current study, demonstrating considerable potential as a prognostic and diagnostic marker. These results highlight the RAC2 potential to improve precision medicine strategies and treatment outcomes for patients with BC.

Does PhenoAge acceleration impact adverse outcomes following major surgery (rehospitalization or death)? A genome-wide association study and mendelian randomisation

Abstract Background Biological aging reflects the state of an individual's cellular and molecular function, and is distinct from chronological age; which reflects the passage of time[1]. Validated measures of biological aging, such as Phenotypic aging (PhenoAge) estimated from nine routine clinical biomarkers, have been associated with increased risk of age-related diseases and mortality in cohort studies[2]. However, the potential causal relationship between them remains unclear. In this study, we aim to identify genetic variants associated with PhenoAge Acceleration (PhenoAgeAccl) and adverse outcome after surgery (rehospitalisation or death within 365-days] and assess the causal relationship between these two phenotypes using Mendelian Randomization framework. Objectives -Perform a genome-wide association study(GWAS) on adverse outcomes following major surgery. -Assess the potential causal relationship between PhenoAgeAccl and adverse outcomes following major surgery using Mendelian randomization analysis. Methods We conducted a GWAS via plink using participants from the UKBiobank (Application 77596) to generate summary statistics for 6509 participants who had undergone major surgery within 365 days of their baseline assessment date(3). For quality control, SNPs were excluded if they didn't meet the following criteria: (1) imputation information score &amp;lt;0.3 (2) minor allele frequency &amp;lt;1%, (3) Hardy–Weinberg equilibrium test p-value significant at the Bonferroni-corrected level. Summary statistics for PhenoAgeAccl was obtained from a previous GWAS of European descents[4]. Gene enrichment analysis was performed using Multi-marker Analysis of GenoMic Annotation (MAGMA) in Functional Mapping and Annotation (FUMA). Then, genetic instruments for PhenoAgeAccl and adverse outcomes following surgery were identified to conduct a summary-based Mendelian Randomisation [Assumptions are listed in Figure 1]. Results GWAS for adverse outcomes following major surgery identified 3 lead SNPs (rs1360618, rs11848297, rs7978) [Figure 2]. Gene-set analysis showed enrichment in the immune system, cell migration, fear response, and iron metabolism. For PhenoAge Acceleration, gene-set analysis showed enrichment in immune system, and cell signalling pathways. 17 SNPs were selected which were used for the Two-Sample MR. We found no causal relationship, as the beta coefficient for Inverse variance weighted was -0.308 [standard error - 0.075, p=0.68]. For every 5-year increase in PhenoAge compared to chronological age, the risk of emergency re-hospitalisation or death increased by 21% (HR 1.16 – 1.3, p &amp;lt; 0.001). Conclusion Gene-set analysis for adverse outcomes following major surgery showed enrichment in immune system, iron metabolism, and fear response which have all recently been associated with mortality post-surgery in cohort studies(4,5). No causal relationship between PhenoAgeAccel and mortality post-surgery was established.Mendelian Randomisation assumptionsManhattan plot adverse-outcomes post-sur

Toden-E: Topology-Based and Density-Based Ensembled Clustering for the Development of Super-PAG in Functional Genomics using PAG Network and LLM.

The integrative analysis of gene sets, networks, and pathways is pivotal for deciphering omics data in translational biomedical research. To significantly increase gene coverage and enhance the utility of pathways, annotated gene lists, and gene signatures from diverse sources, we introduced pathways, annotated gene lists, and gene signatures (PAGs) enriched with metadata to represent biological functions. Furthermore, we established PAG-PAG networks by leveraging gene member similarity and gene regulations. However, in practice, high similarity in functional descriptions or gene membership often leads to redundant PAGs, hindering the interpretation from a fuzzy enriched PAG list. In this study, we developed todenE (topology-based and density-based ensemble) clustering, pioneering in integrating topology-based and density-based clustering methods to detect PAG communities leveraging the PAG network and Large Language Models (LLM). In computational genomics annotation, the genes can be grouped/clustered through the gene relationships and gene functions via guilt by association. Similarly, PAGs can be grouped into higher-level clusters, forming concise functional representations called Super-PAGs. TodenE captures PAG-PAG similarity and encapsulates functional information through LLM, in characterizing network-based functional Super-PAGs. In synthetic data, we introduced a metric called the Disparity Index (DI), measuring the connectivity of gene neighbors to gauge clusterability. We compared multiple clustering algorithms to identify the best method for generating performance-driven clusters. In non-simulated data (Gene Ontology), by leveraging transfer learning and LLM, we formed a language-based similarity embedding. TodenE utilizes this embedding together with the topology-based embedding to generate putative Super-PAGs with superior performance in semantic and gene member inclusiveness.

Comprehensive analysis of lactylation-related gene sets and mitochondrial functions in gastric adenocarcinoma: implications for prognosis and therapeutic strategies.

Gastric adenocarcinoma (STAD) is characterized by high heterogeneity and aggressiveness, leading to poor prognostic outcomes worldwide. This study explored the prognostic significance of lactylation-related gene sets and mitochondrial functions in STAD by integrating large-scale genomic datasets, including TCGA and several GEO datasets. We utilized Spatial transcriptomics and single-cell RNA sequencing to delineate the tumor microenvironment and assess the heterogeneity of cellular responses within the tumor. Additionally, the study identified distinct molecular subtypes within STAD that correspond with unique survival outcomes and immune profiles, enhancing the molecular classification beyond current paradigms. Prognostic models incorporating these molecular markers demonstrated superior predictive capabilities over existing models across multiple validation datasets. Furthermore, our analysis of immune landscapes revealed that variations in lactylation could influence immune cell infiltration and responsiveness, pointing towards novel avenues for tailored immunotherapy approaches. These comprehensive insights provide a foundation for targeted therapeutic strategies and underscore the potential of metabolic and immune modulation in improving STAD treatment outcomes.

Comparative Mitogenomics Analysis Revealed Evolutionary Divergence among Purpureocillium Species and Gene Arrangement and Intron Dynamics of Ophiocordycipitaceae.

The species of Purpureocillium are cosmopolitan and multitrophic fungi that can infect a wide range of invertebrate hosts. This study reports the mitogenome of P. atypicola, a specialized spider pathogenic fungus. The 112,465 bp mitogenome encoded genes typically found in fungal mitogenomes, and a total of 52 introns inserted into seven genes. A comparison with three other Purpureocillium species revealed significant differences in length and intron number, primarily due to intron variation; however, there was no dynamic variation in the introns of the cox1 gene within the same species of the Purpureocillium genus. Different mitochondrial protein-coding genes showed variable degrees of genetic differentiation among these species, but they were all under purifying selection. Additionally, frequent intron loss or gain events were detected to have occurred during the evolution of the Ophiocordycipitaceae mitogenomes, yet the gene arrangement remains conserved. A phylogenetic analysis of the combined mitochondrial gene set gave identical and well-supported tree topologies. The estimated age of the crown of Ophiocordycipitaceae and Purpureocillium were around the Early Cretaceous period (127 Mya) and Late Cretaceous period (83 Mya), respectively. The results of this study advance our understanding of the genomics, evolution, and taxonomy of this important fungal group.

Genetic architectures of the human hippocampus and those involved in neuropsychiatric traits

BackgroundThe hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysis of hippocampal substructures and their genetic correlations across a wide range of neuropsychiatric traits remains underexplored. Given the hippocampus’s high heritability, considering hippocampal and subfield volumes (HASV) as endophenotypes for neuropsychiatric conditions is essential.MethodsWe analyzed MRI-derived volumetric data of hippocampal and subfield structures from 41,525 UK Biobank participants. Genome-wide association studies (GWAS) on 24 HASV traits were conducted, followed by genetic correlation, overlap, and Mendelian randomization (MR) analyses with 10 common neuropsychiatric traits. Polygenic risk scores (PRS) based on HASV traits were also evaluated for predicting these traits.ResultsOur analysis identified 352 independent genetic variants surpassing a significance threshold of 2.1 × 10−9 within the 24 HASV traits, located across 93 chromosomal regions. Notably, the regions 12q14.3, 17q21.31, 12q24.22, 6q21, 9q33.1, 6q25.1, and 2q24.2 were found to influence multiple HASVs. Gene set analysis revealed enrichment of neural differentiation and signaling pathways, as well as protein binding and degradation. Of 240 HASV-neuropsychiatric trait pairs, 75 demonstrated significant genetic correlations (P < 0.05/240), revealing 433 pleiotropic loci. Particularly, genes like ACBD4, ARHGAP27, KANSL1, MAPT, ARL17A, and ARL17B were involved in over 50 HASV-neuropsychiatric pairs. Leveraging Mendelian randomization analysis, we further confirmed that atrophy in the left hippocampus, right hippocampus, right hippocampal body, and right CA1-3 region were associated with an increased risk of developing Parkinson’s disease (PD). Furthermore, PRS for all four HASVs were significantly linked to a higher risk of Parkinson’s disease (PD), with the highest hazard ratio (HR) of 1.30 (95% CI 1.18–1.43, P = 6.15 × 10⁻⁸) for right hippocampal volume.ConclusionsThese findings highlight the extensive distribution of pleiotropic genetic determinants between HASVs and neuropsychiatric traits. Moreover, they suggest a significant potential for effectively managing and intervening in these diseases during their early stages.

Genome-Wide and Exome-Wide Association Study Identifies Genetic Underpinning of Comorbidity between Myocardial Infarction and Severe Mental Disorders.

Myocardial Infarction (MI) and severe mental disorders (SMDs) are two types of highly prevalent and complex disorders and seem to have a relatively high possibility of mortality. However, the contributions of common and rare genetic variants to their comorbidity arestill unclear. We conducted a combined genome-wide association study (GWAS) and exome-wide association study (EWAS) approach. Using gene-based and gene-set association analyses based on the results of GWAS, we found the common genetic underpinnings of nine genes (GIGYF2, KCNJ13, PCCB, STAG1, HLA-C, HLA-B, FURIN, FES, and SMG6) and nine pathways significantly shared between MI and SMDs. Through Mendelian randomization analysis, we found that twenty-seven genes were potential causal genes for SMDs and MI. Based on the exome sequencing data of MI and SMDs patients from the UK Biobank, we found that MUC2 was exome-wide significant in the two diseases. The gene-set analyses of the exome-wide association study indicated that pathways related to insulin processing androgen catabolic process and angiotensin receptor binding may be involved in the comorbidity between SMDs and MI. We also found that six candidate genes were reported to interact with known therapeutic drugs based on the drug-gene interaction information in DGIdb. Altogether, this study revealed the overlap of common and rare genetic underpinning between SMDs and MI and may provide useful insights for their mechanism study and therapeutic investigations.

Exploring the significance and potential mechanisms of hippo pathway-associated genes in prognosis of glioma patients

PurposeDespite the efforts of countless researchers to develop glioma treatment strategies, the current therapeutic effect of glioma is still not ideal, and it is necessary to further explore the mechanism to guide treatment. Thus, this study aims to introduce a novel approach for predicting patient prognosis and guiding further treatment interventions.MethodsInitially, we conducted a differential gene expression analysis to identify Hippo pathway-associated genes overexpressed in tumors and determined genes correlated with prognosis. Subsequently, employing cluster analysis, we categorized samples into two groups and performed further analyses including prediction, immune cell infiltration abundance, and drug response rates. We utilized weighted gene co-expression analysis to reveal gene sets with high co-variation, delineate inter-sample gene correlation patterns, and conduct enrichment analysis. Prognostic models were built using ten machine learning algorithms combined in 101 different combinations, followed by evaluation and validation. Immune infiltration analysis, differential expression analysis of depleted T cell-related markers, drug sensitivity analysis, and exploration of pathway dysregulation were performed for different risk groups. Quality control and batch integration were performed, and single-cell data were analyzed using dimensionality reduction clustering algorithms and annotation tools to evaluate the activity of the prognostic model in malignant cells.ResultsWe conducted data filtering to identify genes overexpressed in tumors, intersecting these genes with Hippo pathway-related genes, identifying 62 genes correlated with prognosis, and performing cluster analysis to divide tumor tissues into two groups. Cluster 2 exhibited a poorer prognosis and demonstrated differences in immune cell infiltration. Utilizing weighted gene co-expression analysis on Cluster 2, we identified gene modules, conducted functional enrichment analysis, and delineated pathways. Employing a combined model based on ten machine learning algorithm combinations, we selected the optimal prognostic model system and validated the model’s predictive ability within the dataset. Through immune-related analysis and drug sensitivity analysis, we uncovered differences in immune infiltration and varying sensitivities to chemotherapy drugs. Additionally, the enrichment analysis of gene set revealed discrepancies in upregulation within relevant pathways between the high and low-risk groups. Finally, annotation and evaluation of malignant cells via single-cell analysis showed increased activity of the prognostic model and variations in distribution across different prognostic levels in malignant cells.ConclusionThis study introduces a novel approach utilizing the Hippo pathway and associated genes for glioma prognosis research, demonstrating the potential and significance of this method in evaluating the outcome for patients with glioma. These findings hold substantial clinical significance in guiding therapy and predicting outcomes for individuals diagnosed with glioma, offering significant clinical utility.

A meta-analysis and polygenic score study identifies novel genetic markers for waist-hip ratio in African populations.

Understanding the genetic underpinnings of anthropometric traits in diverse populations is crucial for gaining insights into their biological mechanisms and potential implications for health. We conducted a genome-wide association study, meta-analysis, and gene set analysis of waist-hip ratio (WHR), WHR adjusted for BMI (WHRadjBMI), waist circumference, BMI, and height using the African Collaborative Center for Microbiome and Genomics Research (ACCME)cohort (n = ~11,000) for discovery and polygenic score target analyses and the Africa America Diabetes Mellitus (AADM) study (n = ~5200) for replication and polygenic score validation. We generated and compared polygenic scores from European, African, Afro-Caribbean, and multiethnic ancestry populations. The top loci associated with each trait in the meta-analysis were in CD36 (rs3211826 [p = 5.90 × 10-12] for WHR and rs73709003 [p = 1.75 × 10-13] for WHRadjBMI), IFI27L1 (rs59775050 [p = 2.66 × 10-08] for waist circumference), INPP4B (rs2636629 [p = 1.44 × 10-09] for BMI), and HMGA1 (rs6937622 [p = 1.40 × 10-15] for height) gene regions. A novel variant rs7797157, near GNAT3, was also significantly associated with WHR (p = 2.50 × 10-10) and WHRadjBMI (p = 2.66 × 10-11). The ancestry-specific parameters for the best predictive polygenic scores were European ancestry (R2 = 0.68%; p = 1.63 × 10-16) and multiethnic ancestry (R2 = 0.06%; p = 1.29 × 10-02) for WHR; European ancestry (R2 = 1.36%; p = 2.94 × 10-31) and multiethnic ancestry (R2 = 1.12%; p = 3.52 × 10-25) for BMI; and European ancestry (R2 = 3.16%; p = 2.95 × 10-73), African ancestry (R2 = 4.16%; p = 1.75 × 10-96), and African and Afro-Caribbean ancestry (R2 = 2.67%; p = 4.35 × 10-62) for height. The discovery of a novel locus for WHR and genetic signals for each trait and the assessment of polygenic score performance underscore the importance of conducting well-powered studies in diverse populations.

Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study

Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge hereafter) using retinal images and PhenoAge, a composite biomarker of phenotypic age. We used retinal photographs from the UK Biobank dataset to train a deep-learning algorithm to predict the composite score of PhenoAge. We used a deep convolutional neural network architecture with multiple layers to develop our deep-learning-based biological ageing marker, as RetiPhenoAge, with the aim of identifying patterns and features in the retina associated with variations of blood biomarkers related to renal, immune, liver functions, inflammation, and energy metabolism, and chronological age. We determined the performance of this biological ageing marker for the prediction of morbidity (cardiovascular disease and cancer events) and mortality (all-cause, cardiovascular disease, and cancer) in three independent cohorts (UK Biobank, the Singapore Epidemiology of Eye Diseases [SEED], and the Age-Related Eye Disease Study [AREDS] from the USA). We also compared the performance of RetiPhenoAge with two other known ageing biomarkers (hand grip strength and adjusted leukocyte telomere length) and one lifestyle factor (physical activity) for risk stratification of mortality and morbidity. We explored the underlying biology of RetiPhenoAge by assessing its associations with different systemic characteristics (eg, diabetes or hypertension) and blood metabolite levels. We also did a genome-wide association study to identify genetic variants associated with RetiPhenoAge, followed by expression quantitative trait loci mapping, a gene-based analysis, and a gene-set analysis. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and corresponding 95% CIs for the associations between RetiPhenoAge and the different morbidity and mortality outcomes. Retinal photographs for 34 061UK Biobank participants were used to train the model, and data for 9429participants from the SEED cohort and for 3986participants from the AREDS cohort were included in the study. RetiPhenoAge was associated with all-cause mortality (HR 1·92 [95% CI 1·42-2·61]), cardiovascular disease mortality (1·97 [1·02-3·82]), cancer mortality (2·07 [1·29-3·33]), and cardiovascular disease events (1·70[1·17-2·47]), independent of PhenoAge and other possible confounders. Similar findings were found in the two independent cohorts (HR 1·67 [1·21-2·31] for cardiovascular disease mortality in SEED and 2·07 [1·10-3·92] in AREDS). RetiPhenoAge had stronger associations with mortality and morbidity than did hand grip strength, telomere length, and physical activity. We identified two genetic variants that were significantly associated with RetiPhenoAge (single nucleotide polymorphisms rs3791224 and rs8001273), and were linked to expression quantitative trait locis in various tissues, including the heart, kidneys, and the brain. Our new deep-learning-derived biological ageing marker is a robust predictor of mortality and morbidity outcomes and could be used as a novel non-invasive method to measure ageing. Singapore National Medical Research Council and Agency for Science, Technology and Research, Singapore.

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.

Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis. Unfortunately, statistical and biological differences between single cell and bulk transcriptomic data make it challenging to use existing gene set collections, which were developed for bulk tissue analysis, on scRNA-seq data. In this paper, we describe a procedure for customizing gene set collections originally created for bulk tissue analysis to reflect the structure of gene activity within specific cell types. Our approach leverages information about mean gene expression in the 81 human cell types profiled via scRNA-seq by the Human Protein Atlas (HPA) Single Cell Type Atlas. This HPA information is used to compute cell type-specific gene and gene set weights that can be used to filter or weight gene set collections. As demonstrated through the analysis of immune cell scRNA-seq data using gene sets from the Molecular Signatures Database (MSigDB), accounting for cell type-specificity can significantly improve gene set testing power and interpretability. An example vignette along with gene and gene set weights for the 81 HPA SCTA cell types and the MSigDB collections are available at https://hrfrost.host.dartmouth.edu/SCGeneSetOpt/ .

Molecular Mechanism of Radioresponsiveness in Colorectal Cancer: A Systematic Review.

Background/Objectives: Colorectal cancer (CRC) is the third most diagnosed cancer globally. Radiotherapy is a common treatment strategy for patients but factors such as gene expressions and molecular mechanism effects may affect tumor radioresponse. The aim of this review is to systematically identify genes suggested to have molecular mechanism effects on the radioresponsiveness of CRC patients. Methods: By following the PRISMA guidelines, a comprehensive literature search was conducted on Pubmed, EMBASE and Cochrane Library. After exclusion and inclusion criteria sorting and critical appraisal for study quality, data were extracted from seven studies. A gene set analysis was conducted on reported genes. Results: From the seven studies, 56 genes were found to have an effect on CRC radioresponsiveness. Gene set analysis show that out of these 56 genes, 24 genes have roles in pathways which could affect cancer radioresponse. These are AKT1, APC, ATM, BRAF, CDKN2A, CTNNB1, EGFR, ERBB2, FLT3, KRAS, MET, mTOR, MYC, NFKB1, KRAS, PDGFRA, PIK3CA, PTEN, PTGS1, PTGS2, RAF1, RET, SMAD4 and TP53. The current project was conducted between the period May 2024 to August 2024. Conclusions: The current review systematically presented 56 genes which have been reported to be related to RT or CRT treatment effectiveness in rectal cancer patients. Gene set analysis shows that nearly half of the genes were involved in apoptosis, DNA damage response and repair, inflammation and cancer metabolism molecular pathways that could affect cancer radioresponse. The gene cohort identified in this study may be used as a foundation for future works focusing on the molecular mechanism of specific pathways contributing to the radioresponse of CRC.

Integrating Genetic Alterations and Histopathological Features for Enhanced Risk Stratification in Non-Muscle-Invasive Bladder Cancer.

Urothelial carcinoma presents as non-muscle-invasive bladder cancer (NMIBC) in ~75% of primary cases. Addressing the limitations of the TNM and WHO04/16 classification systems, this study investigates genetic alterations, the mitotic activity index (MAI), and immunohistochemistry (IHC) markers CK20, p53, and CD25 as better prognostic biomarkers in NMIBC. Using the Oncomine™ Focus Assay for targeted next-generation sequencing (NGS), 409 single-nucleotide variations (SNVs) and 193 copy number variations (CNVs) were identified across 287 patients with TaT1 tumors. FGFR3 and PIK3CA alterations were significantly more prevalent in Ta tumors, while T1 tumors had significant ERBB2 alterations. Low-grade (LG) tumors were enriched with FGFR3 alterations, while high-grade (HG) tumors were significantly associated with ERBB2 alterations, as well as FGFR1 and CCND1 amplifications. FGFR3 alterations were linked to shorter recurrence-free survival (RFS; p = 0.033) but improved progression-free survival (PFS; p < 0.001). Conversely, ERBB2 alterations (p < 0.001), ERBB3 mutations (p = 0.044), and both MYC (p < 0.001) and MYCN (p = 0.011) amplifications were associated with shorter PFS. Survival analysis of gene sets revealed inverse associations between PIK3CA and ERBB2 (p = 0.003), as well as PIK3CA and MYC (p = 0.005), with PFS. In multivariate Cox regression, MAI was the strongest predictor for PFS. Integrating genetic alterations and histopathological features may improve risk stratification in NMIBC.

Monoallelic loss of RB1 Enhances Osteogenic Differentiation and Delays DNA Repair Without Inducing Tumorigenicity

The Retinoblastoma (RB1) gene plays a pivotal role in osteogenic differentiation. Our previous study, employing temporal gene expression analysis using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), revealed the deregulation of osteogenic differentiation in patient-derived heterozygous RB1 mutant orbital adipose-derived mesenchymal stem cells (OAMSCs). The study revealed increased Alizarin Red staining, suggesting heightened mineralization without a corresponding increase in osteogenic lineage-specific gene expression. In this study, we performed high-throughput RNA sequencing on RB1+/+ and RB1+/- patient-derived OAMSCs differentiated towards the osteogenic lineage to investigate the pathways and molecular mechanisms. The pathway analysis revealed significant differences in cell proliferation, DNA repair, osteoblast differentiation, and cancer-related pathways in RB1+/- OAMSC-derived osteocytes. These findings were subsequently validated through functional assays. The study revealed that osteogenic differentiation is increased in RB1+/- cells, along with enhanced proliferation of the osteocytes. There were delayed but persistent DNA repair mechanisms in RB1+/- osteocytes, which were sufficient to maintain genomic integrity, thereby preventing or delaying the onset of tumors. This contrasts with our earlier observation of increased mineralization without corresponding gene expression changes, emphasizing the importance of high-throughput analysis over preselected gene set analysis in comprehending functional assay results.

Multi-step gene set analysis identified HTR3 family genes involving childhood acute lymphoblastic leukemia susceptibility.

In our previous conventional genome-wide association study (GWAS), WWOX was a susceptibility gene associated with acute lymphoblastic leukemia (ALL) development. Nowadays, advancements in genetic association analyses promote an in-depth exploration of ALL genomics. We conducted a two-step enrichment analysis at both gene and pathway levels based on ALL GWAS data including 269 cases and 1039 controls of Chinese descent. The following functional prediction and experiments were used to evaluate the genetic biology of candidate variants and genes. The serotonin-activated cation-selective channel complex gene-set was a potential biological pathway involved in ALL occurrence. Of which, individuals carrying the T allele of rs33940208 exhibited a prominent reduced risk of ALL [odds ratio (OR) = 0.71, 95% confidence interval (CI) = 0.53 to 0.96, P = 2.81 × 10-2], whereas those with the A allele of rs6779545 demonstrated an increased risk (OR = 1.23, 95% CI = 1.01 to 1.51, P = 4.11 × 10-2). Notably, the two variants displayed a better prediction capability when combined, that the risk of developing childhood ALL increased by 131% in subjects with 2-4 risk alleles compared to those with 0-1 risk alleles (Ptrend = 2.05 × 10-3). In addition, the T allele of rs33940208 could reduce HTR3A mRNA level, while the A allele of rs6779545 increased HTR3D mRNA expression. In this study, we identified HTR3A rs33940208 and HTR3D rs6779545 as potential susceptibility loci for ALL in Chinese children. Future validation and functional research will elucidate the underlying molecular processes, refining preventive strategies for this disease.

GsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability.

To investigate the functional significance of genetic risk loci identified through genome-wide association studies (GWASs), genetic loci are linked to genes based on their capacity to account for variation in gene expression, resulting in expression quantitative trait loci (eQTL). Following this, gene set analyses are commonly used to gain insights into functionality. However, the efficacy of this approach is hampered by small effect sizes and the burden of multiple testing. We propose an alternative approach: instead of examining the cumulative associations of individual genes within a gene set, we consider the collective variation of the entire gene set. We introduce the concept of gene set QTL (gsQTL), and show it to be more adept at identifying links between genetic risk variants and specific gene sets. Notably, gsQTL experiences less susceptibility to inflation or deflation of significant enrichments compared with conventional methods. Furthermore, we demonstrate the broader applicability of shared variability within gene sets. This is evident in scenarios such as the coordinated regulation of genes by a transcription factor or coordinated differential expression.

Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis.

The functions of genes in networks are typically correlated due to their functional connectivity. Variable selection methods have been developed to select important genes associated with a trait while incorporating network graphical information. However, no method has been proposed to quantify the uncertainty of individual genes under such settings. In this paper, we construct confidence intervals (CIs) and provide P-values for parameters of a high-dimensional linear model incorporating graphical structures where the number of variables p diverges with the number of observations. For combining the graphical information, we propose a graph-constrained desparsified LASSO (least absolute shrinkage and selection operator) (GCDL) estimator, which reduces dramatically the influence of high correlation of predictors and enjoys the advantage of faster computation and higher accuracy compared with the desparsified LASSO. Theoretical results show that the GCDL estimator achieves asymptotic normality. The asymptotic property of the uniform convergence is established, with which an explicit expression of the uniform CI can be derived. Extensive numerical results indicate that the GCDL estimator and its (uniform) CI perform well even when predictors are highly correlated. An R package implementing the proposed method is available at https://github.com/XiaoZhangryy/gcdl.