Gene Set Analysis Research Articles

Deciphering age-specific molecular features in cervical cancer and constructing an angio-immune prognostic model.

Cancer incidence is increasingly seen in younger individuals. Molecular distinctions between young and elderly patients at onset are understudied. This study used public databases to explore genomic, transcriptomic, and immune-related features across age groups in cervical cancer. Additionally, it aims to create a prognostic model applicable across diverse age cohorts, enabling precise patient stratification, and personalized therapies. Gene mutations, expression data, and clinicopathological information were obtained from 317 cervical cancer patients. These patients were divided into a young group and an old group based on the median age of onset. The characteristics of differential gene mutation, gene expression, and immune cells analysis were analyzed by R software. Finally, the prognostic model was constructed by univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox regression analyses of angiogenic and immune gene sets. Its validity was further confirmed using an additional 300 cervical squamous cell carcinoma and endocervical adenocarcinoma tissues. Cervical cancer patients at elderly onset age exhibit a significantly higher frequency of NOTCH1 and TP53 driver mutations compared to young patients, along with a notably higher tumor mutational burden. However, there were no significant differences between the 2 groups in terms of genomic instability and age-related mutational signatures. Differential gene expression analysis revealed that the young group significantly upregulated interferon-alpha and gamma responses and exhibited significantly higher activity in multiple metabolic pathways. Immune microenvironment analysis indicated enrichment of dendritic cells and natural killer cells in the young group, while transforming growth factor-β signature was enriched in the elderly group, indicating a higher degree of immune exclusion. A multigene prognostic model based on angiogenesis and T cell immune gene sets showed excellent prognostic performance independent of clinical factors such as age. High-risk groups identified by the model exhibit significant activation of tumor-promoting processes, such as metastasis and angiogenesis. Our study reveals distinct patterns in cancer-driving mechanisms, biological processes, and immune system status between young and elderly patients at onset with cervical cancer. These findings shed light on the age-specific underlying mechanisms of carcinogenesis. Furthermore, an independent molecular prognostic model is constructed to provide valuable references for patient stratification and the development of potential drug targets.

Overexpression of TNFSF11 reduces GPX4 levels and increases sensitivity to ferroptosis inducers in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD), the most common and lethal subtype of lung cancer, continues to be a major health concern worldwide. Despite advances in targeted and immune therapies, only a minority of patients derive substantial benefits. As a result, the urgent need for novel therapeutic strategies to improve lung cancer treatment outcomes remains undiminished.MethodsIn our study, we employed the TIMER database to scrutinize TNFSF11 expression across various cancer types. We further examined the differential expression of TNFSF11 in normal and tumor tissues utilizing the TCGA-LUAD dataset and tissue microarray, and probed the associations between TNFSF11 expression and clinicopathological parameters within the TCGA-LUAD dataset. We used the GSE31210 dataset for external validation. To identify genes strongly linked to TNFSF11, we engaged LinkedOmics and conducted a KEGG pathway enrichment analysis using the WEB-based Gene SeT AnaLysis Toolkit. Moreover, we investigated the function of TNFSF11 through gene knockdown or overexpression approaches and explore its function in tumor cells. The therapeutic impact of ferroptosis inducers in tumors overexpressing TNFSF11 were also investigated through in vivo and in vitro experiments. Through these extensive analyses, we shed light on the potential role of TNFSF11 in lung adenocarcinoma, underscoring potential therapeutic targets for this malignancy.ResultsThis research uncovers the overexpression of TNFSF11 in LUAD patients and its inverse correlation with peroxisome-related enzymes. By utilizing gene knockdown or overexpression assays, we found that TNFSF11 was negatively associated with GPX4. Furthermore, cells with TNFSF11 overexpression were relatively more sensitive to the ferroptosis inducers.ConclusionsOur research has provided valuable insights into the role of TNFSF11, revealing its negative regulation of GPX4, which could be influential in crafting therapeutic strategies. These findings set the stage for further exploration into the mechanisms underpinning the relationship between TNFSF11 and GPX4, potentially opening up new avenues for precision medicine in the treatment of LUAD.

Pan-cancer and single-cell analysis of actin cytoskeleton genes related to disulfidptosis.

Disulfidptosis was recently reported to be caused by abnormal disulfide accumulation in cells with high SLC7A11 levels subjected to glucose starvation, suggesting that targeting disulfidptosis was a potential strategy for cancer treatment. We analyzed the relationships between gene expression and mutations and prognoses of patients. In addition, the correlation between gene expression and immune cell infiltration was explored. The potential regulatory mechanisms of these genes were assessed by investigating their related signaling pathways involved in cancer, their expression patterns, and their cellular localization. Most cancer types showed a negative correlation between the gene-set variation analysis (GSVA) scores and infiltration of B cells and neutrophils, and a positive correlation between GSVA scores and infiltration of natural killer T and induced regulatory T cells. Single-cell analysis revealed that ACTB, DSTN, and MYL6 were highly expressed in different bladder urothelial carcinoma subtypes, but MYH10 showed a low expression. Immunofluorescence staining showed that actin cytoskeleton proteins were mainly localized in the actin filaments and plasma membrane. Notably, IQGAP1 was localized in the cell junctions. In conclusion, this study provided an overview of disulfidptosis-related actin cytoskeleton genes in pan-cancer. These genes were associated with the survival of patients and might be involved in cancer-related pathways.

Single cell and bulk RNA expression analyses identify enhanced hexosamine biosynthetic pathway and O-GlcNAcylation in acute myeloid leukemia blasts and stem cells.

Acute myeloid leukemia (AML) is the most common acute leukemia in adults with an overall poor prognosis and high relapse rate. Multiple factors including genetic abnormalities, differentiation defects and altered cellular metabolism contribute to AML development and progression. Though the roles of oxidative phosphorylation and glycolysis are defined in AML, the role of the hexosamine biosynthetic pathway (HBP), which regulates the O-GlcNAcylation of cytoplasmic and nuclear proteins, remains poorly defined. We studied the expression of the key enzymes involved in the HBP in AML blasts and stem cells by RNA sequencing at the single-cell and bulk level. We performed flow cytometry to study OGT protein expression and global O-GlcNAcylation. We studied the functional effects of inhibiting O-GlcNAcylation on transcriptional activation in AML cells by Western blotting and real time PCR and on cell cycle by flow cytometry. We found higher expression levels of the key enzymes in the HBP in AML as compared to healthy donors in whole blood. We observed elevated O-GlcNAc Transferase (OGT) and O-GlcNAcase (OGA) expression in AML stem and bulk cells as compared to normal hematopoietic stem and progenitor cells (HSPCs). We also found that both AML bulk cells and stem cells show significantly enhanced OGT protein expression and global O-GlcNAcylation as compared to normal HSPCs, validating our in silico findings. Gene set analysis showed substantial enrichment of the NF-κB pathway in AML cells expressing high OGT levels. Inhibition of O-GlcNAcylation decreased NF-κB nuclear translocation and the expression of selected NF-κB-dependent genes controlling cell cycle. It also blocked cell cycle progression suggesting a link between enhanced O-GlcNAcylation and NF-κB activation in AML cell survival and proliferation. Our study suggests the HBP may prove a potential target, alone or in combination with other therapeutic approaches, to impact both AML blasts and stem cells. Moreover, as insufficient targeting of AML stem cells by traditional chemotherapy is thought to lead to relapse, blocking HBP and O-GlcNAcylation in AML stem cells may represent a novel promising target to control relapse.

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study.

Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%-98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.

Abstract 2763: A specific ribomethylome pattern in lung cancer is associated with increased risk of metastasis

Abstract Non-coding RNAs drive cancer phenotypes and are associated with patients’ outcome. SnoRNAs are required for the 2’-O-methylation (2’-O-Me) and pseudouridylation of ribosomal RNA (rRNA) thus being essential for ribosomal biogenesis and function. So far, the role of rRNA methylation (ribomethylation) for non-small cell lung cancer (NSCLC) pathogenesis is unknown. To investigate the role of ribomethylation we included samples from 92 patients with lung adenocarcinoma (pathological stage IA to IIB) for comprehensive multi-omics profiling. Amongst transcriptomics, small RNA sequencing, proteomics, and whole exome sequencing, ribomethylation as epitranscriptomic dimension was analyzed using RiboMethSeq. We focused the analysis on complete tumor resection samples. Here 47 out of 106 2’-O-Me sites were fully methylated in all tumor samples suggesting a crucial role for methylation at this position. Surprisingly, 59 sites had a dynamic methylation pattern. By combining ribomethylation sequencing data of dynamic sites with whole-exome-sequencing as well as transcriptome and proteome analyses using a multi-omics factor analysis (MOFA), we discovered a 2’-O-Me signature in a subset of NSCLC patients with a high risk of metastasis and poor prognosis. We termed the patient subset, with underlying ribomethylation signature, epitranscriptomic pro-metastatic phenotype (EPROMET). This phenotype was not associated with genetic mutations as analyzed by exome sequencing. Analysis of gene sets in both transcriptomics and proteomics showed an upregulation of secreted and extracellular matrix proteins in EPROMET patients. Alongside, the ribomethylation site with major contribution to the EPROMET signature; 18S-Um799, mediated through SNORD105/105B, was found to be the most dynamic within the dataset. It also showed the largest difference between EPROMET and non-EPROMET patients. For functional validation, lung cancer cell lines with knockout of snoRNAs contributing to EPROMET signature were generated using CRISPR/Cas9. Lung cancer cells lacking the corresponding rRNA modifications migrated slower in vitro, failed to grow at a distant site in vivo and were impaired in metastasis. Functionally, it was shown that the modulated ribomethylation signature effected translation of secreted proteins by altered mRNA binding to ribosomes. This study indicates the presence of a ribomethylome-associated phenotype of NSCLC related to metastasis and poor prognosis. Altered ribomethylation affects the ribosome function towards differential expression patterns of secreted proteins. Taken together, an epitranscriptomic pattern of 2’-O-Me is associated with invasion/migration properties of NSCLC cells and with development of metastasis. The ribomethylome may present a suitable biomarker and a potential therapeutic target. Citation Format: Cornelius Pauli, Daniel Heid, Christian Rohde, Nadja Krall, Sylvain Delaunay, Michael Kienhoefer, Christian Tischer, Michael Allgäuer, Maximilian Felix Blank, Fengbiao Zhou, Michael Kardorff, Michael Thomas, Hauke Winter, Sarah Sandmann, Marc Kriegsmann, Marc Schneider, Thomas Muley, Alexander Brobeil, Nicole Bäumer, Sebastian Bäumer, Simon Raffel, Albrecht Stenzinger, Peter Schirmacher, Junyan Lu, Judith Zaugg, Michaela Frye, Carsten Müller-Tidow. A specific ribomethylome pattern in lung cancer is associated with increased risk of metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2763.

Abstract 7416: GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data

Abstract Identification of genotype-phenotype relationships is of utmost importance and a core effort in biology. Recent developments in efficient and precise gene targeting approaches coupled to omics methods have significantly improved deciphering of molecular interactions and relationships. However, many single gene perturbations can affect the expression of hundreds of other genes and analysis of the resulting omics-derived gene lists currently remains a significant challenge. Here we present Perturb-Seq based Gene Set Analyzer (GeneSetR), a user-friendly web-server that can analyze user-defined gene lists based on the data from a recently published genome-wide Perturb-Seq study, which targeted 9,866 genes with 11,258 sgRNAs in the K562 cell line. Through this tool, users can cluster gene lists following dimensionality reduction by various algorithms, undertake network analysis from RNA sequencing data, identify key nodes among the submitted genes, perform gene signature analyses, and generate heatmaps based on perturbation or gene expression data. GeneSetR enables researchers to readily identify gene clusters associated with specific phenotypes or biological processes, providing insights into the potential functional roles of these clusters and the role of single genes in them. With robust analysis capabilities, GeneSetR is a powerful resource to facilitate the exploration of genotype-phenotype relationships. Citation Format: Omer Faruk Kuzu, Fahri Saatcioglu. GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7416.

Abstract 5482: Spatial and multi-omic interrogation of the primary and recurrent glioblastoma microenvironment

Abstract Glioblastoma remains as one of the deadliest malignancies with most cases likely to recur. Recent work has focused on characterizing not only glioma cells, but the tumor microenvironment via single-cell and spatial transcriptomic technologies. This has improved our understanding of the high heterogeneity of this disease and described potential tumor domains. However, there is still a need to further understand the changes that occur between the primary and the recurrent lesion. We propose that a spatial, multi-omic characterization of matched primary and recurrent glioblastoma can yield insights into recurrence mechanisms occurring throughout the tumor microenvironment. We collected 22 IDH-WT glioblastoma samples from 12 patients (11 primary, 11 recurrent, 10 matched pairs) from the Mayo Clinic Neuroscience Biobank. We performed single-nucleus RNA sequencing (15 samples, 10 patients, 5 matched pairs) and proteomics (16 samples, 6 matched pairs) from fresh-frozen tissue, as well as spatial transcriptomics with 10x Visium (14 samples, 8 patients, 6 matched pairs) and 10x Xenium in-situ molecular imaging (6 samples, 2 matched pairs). Using spatial transcriptomics data, we identified regional subsets present in different proportions and distributions between primary and recurrent glioblastoma. These clusters were characterized with gene set and gene ontology analysis, identifying regions enriched with known glioma subtypes and cellular activity. Spatial-based cluster genes were consistent with significantly differentially expressed proteins. We used single-nucleus RNAseq and in-situ molecular imaging data from matched tissue samples to further interrogate relevant cell populations and location in the tumor micro-environment. We identified regions with enriched extra cellular matrix interaction and remodeling signatures to be highly expressed in recurrent glioblastoma. Primary glioblastoma was enriched with oligodendrocyte-like regions with high cell communication and extracellular vesicle signatures. This study will further our understanding of the changes in the mechanisms at play in the complex ecosystem of glioblastoma as it recurs. Citation Format: Jean R. Clemenceau, Paola Suarez-Meade, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial and multi-omic interrogation of the primary and recurrent glioblastoma microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5482.

Abstract 3432: Blood metabolomic profile of glioma risk: A pooled, multi-cohort analysis in COMETS

Abstract Although gliomas are relatively rare, they are among the most lethal malignancies. Yet, with the exception of ionizing radiation and family history, their etiologic factors remain largely unknown. We conducted an untargeted metabolomics investigation pooling nested case-control studies from 9 prospective cohorts (median age 63 years, 59% women) in the Consortium of Metabolomics Studies (COMETS). One cancer-free control was individually matched to each of the 803 glioma cases based on age, sex, self-identified race/ethnicity, and blood collection date. Using an ultrahigh-performance LC-MS/MS platform (Metabolon, Inc.), we identified 874 known metabolites in pre-diagnostic serum or plasma, and logistic regression models conditioned on the matching factors) estimated odds ratios (ORs) and 95% confidence intervals for the associations between 1-SD increases in circulating metabolites and glioma risk (Bonferroni corrected significance threshold P=5.7 × 10−5). A total of 58 metabolites were associated with glioma (P<0.05), with the top three being 3-methylglutarylcarnitine (OR=1.19; P=0.001), cholate (OR=0.85; P=0.001) and a sphingomyelin (SM) (d18:2/21:0, d16:2/23:0, OR=1.19; P=0.002). Analysis restricted to cases diagnosed >5 years after blood collection showed 51 metabolites associated with risk (P<0.05), including SM d18:2/23:1 (OR=1.32; P=5.2 × 10−5), and two other SMs (d18:2/21:0, d16:2/23:0 and d17:1/24:1(15Z)) (ORs=1.27 and 1.26, respectively; P<5.6 × 10−4). Our findings were not materially altered with further adjustment for smoking, BMI, height and diabetes. Analysis restricted to high-grade glioma cases (n=595) showed similar associations, whereas low-grade glioma (n=207) showed fewer metabolites related to risk (top signals of sphingosine, sphinganine and L-glutamic acid, each inversely associated). We did, however, observe that the 2-hydroxyglutarate/alpha-ketoglutarate ratio was significantly associated with low-grade disease (OR=2.9; P=0.0008), which was most pronounced among cases diagnosed >10 years following blood collection (OR=9.99; P=0.003). Glioblastoma (n=485) showed similar positive risk estimates including for 3-methylglutarylcarnitine which achieved Bonferroni significance (OR=1.35; P=3 × 10−5) and several SMs. In contrast, the top metabolite for astrocytoma risk (n=88) was tyrosine (OR=0.55; P=0.002). Gene-set and principal components analyses confirmed that the sub-pathways of SM, dihydro-SM, fatty acid, and dihydroceramide metabolism were significantly associated with overall glioma risk, and dihydro-SM, dihydroceramide, SM, lactosylceramide, glutathione, and purine metabolism were associated with high-grade glioma (all P values <0.05). These novel findings regarding altered metabolic pathways/dysregulation occurring years before glioma diagnoses may be related to etiologic factors or subclinical disease. Citation Format: Jiaqi Huang, Bin Zhao, Stephanie J. Weinstein, Marian L. Neuhouser, Steve C. Moore, Heather Eliassen, David J. Cote, Loic Le Marchand, Lorelei Mucci, Ying Yang, Victoria Stevens, Wei Zheng, Xiao-Ou Shu, Demetrius Albanes. Blood metabolomic profile of glioma risk: A pooled, multi-cohort analysis in COMETS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3432.

Abstract 4342: Breast cancer multi-omic single cell profiling identifies key progressive disease markers

Abstract Breast cancer is one of the most diagnosed cancers and has a complex tumor microenvironment (TME). Breast tumors are subtyped by receptor expression, and all have a high degree of cellular heterogeneity. We analyzed tumors from 8 individuals with early-stage breast cancer. The patients had either triple negative breast (TNBC) or an estrogen receptor (ER) positive cancer. The tumors and normal adjacent tissue were profiled by single-nuclei gene expression (GEX) and ATAC data using the 10X multi-ome methodology. Gene set analysis identified specific epithelial clusters associated with the estrogen early/late response, epithelial mesenchymal transition, and several key cytokine pathways. From those epithelial cells, a subset highly proliferative in nature emerged. It consists of both ER positive and TNBC tumor types enriched for specific DNA transcriptional motifs including those for stem cell (Yamanaka) factors and other cellular proliferation motifs. GEX proliferation markers such as MKi-67 were also highly expressed. Overlaying GEX and ATAC data, it was possible to filter the numerous differentially expressed genes and peaks into a subset of genes specific to progressive breast cancer within the proliferative cluster. Those genes included ESRP1, POP4, CCNE1, and AAMDC all of which are associated with progressive breast cancer. To further elucidate the complexity of the TME, we examined clusters of fibroblasts from our dataset. A difference in cancer associated fibroblast marker gene expression was related to the level of ER expression expressed in the tumor epithelium. In general, ER-low tumor fibroblasts had higher expression of immune cancer associated fibroblasts (iCAFs) markers while ER-positive and TNBC tumors had greater expression of extracellular matrix associated markers (emCAFs). Specific clusters were enriched for emCAFs and iCAFs representing another critical part of the TME. In conclusion, using a multi-modal single-cell approach to the TME allowed the identification of an aggressive cluster of epithelial cells marked by genes for cellular proliferation and noted therapeutic targets while also characterizing other critical TME components especially, CAFs. Citation Format: Hugh Russell, Manisha Rao, Grant Duclos, Rogelio Aguilar, Nick Barkas, Megan Callahan, Ojasvi Chaudhary, Peter Gathungu, Chris Rands, Varsha Shankarappa, Daniel Stetson, Asaf Rotem, Maurizio Scaltriti, Brian Dougherty. Breast cancer multi-omic single cell profiling identifies key progressive disease markers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4342.

Abstract 7559: Multi-state gene cluster switches determining the adaptive mitochondrial and metabolic landscape of breast cancer

Abstract Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution, where they represent important therapeutic targets. However, the full spectrum, characteristics and regulation of existing metabolic switches are unknown. We propose that metabolic switches can be recognised by locating large alternating gene expression patterns and associate them with specific metabolic states. We developed a method to identify interspersed genesets by massive correlated biclustering (MCbiclust) and to predict their metabolic wiring. Testing the method on major breast cancer transcriptome datasets we discovered a series of gene sets with switch-like behaviour, predicting mitochondrial content, activity and central carbon fluxes in tumours associated with different switch positions. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labelled substrates and were ultimately extended by geneset analysis to link metabolic alterations to cellular states, thus predicting tumour pathology, prognosis and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Citation Format: Michela Menegollo, Robert Bentham, Neill Patani, Robert C. Stein, Mariia Yuneva, Gyorgy Szabadkai. Multi-state gene cluster switches determining the adaptive mitochondrial and metabolic landscape of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7559.

Systemic inflammation activates coagulation and immune cell infiltration pathways in brains with propagating α-synuclein fibril aggregates

Synucleinopathies are a group of diseases characterized by brain aggregates of α-synuclein (α-syn). The gradual accumulation of α-syn and the role of inflammation in early-stage pathogenesis remain poorly understood. We explored this interaction by inducing chronic inflammation in a common pre-clinical synucleinopathy mouse model. Three weeks post unilateral intra-striatal injections of human α-syn pre-formed fibrils (PFF), mice underwent repeated intraperitoneal injections of 1 mg/ml lipopolysaccharide (LPS) for 3 weeks. Histological examinations of the ipsilateral site showed phospho-α-syn regional spread and LPS-induced neutrophil recruitment to the brain vasculature. Biochemical assessment of the contralateral site confirmed spreading of α-syn aggregation to frontal cortex and a rise in intracerebral TNF-α, IL-1β, IL-10 and KC/GRO cytokines levels due to LPS. No LPS-induced exacerbation of α-syn pathology load was observed at this stage. Proteomic analysis was performed contralateral to the PFF injection site using LC-MS/MS. Subsequent downstream Reactome Gene-Set Analysis indicated that α-syn pathology alters mitochondrial metabolism and synaptic signaling. Chronic LPS-induced inflammation further lead to an overrepresentation of pathways related to fibrin clotting as well as integrin and B cell receptor signaling. Western blotting confirmed a PFF-induced increase in fibrinogen brain levels and a PFF + LPS increase in Iba1 levels, indicating activated microglia. Splenocyte profiling revealed changes in T and B cells, monocytes, and neutrophils populations due to LPS treatment in PFF injected animals. In summary, early α-syn pathology impacts energy homeostasis pathways, synaptic signaling and brain fibrinogen levels. Concurrent mild systemic inflammation may prime brain immune pathways in interaction with peripheral immunity.

Multi-omics integration identifies regulatory factors underlying bovine subclinical mastitis

BackgroundMastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry. Multi-omics approaches enable the comprehensive investigation of the complex interactions between multiple layers of information to provide a more holistic view of disease pathogenesis. Therefore, this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data (mRNA and lncRNA), small RNA sequencing data (miRNA) and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes.ResultsFunctional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis, provided further insights into subclinical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis. The abundant genomic and epigenomic signatures with significant alterations related to subclinical mastitis were observed, including 30,846, 2552, 1276 and 57 differential methylation haplotype blocks (dMHBs), differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs), respectively. Next, 5 factors presenting the principal variation of differential multi-omics signatures were identified. The important roles of Factor 1 (DEG, DEM and DEL) and Factor 2 (dMHB and DEM), in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed. Each of the omics within Factors 1 and 2 explained about 20% of the source of variation in subclinical mastitis. Also, networks of important functional gene sets with the involvement of multi-omics signatures were demonstrated, which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis. Furthermore, multi-omics integration enabled the association of the epigenomic regulatory factors (dMHBs, DELs and DEMs) of altered genes in important pathways, such as ‘Staphylococcus aureus infection pathway’ and ‘natural killer cell mediated cytotoxicity pathway’, etc., which provides further insights into mastitis regulatory mechanisms. Moreover, few multi-omics signatures (14 dMHBs, 25 DEGs, 18 DELs and 5 DEMs) were identified as candidate discriminant signatures with capacity of distinguishing subclinical mastitis cows from healthy cows.ConclusionThe integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis, which may ultimately lead to the development of more effective mastitis control and management strategies.

Cerebral visual impairment: genetic diagnoses and phenotypic associations

BackgroundCerebral visual impairment (CVI) is the most common form of paediatric visual impairment in developed countries. CVI can arise from a host of genetic or acquired causes, but there has...

LRRC59 promotes the progression of oral squamous cell carcinoma by interacting with SRP pathway components and enhancing the secretion of CKAP4-containing exosomes

BackgroundAs a ribosome receptor, LRRC59 was thought to regulate mRNA translation on the ER membrane. Evidence suggests that LRRC59 is overexpressed in a number of human malignancies and is associated with poor prognoses, but its primary biological function in the development of oral squamous cell carcinoma (OSCC) remains obscure. ObjectiveThe purpose of this study is to investigate at the expression changes and functional role of LRRC59 in OSCC. MethodsLRRC59 gene expression and correlation with prognosis of OSCC patients were first examined using the data from The Cancer Genome Atlas (TCGA) databases. Following that, a series of functional experiments, including cell counting kit-8, cell cycle analysis, wound healing assays, and transwell assays, were carried out to analyze the biological roles of LRRC59 in tumor cells. Mechanistically, we employed Tandem Affinity Purification-Mass Spectrometry (TAP-MS) approach to isolate and identify protein complexes of LRRC59. Downstream regulatory proteins of LRRC59 were verified through immunoprecipitation and immunofluorescence experiments. Furthermore, we isolated exosomes from OSCC cell supernatant and conducted co-culture experiments to examine the effect of LRRC59 knockdown on OSCC cells. ResultsIn samples from OSCC patients, LRRC59 was highly expressed and correlated with poor prognoses. Moreover, the gene sets analysis based on TCGA RNA-seq data indicated that LRRC59 seemed to be strongly related with protein secretory and OSCC migration. Upregulated levels of LRRC59 are more prone to lymph node metastasis in OSCC patients. LRRC59 knockdown impaired the ability of OSCC cell proliferation, migration, and invasion invitro. Mechanistically, our TAP-MS data situate LRRC59 in a functional nexus for mRNA translation regulation via interactions with SRP pathway components, translational initiation factors, CRD-mediated mRNA stabilization factors. More importantly, we found that LRRC59 interacted with cytoskeleton-associated protein 4 (CKAP4) and promoted the formation of CKAP4-containing exosomes. We also revealed that the LRRC59-CKAP4 axis was a crucial regulator of CKAP4-containing exosome secretion in OSCC cells for migration and invasion. ConclusionsTherefore, based on our findings, LRRC59 may serve as a potential biomarker for OSCC patients, and LRRC59-induced exosome secretion via the CKAP4 axis may serve as a potential therapeutic target for OSCC.

High Frequencies of Genetic Variants in Patients with Atypical Femoral Fractures.

This study explores the genetic factors associated with atypical femoral fractures (AFF), rare fractures associated with prolonged anti-resorptive therapy. AFF are fragility fractures that typically appear in the subtrochanteric or diaphyseal regions of the femur. While some cases resemble fractures in rare genetic bone disorders, the exact cause remains unclear. This study investigates 457 genes related to skeletal homeostasis in 13 AFF patients by exome sequencing, comparing the results with osteoporotic patients (n = 27) and Iberian samples from the 1000 Genomes Project (n = 107). Only one AFF case carried a pathogenic variant in the gene set, specifically in the ALPL gene. The study then examined variant accumulation in the gene set, revealing significantly more variants in AFF patients than in osteoporotic patients without AFF (p = 3.7 × 10-5), particularly in ACAN, AKAP13, ARHGEF3, P4HB, PITX2, and SUCO genes, all of them related to osteogenesis. This suggests that variant accumulation in bone-related genes may contribute to AFF risk. The polygenic nature of AFF implies that a complex interplay of genetic factors determines the susceptibility to AFF, with ACAN, SUCO, AKAP13, ARHGEF3, PITX2, and P4HB as potential genetic risk factors. Larger studies are needed to confirm the utility of gene set analysis in identifying patients at high risk of AFF during anti-resorptive therapy.

Expression patterns of E2Fs identify tumor microenvironment features in human gastric cancer.

E2F transcription factors are associated with tumor development, but their underlying mechanisms in gastric cancer (GC) remain unclear. This study explored whether E2Fs determine the prognosis or immune and therapy responses of GC patients. E2F regulation patterns from The Cancer Genome Atlas (TCGA) were systematically investigated and E2F patterns were correlated with the characteristics of cellular infiltration in the tumor microenvironment (TME). A principal component analysis was used to construct an E2F scoring model based on prognosis-related differential genes to quantify the E2F regulation of a single tumor. This scoring model was then tested in patient cohorts to predict effects of immunotherapy. Based on the expression profiles of E2F transcription factors in GC, two different regulatory patterns of E2F were identified. TME and survival differences emerged between the two clusters. Lower survival rates in the Cluster2 group were attributed to limited immune function due to stromal activation. The E2F scoring model was then constructed based on the E2F-related prognostic genes. Evidence supported the E2F score as an independent and effective prognostic factor and predictor of immunotherapy response. A gene-set analysis correlated E2F score with the characteristics of immune cell infiltration within the TME. The immunotherapy cohort database showed that patients with a higher E2F score demonstrated better survival and immune responses. This study found that differences in GC prognosis might be related to the E2F patterns in the TME. The E2F scoring system developed in this study has practical value as a predictor of survival and treatment response in GC patients.

Identifying novel chemical-related susceptibility genes for five psychiatric disorders through integrating genome-wide association study and tissue-specific 3'aQTL annotation datasets.

The establishment of 3'aQTLs comprehensive database provides an opportunity to help explore the functional interpretation from the genome-wide association study (GWAS) data of psychiatric disorders. In this study, we aim to search novel susceptibility genes, pathways, and related chemicals of five psychiatric disorders via GWAS and 3'aQTLs datasets. The GWAS datasets of five psychiatric disorders were collected from the open platform of Psychiatric Genomics Consortium (PGC, https://www.med.unc.edu/pgc/ ) and iPSYCH ( https://ipsych.dk/ ) (Demontis et al. in Nat Genet 51(1):63-75, 2019; Grove et al. in Nat Genet 51:431-444, 2019; Genomic Dissection of Bipolar Disorder and Schizophrenia in Cell 173: 1705-1715.e1716, 2018; Mullins et al. in Nat Genet 53: 817-829; Howard et al. in Nat Neurosci 22: 343-352, 2019). The 3'untranslated region (3'UTR) alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) summary datasets of 12 brain regions were obtained from another public platform ( https://wlcb.oit.uci.edu/3aQTLatlas/ ) (Cui et al. in Nucleic Acids Res 50: D39-D45, 2022). First, we aligned the GWAS-associated SNPs of psychiatric disorders and datasets of 3'aQTLs, and then, the GWAS-associated 3'aQTLs were identified from the overlap. Second, gene ontology (GO) and pathway analysis was applied to investigate the potential biological functions of matching genes based on the methods provided by MAGMA. Finally, chemical-related gene-set analysis (GSA) was also conducted by MAGMA to explore the potential interaction of GWAS-associated 3'aQTLs and multiple chemicals in the mechanism of psychiatric disorders. A number of susceptibility genes with 3'aQTLs were found to be associated with psychiatric disorders and some of them had brain-region specificity. For schizophrenia (SCZ), HLA-A showed associated with psychiatric disorders in all 12 brain regions, such as cerebellar hemisphere (P = 1.58 × 10-36) and cortex (P = 1.58 × 10-36). GO and pathway analysis identified several associated pathways, such as Phenylpropanoid Metabolic Process (GO:0009698, P = 6.24 × 10-7 for SCZ). Chemical-related GSA detected several chemical-related gene sets associated with psychiatric disorders. For example, gene sets of Ferulic Acid (P = 6.24 × 10-7), Morin (P = 4.47 × 10-2) and Vanillic Acid (P = 6.24 × 10-7) were found to be associated with SCZ. By integrating the functional information from 3'aQTLs, we identified several susceptibility genes and associated pathways especially chemical-related gene sets for five psychiatric disorders. Our results provided new insights to understand the etiology and mechanism of psychiatric disorders.

Salmonella-mediated methionine deprivation drives immune activation and enhances immune checkpoint blockade therapy in melanoma

BackgroundAlthough immune checkpoint inhibitor (ICI)-based therapy is advantageous for patients with advanced melanoma, resistance and relapse are frequent. Thus, it is crucial to identify effective drug combinations and develop new...

Predicting clinical outcomes in the S1314-COXEN trial using a multimodal deep learning model integrating histopathology, cell types, and gene expression.

533 Background: Accurate prediction of response to neoadjuvant chemotherapy (NAC) is essential for optimizing treatment outcomes in muscle-invasive bladder cancer (MIBC). We present a cutting-edge multimodal deep learning model aimed at predicting outcomes of NAC from accessible H & E images and molecular data. Methods: We designed a Graph-based Multi-modal Late Fusion (GMLF) deep learning model that integrates three types of data from the S1314-COXEN clinical trial: 1) Neural embeddings from 182 whole slide images (WSIs), generated via the ResNet50 architecture. 2) Cell types based on morphology, including cancer, necrotic, immune, and stromal cells, and their spatial positions within the tumor microenvironment extracted from WSIs using the HoVer-Net framework, a multiple-branch convolutional neural network for predicting cell types. 3) Patient-level RNA expression data derived from 1,071-dimensional gene expression vectors. The dataset was randomly divided into 80% for model training, using 5-fold cross-validation (5-fold CV), and 20% for internal validation. Results: Our dataset included 182 WSIs of 180 patients who were randomized to receive either gemcitabine-cisplatin or dose-dense methotrexate-vinblastine-adriamycin-doxorubicin-cisplatin. Of all patients, 30.8% demonstrated a complete pathological response (pT0 after radical cystectomy). Our GMLF model achieved an AUROC of 0.7417 ± 0.1021 for predicting whether a patient showed a complete pathologic response to NAC in a 5-fold CV. Using an 80/20 training and validation split, the model yielded an AUROC of 0.7236. Interestingly, our model emphasized the contribution of spatial information collected from WSIs. For selecting the best architecture for the histology branch, the Slidegraph+ framework, a graph neural network-based model, achieved an AUROC of 0.6938 ± 0.0565, significantly outperforming patch-level models as the second-best, which achieved AUROC of 0.5572 ± 0.1793. Shapley Additive Explanation (SHAP) analysis revealed RNA expression branch as the most influential, with a mean SHAP magnitude of 0.13, followed by the neural embeddings at 0.12. SHAP-based interpretation identified the most important expressed genes impacting model predictability, including TP63. Our gene set analysis identified the most influential pathways for predicting response, which included basal differentiation and myo-fibroblast enrichment (p-value < 0.05). Conclusions: Our interpretable GMLF model accurately predicts NAC response in patients with MIBC by integrating standard H&E images and RNA expression data. Our model interpretations not only revealed the importance of each modality but also uncovered histopathological, cellular, and molecular underpinnings of response to NAC in MIBC. These findings open the door to AI-guided development of precision therapies for MIBC. Clinical trial information: NCT02177695 .