Set Of Genes Research Articles

Transcriptional dynamic changes in energy metabolism, protein synthesis and cell cycle regulation reveal the biological adaptation mechanisms of juvenile Acrossocheilus wenchowensis under acute temperature changes

In recent years, frequent acute temperature changes have posed a serious threat to the physiology and survival of fish. This study utilized RNA-Seq technology to analyze the transcriptional dynamics in the muscle tissues of Acrossocheilus wenchowensis under various acute temperature conditions (16◦C, 20◦C, 24◦C, 28◦C and 32◦C). Through comprehensive analysis, we identified 11509 differentially expressed genes (DEGs), a gene set (profiles 19) that was significantly up-regulated with increasing temperature, and two weighted gene co-expression network analysis (WGCNA) modules that were significantly correlated with acute temperature changes. Furthermore, we identified 28 transcription factors that are pivotal in oxidative stress and energy metabolism under acute temperature changes. Our results showed that, compared to the control group (24°C), KEGG functional enrichment analysis revealed significant enrichment of DEGs in the cell cycle, DNA replication, and p53 signaling pathway, with an overall trend of suppressed expression. This indicates that maintaining cell stability and reducing cell damage is an effective adaptive mechanism for A. wenchowensis to cope with acute temperature changes. Through STEM analysis and the black WGCNA module associated with high-temperature stress, we identified significant up-regulation of pathways and hub genes related to energy metabolism including oxidative phosphorylation, TCA cycle, purine metabolism, and glutathione metabolism, as well as the central roles of signal transduction pathways such as MAPK signaling pathway and AMPK signaling pathway, which synergistically regulate energy production. Under acute low-temperature stress, the turquoise WGCNA module highlighted significant up-regulation of hub genes associated with Ribosomal and Spliceosomal pathways related to protein synthesis and processing, as well as activation of calcium signaling pathways, which plays an important role in maintaining cellular function during low-temperature adaptation. These findings provide a critical theoretical and molecular basis for the adaptation of eurythermal fish to rapid temperature changes.

Comprehensive Genomic Analysis of Puerarin in Inhibiting Bladder Urothelial Carcinoma Cell Proliferation and Migration.

Bladder urothelial carcinoma (BUC) ranks second in the incidence of urogenital system tumors, and the treatment of BUC needs to be improved. Puerarin, a traditional Chinese medicine (TCM), has been shown to have various effects such as anti-cancer effects, the promotion of angiogenesis, and anti-inflammation. This study investigates the effects of puerarin on BUC and its molecular mechanisms. Through GeneChip experiments, we obtained differentially expressed genes (DEGs) and analyzed these DEGs using the Ingenuity® Pathway Analysis (IPA®), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analyses. The Cell Counting Kit 8 (CCK8) assay was used to verify the inhibitory effect of puerarin on the proliferation of BUC T24 cells. String combined with Cytoscape® was used to create the Protein-Protein Interaction (PPI) network, and the MCC algorithm in cytoHubba plugin was used to screen key genes. Gene Set Enrichment Analysis (GSEA®) was used to verify the correlation between key genes and cell proliferation. A total of 1617 DEGs were obtained by GeneChip. Based on the DEGs, the IPA® and pathway enrichment analysis showed they were mainly enriched in cancer cell proliferation and migration. CCK8 experiments proved that puerarin inhibited the proliferation of BUC T24 cells, and its IC50 at 48 hours was 218μmol/L. Through PPI and related algorithms, 7 key genes were obtained: ITGA1, LAMA3, LAMB3, LAMA4, PAK2, DMD, and UTRN. GSEA showed that these key genes were highly correlated with BUC cell proliferation. Survival curves showed that ITGA1 upregulation was associated with poor prognosis of BUC patients. Our findings support the potential antitumor activity of puerarin in BUC. To the best of our knowledge, bioinformatics investigation suggests that puerarin demonstrates anticancer mechanisms via the upregulation of ITGA1, LAMA3 and 4, LAMB3, PAK2, DMD, and UTRN, all of which are involved in the proliferation and migration of bladder urothelial cancer cells.

Exploration of Key Genes Combining with Immune Infiltration Level andTumor Mutational Burden in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a lethal malignancy due to its heterogeneity and aggressive behavior. Recently, somatic mutations and tumor cell interactions with the surrounding tumor immune microenvironment (TIME) have been reported to participate in HCC carcinogenesis and predict HCC progression. In this study, we aimed to investigate the association between tumor mutational burden (TMB) and TIME in HCC. Additionally, we sought to identify differentially expressed genes (DEGs) associated with HCC prognosis and progression. The expression, clinical, and mutational data were downloaded from the cancer genome atlas (TCGA) database. The immune infiltration levels and TMB levels of the HCC samples were estimated and the samples were divided into immune cluster (ICR)-1 and 2 based on immune infiltration score and high and low TMB groups based on TMB score. Thereafter, differential gene expression analysis was conducted to identify the DEGs in the ICR1/2 and high/low TMB groups, and the intersecting DEGs were selected. Thereafter, Cox regression analysis was performed on 89 significant DEGs, among which 19 were associated with prognosis. These 19 DEGs were then used to construct a prognostic model based on their expression levels and regression coefficients. Thereafter, we analyzed the DEGs in mutant and wildtype TP53 HCC samples and identified high BCL10 and TRAF3 expression in the mutant TP53 samples. BCL10 and TRAF3 expression was detected by real-time quantitative reverse transcription PCR and immunohistochemistry, and their clinical correlation, biological function, and immune infiltration levels were analyzed by chi-square analyses, Gene Set Enrichment Analysis (GSEA), and "ssGSEA", respectively. The results of our study revealed that immune infiltration level was correlated with TMB and that they synergistically predicted poor prognosis of HCC patients. DEGs enriched in immune-related pathways could serve as indicators of immunotherapy response in HCC. Among these DEGs, BCL10 and TRAF3 were highly expressed in HCC tissues, especially in the mutant TP53 group, and they co-operatively exhibited immunological function, thereby affecting HCC progression and prognosis. In this study, we identified BCL10 and TRAF3 as potential prognostic indicators in HCC patients. Additionally, we found that BCL10 and TRAF3 influence TMB and TIME in HCC patients and can be used for the development of immune-based therapies for improving the long-term survival of HCC patients.

Molecular dissection of the parental contribution in Paeonia Itoh hybrids.

Hybrid breeding between herbaceous peonies (the maternal parent) and tree peonies (the paternal parent) results in Paeonia Itoh hybrids (Itoh peonies), a triploid species that combines advantageous traits from both parental species, thus offering great economic value. However, the exact genetic contribution of the two parents is unclear. In this study, we introduce a straightforward approach utilizing heterozygous SNPs and Sanger sequencing of targeted gene fragments to trace the original bases back to their parents in Itoh peonies. Our results indicate that in triploid Itoh peonies, only one set of genes are derived from herbaceous peonies, and two sets of genes are derived from the tree peonies. Notably, the presence of three distinct bases of heterozygous SNPs across multiple Itoh cultivars suggests that the gametes from the paternal parents carry two sets of heterozygous homologous chromosomes, which could be due to meiosis I failure during gamete formation. To validate our method's effectiveness in parentage determination, we analyzed two Itoh hybrids and their parents, confirming its practical utility. This research presents a method to reveal the parental genetic contribution in Itoh peonies, which could enhance the efficiency and precision of hybrid breeding programs of triploids in Paeonia and other plant species.

Loss of Lkb1 cooperates with BrafV600E and ultraviolet radiation, increasing melanoma multiplicity and neural-like dedifferentiation.

The mechanisms that work alongside BRAFV600E oncogene in melanoma development, in addition to ultraviolet (UV) radiation (UVR), are of great interest. Analysis of human melanoma tumors [data from The Cancer Genome Atlas (TCGA)] revealed that 50% or more of the samples expressed no or low amounts of serine/threonine protein kinase STK11 (also known as LKB1) protein. Here, we report that, in a mouse model, concomitant neonatal BrafV600E activation and Lkb1 tumor suppressor ablation in melanocytes led to full melanoma development. A single postnatal dose of UVB radiation had no effect on melanoma onset in Lkb1-depleted mice compared with BrafV600E-irradiated mice, but increased tumor multiplicity. In concordance with these findings and previous reports, Lkb1-null irradiated mice exhibited deficient DNA damage repair (DDR). Histologically, tumors lacking Lkb1 were enriched in neural-like tumor morphology. Genetic profiling and gene set enrichment analyses of tumor sample mutated genes indicated that loss of Lkb1 promoted the selection of altered genes associated with neural differentiation processes. Thus, these results suggest that the loss of Lkb1 cooperates with BrafV600E and UVR, impairing the DDR and increasing melanoma multiplicity and neural-like dedifferentiation.

GENEVIC: GENetic data exploration and visualization via intelli- gent interactive console.

The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's 'copilot'. It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer's disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. GENEVIC's operation is user-friendly, accessible without any specialized training, secured by Azure OpenAI's HIPAA-compliant infrastructure, and evaluated for its efficacy through real-time query testing. As a prototype, GENEVIC is set to advance genetic research, enabling informed biomedical decisions. GENEVIC is publicly accessible at https://genevic- anath2024.streamlit.app. The underlying code is open-source and available via GitHub at https://github.com/bsml320/GENEVIC.git (also at https://github.com/anath2110/GENEVIC.git). Available at Bioinformatics online and at https://github.com/bsml320/GENEVIC_Supplementary.git (also at https://github.com/anath2110/GENEVIC_Supplementary.git).

Plant Circadian Clocks: Unravelling the Molecular Rhythms of Nature

The circadian clock is a fundamental biological mechanism that allows organisms to synchronize their internal processes with the external environment, thereby optimizing growth, development, and physiology. In plants, circadian rhythms govern various aspects of their life cycle, including germination, leaf movement, flowering, and responses to environmental cues such as light and temperature. Understanding the molecular mechanisms underlying plant circadian clocks is essential not only for elucidating fundamental principles of plant biology but also for applications in agriculture and biotechnology. This review delves into the intricate molecular networks that comprise plant circadian clocks, focusing on key components and their interactions. At the core of these clocks are transcription-translation feedback loops (TTFLs) involving a set of clock genes. The central oscillator, composed of genes such as CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), LATE ELONGATED HYPOCOTYL (LHY), TIMING OF CAB EXPRESSION 1 (TOC1), and PSEUDO-RESPONSE REGULATOR (PRR) genes, drives rhythmic expression of downstream clock-controlled genes (CCGs). The interplay between positive and negative regulators within the TTFLs generates robust oscillations with a period of approximately 24 hours. Additionally, post-translational modifications, protein-protein interactions, and chromatin remodeling contribute to fine-tuning the circadian system, allowing plants to adapt to changing environmental conditions. Light perception through photoreceptors such as phytochromes and cryptochromes plays a crucial role in entraining the circadian clock to external cues, ensuring synchronization with the day-night cycle. Furthermore, recent advancements in omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have provided unprecedented insights into the complexity and plasticity of plant circadian clocks. Integration of multi-omics data has facilitated the construction of comprehensive regulatory networks and computational models, enabling predictive understanding of clock function and behavior under diverse conditions. Overall, unraveling the molecular rhythms of plant circadian clocks not only enhances our knowledge of fundamental biological processes but also holds promise for improving crop productivity, stress resilience, and sustainability in agriculture through targeted manipulation of clock components and their associated pathways. Future research endeavors will undoubtedly continue to unveil the intricacies of these fascinating timekeeping mechanisms, further enriching our understanding of the molecular rhythms of nature.

Identification of potential diagnostic genes for atherosclerosis in women with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS), which is the most prevalent endocrine disorder among women in their reproductive years, is linked to a higher occurrence and severity of atherosclerosis (AS). Nevertheless, the precise manner in which PCOS impacts the cardiovascular well-being of women remains ambiguous. The Gene Expression Omnibus database provided four PCOS datasets and two AS datasets for this study. Through the examination of genes originating from differentially expressed (DEGs) and critical modules utilizing functional enrichment analyses, weighted gene co-expression network (WGCNA), and machine learning algorithm, the research attempted to discover potential diagnostic genes. Additionally, the study investigated immune infiltration and conducted gene set enrichment analysis (GSEA) to examine the potential mechanism of the simultaneous occurrence of PCOS and AS. Two verification datasets and cell experiments were performed to assess biomarkers' reliability. The PCOS group identified 53 genes and AS group identified 175 genes by intersecting DEGs and key modules of WGCNA. Then, 18 genes from two groups were analyzed by machine learning algorithm. Death Associated Protein Kinase 1 (DAPK1) was recognized as an essential gene. Immune infiltration and single-gene GSEA results suggest that DAPK1 is associated with T cell-mediated immune responses. The mRNA expression of DAPK1 was upregulated in ox-LDL stimulated RAW264.7 cells and in granulosa cells. Our research discovered the close association between AS and PCOS, and identified DAPK1 as a crucial diagnostic biomarker for AS in PCOS.

Identification of ENTPD1 as a novel biomarker linking allergic rhinitis and systemic lupus erythematosus

Several studies reveal that allergic rhinitis (AR) is a significant risk factor of systemic lupus erythematosus (SLE). However, studies investigating the common pathogenesis linking AR and SLE are lacking. Our study aims to search for the shared biomarkers and mechanisms that may provide new therapeutic targets for preventing AR from developing SLE. GSE50223 for AR and GSE103760 for SLE were downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to explore the functions of shared DEGs. Hub genes were screened by cytoHubba (a plugin of Cytoscape) and validated in another two datasets. Gene set enrichment analysis (GSEA) and single-sample Gene set enrichment analysis (ssGSEA) algorithm were applied to understand the functions of hub gene. ENTPD1 was validated as a hub gene between AR and SLE. GSEA results revealed that ENTPD1 was associated with KRAS_SIGNALING_UP pathway in AR and related to HYPOXIA, TGF_BETA_SIGNALING and TNFA_SIGNALING_VIA_NFKB pathways in SLE. The expression of ENTPD1 was positively correlated with activated CD8 T cell in both diseases. Thus, ENTPD1 may be a novel therapeutic target for preventing AR from developing SLE.

MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images

The utility of deep neural nets has been demonstrated for mapping hematoxylin-and-eosin (H&E) stained image features to expression of individual genes. However, these models have not been employed to discover clinically relevant spatial biomarkers. Here we develop MOSBY (Multi-Omic translation of whole slide images for Spatial Biomarker discoverY) that leverages contrastive self-supervised pretraining to extract improved H&E whole slide images features, learns a mapping between image and bulk omic profiles (RNA, DNA, and protein), and utilizes tile-level information to discover spatial biomarkers. We validate MOSBY gene and gene set predictions with spatial transcriptomic and serially-sectioned CD8 IHC image data. We demonstrate that MOSBY-inferred colocalization features have survival-predictive power orthogonal to gene expression, and enable concordance indices highly competitive with survival-trained multimodal networks. We identify and validate (1) an ER stress-associated colocalization feature as a chemotherapy-specific risk factor in lung adenocarcinoma, and (2) the colocalization of T effector cell vs cysteine signatures as a negative prognostic factor in multiple cancer indications. The discovery of clinically relevant biologically interpretable spatial biomarkers showcases the utility of the model in unraveling novel insights in cancer biology as well as informing clinical decision-making.

Characterization of SUSD3 as a novel prognostic biomarker and therapeutic target for breast cancer.

Breast cancer (BC) remains a significant global health challenge, contributing substantially to cancer-related deaths worldwide. Its prevalence and associated death rates remain alarmingly high, highlighting the persistent public health burden. The objective of this study was to systematically examine the involvement of SUSD3 (Sushi Domain-Containing 3) in BC, highlighting its crucial role in the pathogenesis and progression of this disease. BC-related gene microarray data, along with corresponding clinicopathological information, were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Leveraging TIMER and HPA databases, we conducted comparative analyses to evaluate SUSD3 expression in BC. We then analyzed the association between SUSD3 and clinical traits, as well as the prognostic value of SUSD3. SUSD3-related differential expression genes (DEGs) were sent for analysis utilizing GO, KEGG, and GSEA. We utilized SUSD3 mRNA expression to assess immune cells' scores in BC tissues calculated by single-sample enrichment analyses based on "CIBERSORT" R package. Drug sensitivity analysis was used to screen potential drugs sensitive to SUSD3. R software was used for statistical analyses and graphical representation of the data. Our findings confirmed a significant upregulation of SUSD3 expression in BC, which correlated with a favorable prognosis. Clinical correlation analysis further emphasized the strong association between SUSD3 expression and key clinical parameters like estrogen receptor (ER) status, progesterone receptor (PR) status, stage, and T classification in breast cancer. Univariate and multivariate Cox regression analyses showed that SUSD3 could be used as an independent prognostic factor for BC. Differentially expressed genes (DEGs) co-expressed with SUSD3 were significantly associated with various biological processes, such as the cell cycle, DNA replication, p53 signaling pathway, cancer-related pathways, and Wnt signaling pathway, as indicated by gene set enrichment analysis (GSEA). Furthermore, our analysis demonstrated that SUSD3 generally exhibited negative associations with immune modulators. Drug sensitivity analysis revealed positive correlations between SUSD3 and the efficacy of Fulvestrant, Raloxifene, and Fluphenazine. The research emphasizes the significance of SUSD3 as a potential marker for BC, providing insights into the underlying molecular mechanisms implicated in tumorigenesis. SUSD3 holds promise in helping the classification of breast cancer pathological groups, predicting prognosis, and facilitating targeted therapy.

Identification of Key Genes and Signaling Pathways in Entrectinibresistant Non-small Cell Lung Cancer Using Bioinformatic Analysis and Experimental Verification.

Entrectinib, a ROS1 inhibitor, is effective in patients with ROS1-positive non-small-cell lung cancer (NSCLC). However, entrectinib resistance remains a challenge worldwide. The biomarkers of entrectinib resistance and molecular mechanisms have not been clarified based on the Gene Expression Omnibus (GEO) database. The aim of this study is to identify key genes and signaling pathways involved in the development of entrectinib-resistant NSCLC through bioinformatics analysis and experimental validation. Differentially expressed genes (DEGs) were screened between entrectinib resistant and parental human NSCLC cell lines of the GSE214715 dataset, lung adenocarcinoma (LUAD) and non-tumor adjacent tissues of the GSE75037 dataset, and NSCLC and non-tumor adjacent tissues of the GSE18842 dataset. Functional enrichment analyses were performed, including Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Overlapped DEGs among those three datasets were identified using the Venn diagram package. The transcriptional levels of key genes were investigated using the University of ALabama at Birmingham CANcer data analysis Portal (UALCAN). The association between transcriptional levels of key genes and survival was analyzed using Kaplan-Meier Plotter (https://www.kmplot.com/analysis/). The correlations between hub genes and immune cell infiltration were investigated using the Tumor Immune Estimation Resource (TIMER) database. Specific signaling pathway enrichment analysis was performed using Gene Set Enrichment Analysis (GSEA) of LinkedOmics. Competitive endogenous RNA (ceRNA) networks, genome-wide association studies (GWAS), and drug sensitivity analyses of key genes were further investigated. The expression of ZEB2 was subsequently confirmed in both parental HCC78 cells and entrectinib-resistant HCC78 cells using real-time quantitative polymerase chain reaction (qRT-PCR). 708 DEGs were identified between entrectinib-resistant CUTO28 (CUTO28-ER) and parental CUTO28 cell lines in the GSE214715 dataset. One thousand three hundred and ninety-five DEGs were identified between entrectinib resistant (CUTO37-ER) and parental CUTO37 cell lines in the GSE214715 dataset. Eight hundred and forty-nine DEGs were identified between LUAD and non-tumor adjacent tissues in the GSE75037 dataset. Seven hundred and sevety-three DEGs were identified between NSCLC and non-tumor adjacent tissues in the GSE18842 dataset. Among these three datasets, seven overlapped DEGs were identified, including ZBED2, CHI3L2, CELF2, SEMA5A, ZEB2, S100A12, and PDK4. Among these seven overlapped DEGs, the expression levels of CHI3L2, ZEB2, and S100A12 were downregulated in those three datasets. The results of analysis using the UALCAN database showed that these three genes were significantly downregulated in LUAD and LUSC patients compared with the normal population. However, only the lower transcriptional level of ZEB2 was linked to worse survival in patients with lung cancer. GSEA analysis revealed that ZEB2 was significantly negatively correlated with nucleotide excision repair (NER) in LUAD, and homologous recombination (HR) and NER in LUSC, which were linked to drug resistance. A ceRNA network of THRB-AS1/ has-miR-1293/ ZEB2 in LUAD was established. We have identified core genes associated with non-small cell resistance to entrectinib, including CHI3L2, ZEB2, and S100A12. ZEB2 is a core gene associated with acquired resistance to entetinib in NSCLC.

Genomic and transcriptomic profiles associated with response to eribulin and nivolumab combination in HER-2-negative metastatic breast cancer

BackgroundBiomarkers for predicting response to the immunotherapy and chemotherapy combination in breast cancer patients are not established. In this study, we report exploratory genomic and transcriptomic analyses of pretreatment tumor tissues from patients enrolled in phase II clinical trial of a combination of eribulin and nivolumab for HER-2-negative metastatic breast cancer (MBC) (KORNELIA trial, NCT04061863).MethodsWe analyzed associations between tumor molecular profiles based on genomic (n = 76) and transcriptomic data (n = 58) and therapeutic efficacy. Patients who achieved progression-free survival (PFS) ≥ 6 months were defined as PFS6-responders and PFS6-nonresponders otherwise.FindingsAnalyses on tumor mutation burden (TMB) showed a tendency toward a favorable effect on efficacy, while several analyses related to homologous recombination deficiency (HRD) indicated a potentially negative impact on efficacy. Patients harboring TP53 mutations showed significantly poor PFS6 rate and PFS, which correlated with the enrichment of cell cycle-related signatures in PFS6-nonresponders. High antigen presentation gene set enrichment scores (≥ median) were significantly associated with longer PFS. Naïve B-cell and plasma cell proportions were considerably higher in long responders (≥ 18 months).InterpretationGenomic features including TMB, HRD, and TP53 mutations and transcriptomic features related to immune cell profiles and cell cycle may distinguish responders. Our findings provide insights for further exploring the combination regimen and its biomarkers in these tumors.

11 Tumor evolution of brain-specific tropism in metastatic renal cell carcinoma

Abstract Background Brain metastases (BMets) pose a clinical challenge in the management of patients (pts) with metastatic renal cell carcinoma (RCC), leading to significant morbidity and mortality. Herein, we sought to comprehensively characterize the molecular landscape of BMets in RCC. Methods We performed panel-based DNA (DNAseq) and whole transcriptome (RNAseq) sequencing to analyze BMets, matched non-Brain metastases (nBMets), and matched primary renal tumors (PRT) from pts who underwent surgical resection of BMet(s) from RCC at our institution. Results Our cohort consisted of 95 samples from 53 patients (BMets = 54, nBMets = 14, PRT = 27) with clear cell histology in 45 pts (84.9%). DNAseq was available on 85 samples (50 pts). Patient-level mutations revealed recurring mutations in VHL (18 pts, 36%), TP53 (n = 12 pts, 24%), PBRM1 (12 pts, 24%), SETD2 (12 pts, 24%), and BAP1 (4 pts, 8%). Mutations within the MTOR signaling pathway were enriched, with 25 pts (50%) having at least one mutation in PTEN (12 pts, 24%), TSC1 (4 pts, 8%), TSC2 (5 pts, 10%), or MTOR (9 pts, 18%). Copy number analyses revealed frequent deletions in chr14q21 (34 pts, 68%) and chr9p21 (35 pts, 70%) and gains in chr20q13 (n = 31, 62%) and chr7p15 (n = 31, 62%). At the sample-level, PTEN mutations were more common in BMets (11 pts, 22.9%) vs nBMets (0 pts) and PRTs (3 pts, 12%, p = 0.124), as were deletions in chr13q22 (BMets = 13pts [26.5%], nBMet = 1pts [8.3%], PRT = 1 [4%]; p = 0.036) and gains in chr20q13 (BMets = 30pts [61.2%], nBMet = 4pts [33.3%], PRT = 8 [32%]; p = 0.03). Deletions in chr9p21 were enriched in BMets (32, 65.3%) and nBMets (8, 66.7%) relative to PRTs (7, 28%, p = 0.006), whereas other copy number alterations and somatic mutations exhibited similar proportions across specimen sites. Differential expression analysis performed on 86 samples (51 pts) identified 806 differentially expressed genes (DEGs) between BMets and PRT (adjusted [adj.] p < 0.05) and 399 DEGs between BMet and nBMets (adj. p < 0.05). Gene set enrichment analysis of MSigDB Hallmark gene sets revealed upregulation of MTORC1 signaling, glycolysis, and MYC targets in BMets comparted to PRT and nBMETs (adj. p <0.0001). In contrast, immune-related gene sets, such as interferon-alpha, interferon-gamma, and tumor necrosis factor-alpha, were enriched in PRT relative to BMets (adj. p <0.0001), but not nBMETs (adj. p > 0.05). CIBERSORT cellular deconvolution analysis comparing BMets with PRT revealed decreased proportions of M1 macrophages (p = 0.0003) and CD8+ T-cells (p = 0.0106), but an increased proportion of M2 macrophages (p = 0.0009). Conclusions RCC with brain metastases are characterized by distinct copy number alterations, enrichment of MTOR pathway mutations, MTOR pathway hyperactivation, and an immunosuppressive tumor milieu. These findings may hold therapeutic implications of MTOR pathway inhibition and immune modulators in treating RCC BMets.

Evolution and maintenance of mtDNA gene content across eukaryotes.

Across eukaryotes, most genes required for mitochondrial function have been transferred to, or otherwise acquired by, the nucleus. Encoding genes in the nucleus has many advantages. So why do mitochondria retain any genes at all? Why does the set of mtDNA genes vary so much across different species? And how do species maintain functionality in the mtDNA genes they do retain? In this review, we will discuss some possible answers to these questions, attempting a broad perspective across eukaryotes. We hope to cover some interesting features which may be less familiar from the perspective of particular species, including the ubiquity of recombination outside bilaterian animals, encrypted chainmail-like mtDNA, single genes split over multiple mtDNA chromosomes, triparental inheritance, gene transfer by grafting, gain of mtDNA recombination factors, social networks of mitochondria, and the role of mtDNA dysfunction in feeding the world. We will discuss a unifying picture where organismal ecology and gene-specific features together influence whether organism X retains mtDNA gene Y, and where ecology and development together determine which strategies, importantly including recombination, are used to maintain the mtDNA genes that are retained.

Dual and triple gene combinations of KRT5, KRT17, and S100A2 identify basal-like subtype of pancreatic ductal adenocarcinoma and correlate with survival outcome.

There is a significant difference in prognosis and response to chemotherapy between basal and classical subtypes of pancreatic ductal adenocarcinoma (PDAC). Further biomarkers are required to identify subtypes of PDAC. We selected candidate biomarkers via review articles. Correlations between these candidate markers and the PDAC molecular subtype gene sets were analyzed using bioinformatics, confirming the biomarkers for identifying classical and basal subtypes. Subsequently, 298 PDAC patients were included, and their tumor tissues were immunohistochemically stratified using these biomarkers. Survival data underwent analysis, including Cox proportional hazards modeling. Our results indicate that the pairwise and triple combinations of KRT5/KRT17/S100A2 exhibit a higher correlation coefficient with the basal-like subtype gene set, whereas the corresponding combinations of GATA6/HNF4A/TFF1 show a higher correlation with the classical subtype gene set. Whether analyzing unmatched or propensity-matched data, the overall survival time was significantly shorter for the basal subtype compared with the classical subtype (p < .001), with basal subtype patients also facing a higher risk of mortality (HR = 4.017, 95% CI 2.675-6.032, p < .001). In conclusion, the combined expression of KRT5, KRT17, and S100A2, in both pairwise and triple combinations, independently predicts shorter overall survival in PDAC patients and likely identifies the basal subtype. Similarly, the combined expression of GATA6, HNF4A, and TFF1, in the same manner, may indicate the classical subtype. In our study, the combined application of established biomarkers offers valuable insights for the prognostic evaluation of PDAC patients.

ScPriorGraph: constructing biosemantic cell–cell graphs with prior gene set selection for cell type identification from scRNA-seq data

Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.

26 Applying single cell RNA sequencing of sarcomatoid renal cell carcinoma for the development of a novel transcriptomic biomarker to predict immunotherapy response

Abstract Background Renal cell carcinoma with sarcomatoid features (sRCC) is a unique kidney cancer subtype associated with aggressive biology and poor clinical outcomes. Intriguingly, sRCC has recently exhibited preferential responsiveness to immune checkpoint blockade (ICB) therapies. Within the RCC population, however, there currently is a paucity of biomarkers predictive for ICB response, representing a critical unmet clinical need for optimal therapy selection. Therefore, we sought to derive a transcriptomic signature from sRCC samples encapsulating this paradoxical hyper-aggression and ICB-responsiveness to identify RCC patients most likely to benefit from ICB irrespective of sarcomatoid feature presence. Methods Nephrectomy specimens from patients with RCC were processed for single cell RNA sequencing (scRNAseq). Clustering was performed and annotated tumor cells were computationally isolated. Tumor cell counts were aggregated and differential expression between sRCC and non-sRCC cases was performed. Genes significantly upregulated in sRCC were next filtered against differential gene expression data of sRCC vs non-sRCC from three clinical RCC datasets (TCGA, CheckMate, &amp; Javelin101) to identify genes enriched in sRCC across scRNAseq and bulk RNA sequencing. The prognostic and predictive features of this gene signature were then assessed in cohorts of patients with RCC receiving ICB. Results In total, 73,123 unique cells from 18 RCC patients (10 sRCC; 8 clear cell RCC) were analyzed by scRNAseq, including 5,386 tumor cells. Differential gene expression of tumor clones revealed 20 genes significantly enriched in sRCC relative to ccRCC. Filtering against public differential gene expression data resulted in 10 genes included within the final Sarcomatoid Signature (SS). SS expression scores for clinical specimens were calculated by single-sample gene set enrichment analysis. SS scores were significantly enriched in sRCC patient tumors across TCGA KIRC, CheckMate, and IMmotion151 datasets (p&amp;lt;0.001 for all cohorts). Within TCGA KIRC, SS scores were significantly increased in patients with nuclear grade 4 (p&amp;lt;0.001) and metastatic (p&amp;lt;0.001) disease, while stratification by median SS score revealed worsened overall (HR=2.19, p&amp;lt;0.001) and disease-free (HR=2.08, p&amp;lt;0.001) survival among SS-high patients. Amongst the CheckMate cohort (including patient samples from the CheckMate-010 &amp; CheckMate-025 trials), SS-high patients experienced improved progression free survival (PFS) when treated with nivolumab relative to everolimus (HR=0.70, p=0.055), whereas no difference was seen between therapies in the SS-low patient population (HR=0.98, p=0.9). In the IMmotion151 trial, which was not utilized for gene filtering and signature refinement and thus represents an independent validation cohort, SS-high patients experienced reduced PFS duration (HR=1.39, p&amp;lt;0.001) irrespective of treatment arm. However, SS-high patients experienced improved objective response rates with the ICB-containing atezolizumab/bevacizumab regimen compared to sunitinib (p&amp;lt;0.001) and relative to SS-low patients receiving atezolizumab/bevacizumab (p=0.028) (Figure 1). Within the SS-high population, patients experienced prolonged PFS with atezolizumab/bevacizumab (HR=0.70, p=0.003) relative to sunitinib whereas no PFS difference between arms was seen in the SS-low population (HR=1.01, p&amp;gt;0.99). Conclusions This novel SS derived from scRNAseq demonstrates negative prognostic yet positive predictive value for ICB response. In a domain currently void of efficacious biomarkers, the SS, upon prospective validation, may assist in optimal therapy selection for patients with RCC.

Insights into gemcitabine resistance in pancreatic cancer: association with metabolic reprogramming and TP53 pathogenicity in patient derived xenografts

BackgroundWith poor prognosis and high mortality, pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. Standard of care therapies for PDAC have included gemcitabine for the past three decades, although resistance often develops within weeks of chemotherapy initiation through an array of possible mechanisms.MethodsWe reanalyzed publicly available RNA-seq gene expression profiles of 28 PDAC patient-derived xenograft (PDX) models before and after a 21-day gemcitabine treatment using our validated analysis pipeline to identify molecular markers of intrinsic and acquired resistance.ResultsUsing normalized RNA-seq quantification measurements, we first identified oxidative phosphorylation and interferon alpha pathways as the two most enriched cancer hallmark gene sets in the baseline gene expression profile associated with intrinsic gemcitabine resistance and sensitivity, respectively. Furthermore, we discovered strong correlations between drug-induced expression changes in glycolysis and oxidative phosphorylation genes and response to gemcitabine, which suggests that these pathways may be associated with acquired gemcitabine resistance mechanisms. Thus, we developed prediction models using baseline gene expression profiles in those pathways and validated them in another dataset of 12 PDAC models from Novartis. We also developed prediction models based on drug-induced expression changes in genes from the Molecular Signatures Database (MSigDB)’s curated 50 cancer hallmark gene sets. Finally, pathogenic TP53 mutations correlated with treatment resistance.ConclusionOur results demonstrate that concurrent upregulation of both glycolysis and oxidative phosphorylation pathways occurs in vivo in PDAC PDXs following gemcitabine treatment and that pathogenic TP53 status had association with gemcitabine resistance in these models. Our findings may elucidate the molecular basis for gemcitabine resistance and provide insights for effective drug combination in PDAC chemotherapy.

Long-term exposure to diesel exhaust particles induces concordant changes in DNA methylation and transcriptome in human adenocarcinoma alveolar basal epithelial cells

BackgroundDiesel exhaust particles (DEP), which contain hazardous compounds, are emitted during the combustion of diesel. As approximately one-third of the vehicles worldwide use diesel, there are growing concerns about the risks posed by DEP to human health. Long-term exposure to DEP is associated with airway hyperresponsiveness, pulmonary fibrosis, and inflammation; however, the molecular mechanisms behind the effects of DEP on the respiratory tract are poorly understood. Such mechanisms can be addressed by examining transcriptional and DNA methylation changes. Although several studies have focused on the effects of short-term DEP exposure on gene expression, research on the transcriptional effects and genome-wide DNA methylation changes caused by long-term DEP exposure is lacking. Hence, in this study, we investigated transcriptional and DNA methylation changes in human adenocarcinoma alveolar basal epithelial A549 cells caused by prolonged exposure to DEP and determined whether these changes are concordant.ResultsDNA methylation analysis using the Illumina Infinium MethylationEPIC BeadChips showed that the methylation levels of DEP-affected CpG sites in A549 cells changed in a dose-dependent manner; the extent of change increased with increasing dose reaching the statistical significance only in samples exposed to 30 µg/ml DEP. Four-week exposure to 30 µg/ml of DEP significantly induced DNA hypomethylation at 24,464 CpG sites, which were significantly enriched for DNase hypersensitive sites, genomic regions marked by H3K4me1 and H3K27ac, and several transcription factor binding sites. In contrast, 9,436 CpG sites with increased DNA methylation levels were significantly overrepresented in genomic regions marked by H3K27me3 as well as H3K4me1 and H3K27ac. In parallel, gene expression profiling by RNA sequencing demonstrated that long-term exposure to DEP altered the expression levels of 2,410 genes, enriching 16 gene sets including Xenobiotic metabolism, Inflammatory response, and Senescence. In silico analysis revealed that the expression levels of 854 genes correlated with the methylation levels of the DEP-affected cis-CpG sites.ConclusionsTo our knowledge, this is the first report of genome-wide transcriptional and DNA methylation changes and their associations in A549 cells following long-term exposure to DEP.