Single-gene Analysis Research Articles

Abstract 3349: Identification of new potential vulnerabilities for differentiation-based therapeutic strategies against osteosarcoma

Abstract Osteosarcoma (OS), a mesenchymal bone tumor affecting mainly children and adolescents is characterized by a particularly aggressive behavior, with 20% of patients showing lung metastasis already at diagnosis. Current OS treatment strategies rely on multi-drug chemotherapy, and the prognosis for metastatic cases remains grim, underscoring the pressing need for novel therapeutic approaches. OS is considered a differentiation-related disorder, stemming from the inability of mesenchymal stem cells or osteoblastic progenitors to progress towards terminal differentiation. In our study we explored the impact of targeting ZEB1, a transcription factor known to play a pivotal role in maintaining mesenchymal and stemness characteristics in cancer, on OS cells. We employed CRISPR-Cas9 and shRNA interference techniques to inhibit ZEB1 expression in mouse immune competent OS models. In vitro, ZEB1 deficiency significantly diminished the stemness potential of OS cells, as assessed by spheroid formation assays, while promoting osteogenic differentiation. In vivo, ZEB1 deletion in OS cells led to a significant reduction in primary tumor growth. Tumors originating from ZEB1 KO cells exhibited a more differentiated morphology and higher extracellular matrix deposition compared to controls. Intriguingly, the absence of ZEB1 in tumor cells also influenced the immune infiltrate, with ZEB1-deficient tumors showing significantly fewer pro-tumoral CD206+ M2-like macrophages. This observation is in line with the significant down-modulation of CCL2 expression we detected in ZEB1 KO cells. When injected intravenously, ZEB1-deficient cells produced fewer and smaller lung metastases than control cells. Transcriptional changes induced by ZEB1 deletion were investigated by gene expression profile (GEP) and identified 849 down-regulated and 1093 up-regulated genes in ZEB1 knockout (KO) clones versus ZEB1-competent controls. Gene set enrichment analysis showed a down-modulation of pathways related to cellular proliferation and survival, such as MTORC1 signaling, MYC targets, GM2 checkpoint, E2F targets and oxidative phosphorylation in the absence of ZEB1. Single gene analysis identified among the most up-regulated genes in ZEB1-deficient cells Sfrp1, a negative regulator of the WNT/b-CAT pathway, which in turn regulates MYC, whose pathway is indeed repressed in ZEB1 KO cells. We hypothesize that a high expression of SFRP1 could vicariate the effects of ZEB1 inhibition on OS cell stemness, differentiation state, and in vivo aggressiveness, and therefore could act as a tumor suppressor, which could be potentially exploited for therapeutic strategies. Citation Format: Caterina Cascini, Daniele Lecis, Valeria Cancila, Katia Scotlandi, Claudio Tripodo, Mario P. Colombo, Claudia Chiodoni. Identification of new potential vulnerabilities for differentiation-based therapeutic strategies against osteosarcoma [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 3349.

Bioinformatics analysis and validation of genes related to paclitaxel's anti-breast cancer effect through immunogenic cell death

Research indicated that Paclitaxel (PTX) can induce immunogenic cell death (ICD) through immunogenic modulation. However, the combination of PTX and ICD has not been extensively studied in breast cancer (BRCA). The TCGA-BRCA and GSE20685 datasets were enrolled in this study. Samples from the TCGA-BRCA dataset were consistently clustered based on selected immunogenic cell death-related genes (ICD-RGs). Next, candidate genes were obtained by overlapping differentially expressed genes (DEGs) between BRCA and normal groups, intersecting genes common to DEGs between cluster1 and cluster2 and hub module genes, and target genes of PTX from five databases. The univariate Cox algorithm and the least absolute shrinkage and selection operator (LASSO) were performed to obtain biomarkers and build a risk model. Following observing the immune microenvironment in differential risk subgroups, single-gene gene set enrichment analysis (GSEA) was carried out in all biomarkers. Finally, the expression of biomarkers was analyzed. Enrichment analysis showed that 626 intersecting genes were linked with inflammatory response. Further five biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified and a risk model was built. The model's performance was validated using GSE20685 dataset. Furthermore, the biomarkers were enriched with adaptive immune response. Lastly, the experimental results indicated that the alterations in IL18, SH2D2A, and CHI3L1 expression after treatment matched those in the public database. In this study, Five PTX-ICD-related biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified to aid in predicting BRCA treatment outcomes.

Identification of key genes for atherosclerosis in different arterial beds

Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

Utilizing systematic Mendelian randomization to identify potential therapeutic targets for mania.

Mania has caused incalculable economic losses for patients, their families, and even society, but there is currently no effective treatment plan for this disease without side effects. Using bioinformatics and Mendelian randomization methods, potential drug target genes and key substances associated with mania were explored at the mRNA level. We used the chip expression profile from the GEO database to screen differential genes and used the eQTL and mania GWAS data from the IEU database for two-sample Mendelian randomization (MR) to determine core genes by colocalization. Next, we utilized bioinformatics analysis to identify key substances involved in the mechanism of action and determined related gene targets as drug targets. After differential expression analysis and MR, a causal relationship between the expression of 46 genes and mania was found. Colocalization analysis yielded six core genes. Five key substances were identified via enrichment analysis, immune-related analysis, and single-gene GSVA analysis of the core genes. MR revealed phenylalanine to be the only key substance that has a unidirectional causal relationship with mania. In the end, SBNO2, PBX2, RAMP3, and QPCT, which are significantly associated with the phenylalanine metabolism pathway, were identified as drug target genes. SBNO2, PBX2, RAMP3, and QPCT could serve as potential target genes for mania treatment and deserve further basic and clinical research. Medicinal target genes regulate the phenylalanine metabolism pathway to achieve the treatment of mania. Phenylalanine is an important intermediate substance in the treatment of mania that is regulated by drug target genes.

Regulation mechanisms of disulfidptosis-related genes in ankylosing spondylitis and inflammatory bowel disease.

Disulfidptosis is a recently identified form of cell death that contributes to maintaining the internal environment balance of an organism. However, the molecular basis of disulfidptosis in ulcerative colitis (UC), ankylosing spondylitis (AS), and Crohn's disease (CD) has not been thoroughly explored. Firstly, the differentially expressed genes (DEGs) and disulfidptosis-associated genes (DAGs) were obtained through differential analysis between diseases (AS, CD, and UC) and control groups. After the disulfidptosis score was acquired using the single-sample gene set enrichment analysis (ssGSEA) algorithm, the DE-DAGs were screened by overlapping DAGs and DEGs of the three diseases. Next, the feature genes were selected through a combination of machine learning algorithms, receiver operating characteristic (ROC) curves, and expression analysis. Based on these feature genes, nomograms were created for AS, CD and UC. The co-feature genes were then identified by taking the intersections of the genes featured in all three diseases. Meanwhile, single-gene set enrichment analysis (GSEA) and the TF-mRNA-miRNA network were utilized to investigate the molecular mechanisms of the co-feature genes. To validate the expression differences of the co-feature genes between healthy controls and patients (AS and IBD), RT-PCR was performed. Lastly, mendelian randomization (MR) analysis was utilized to explore the causality between genetic variants of S100A12 with AS, UC and CD. In this study, 11 DE-DAGs were obtained. Functional enrichment analysis revealed their involvement in cytokine production and fatty acid biosynthesis. Latterly, AS/CD/UC -feature genes were derived, and they all had decent diagnostic performance. Through evaluation, the performance of the nomogram was decent for three diseases. Then, 2 co-feature genes (S100A12 and LILRA5) were obtained. The GSEA enrichment results indicated that the co-feature genes were mainly enriched in the cytokine-cytokine receptor interaction and drug metabolism cytochrome P450. As shown by functional experiments, there was a correlation between the mRNA expression of S100A12 with AS, UC and CD. Additionally, a causal connection between S100A12 and IBD was detected through MR analysis. In this study, 2 co-feature genes (S100A12 and LILRA5) were screened, and their functions were investigated in AS, CD and UC, providing a basis for further research into diagnosis and treatment.

Integrated single-cell and bulk characterization of branched chain amino acid metabolism-related key gene BCAT1 and association with prognosis and immunogenicity of clear cell renal cell carcinoma.

The relationship between clear cell renal cell carcinoma (ccRCC) and branched-chain amino acids (BCAA) metabolism has yet to be thoroughly explored. The BCAA metabolism-related clusters were constructed using non-negative matrix factorization (NMF). The features of BCAA metabolism in ccRCC were evaluated by building a prognostic model using least absolute shrinkage and selection operator (LASSO) regression algorithm. Real-time quantitative PCR (RT-qPCR) was employed to analyze differential expression of branched-chain amino acid transaminase 1 (BCAT1) between cancer and paracancer tissues and between different cell lines. Cell counting kit-8, wound healing and Transwell chamber assays were conducted to determine changes in proliferative and metastatic abilities of A498 and 786-O cells. Two BCAA metabolism-related clusters with distinct prognostic and immune infiltration characteristics were identified in ccRCC. The BCAA metabolic signature (BMS) was capable of distinguishing immune features, tumor mutation burden, responses to immunotherapy, and drug sensitivity among ccRCC patients. RT-qPCR revealed overexpression of BCAT1 in ccRCC tissues and cell lines. Additionally, single-gene RNA sequencing analysis demonstrated significant enrichment of BCAT1 in macrophages and tumor cells. BCAT1 played tumor-promoting role in ccRCC and was closely associated with immunosuppressive cells and checkpoints. BCAT1 promoted ccRCC cell proliferation and metastasis. The BMS played a crucial role in determining the prognosis, tumor mutation burden, responses to immunotherapy and drug sensitivity of ccRCC patients, as well as the immune cell infiltration features. BCAT1 was linked to immunosuppressive microenvironments and may offer new sights into ccRCC immunotherapeutic targets.

Single-cell combined bioinformatics analysis: construction of immune cluster and risk prognostic model in kidney renal clear cells based on CD8+ T cell-associated genes

BackgroundKidney cancer is an immunogenic solid tumor, characterized by high tumor burden and infiltration of CD8+ T cells. Although immunotherapy targeting the PD1/CTLA-4 axis has demonstrated excellent clinical efficacy, clinical outcomes in most patients are poor.MethodsWe used the RNA sequencing data from the GEO database for KIRC GSE121636 and normal kidney tissue GSE131685, and performed single-cell analysis for cluster identification, pathway enrichment, and CD8+ T cell-associated gene identification. Subsequently, the significance of different CD8+ T-cell associated gene subtypes was elucidated by consensus clustering, pathway analysis, mutated gene analysis, and KIRC immune microenvironment analysis in the TCGA–KIRC disease cohort. Single gene analysis identified LAG3 as the most critical CD8+ T-cell-associated gene and its function was verified by cell phenotype and immunohistochemistry in KIRC.ResultsIn the present study, CD8+ T-cell associated genes in KIRC were screened, including GZMK, CD27, CCL4L2, FXYD2, LAG3, RGS1, CST7, DUSP4, CD8A, and TRBV20-1 and an immunological risk prognostic model was constructed (risk score = − 0.291858656434841*GZMK − 0.192758342489394*FXYD2 + 0.625023643446193*LAG3 + 0.161324477181591*RGS1 − 0.380169045328895*DUSP4 − 0.107221347575037*TRBV20-1). LAG3 was identified and proved as the most critical CD8+ T cell-associated gene in KIRC.ConclusionWe proposed and constructed an immunological risk prognostic model for CD8+ T cell-associated genes and identified LAG3 as a pivotal gene for KIRC progression and CD8+ T-cell infiltration. The model comprehensively explained the immune microenvironment and provided novel immune-related therapeutic targets and biomarkers in KIRC.

Impulsivity and aggression in alcohol withdrawal syndrome is modulated by the interaction of ZNF804A and mTOR polymorphism

Alcohol withdrawal syndrome (AWS) is a poorly studied phenotype of alcohol use disorder. Understanding the relationship between allelic interactions and AWS-related impulsivity and aggression could have significant implications. This study aimed to investigate the main and interacting effects of ZNF804A and mTOR on impulsivity and aggression during alcohol withdrawal. 446 Chinese Han adult males with alcohol dependence were included in the study. Impulsivity and aggression were assessed, and genomic DNA was genotyped. Single gene analysis showed that ZNF804A rs1344706 (A allele/CC homozygote) and mTOR rs1057079 (C allele/TT homozygote) were strongly associated with AWS-related impulsivity and aggression. In the allelic group, MANOVA revealed a significant gene x gene interaction, suggesting that risk varied systematically depending on both ZNF804A and mTOR alleles. Additionally, a significant interactive effect of ZNF804A rs1344706 and mTOR rs7525957 was found on motor impulsivity and physical aggression, and the ZNF804A rs1344706 gene variant had significant effects on motor impulsivity and physical aggression only in mTOR rs7525957 TT homozygous carriers. The study showed that specific allelic combinations of ZNF804A and mTOR may have protective or risk-enhancing effects on AWS-related impulsivity and aggression.

Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork.

Low level of drip loss is an important quality characteristic of meat with high economic value. However, the key genes and regulatory networks contributing to drip loss in pork remain largely unknown. To accurately identify the key genes affecting drip loss in muscles postmortem, 12 Duroc × (Landrace × Yorkshire) pigs with extremely high (n ＝ 6, H group) and low (n ＝ 6, L group) drip loss at both 24h and 48h postmortem were selected for transcriptome sequencing. The analysis of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were performed to find the overlapping genes using the transcriptome data, and functional enrichment and protein-protein interaction (PPI) network analysis were conducted using the overlapping genes. Moreover, we used machine learning to identify the key genes and regulatory networks related to drip loss based on the interactive genes of the PPI network. Finally, nine potential key genes (IRS1, ESR1, HSPA6, INSR, SPOP, MSTN, LGALS4, MYLK2, and FRMD4B) mainly associated with the MAPK signaling pathway, the insulin signaling pathway, and the calcium signaling pathway were identified, and a single-gene set enrichment analysis (GSEA) was performed to further annotate the functions of these potential key genes. The GSEA results showed that these genes are mainly related to ubiquitin mediated proteolysis and oxidative reactions. Taken together, our results indicate that the potential key genes influencing drip loss are mainly related to insulin signaling mediated differences in glycolysis and ubiquitin mediated changes in muscle structure and improves the understanding of gene expression and regulation related to drip loss and contributes to future molecular breeding for improving pork quality.

Application of Newly Customized Myeloid NGS Panel in the Diagnosis of Myeloid Malignancies.

Genetic mutations are major factors in the diagnosis and prognosis of leukemia, and it is difficult to assess these variants using single-gene analysis. Therefore, this study aimed to develop a fast and cost-effective method for genetic screening of myeloid malignancies using a customized next-generation sequencing (NGS) panel. A customized myeloid panel was designed and investigated in 15 acute myeloid leukemia patients. The panel included 11 genes that were most commonly mutated in myeloid malignancies. This panel was designed to sequence the complete genome of CALR, IDH1, IDH2, JAK2, FLT3, NPM1, MPL, TET2, SF3B1, TP53, and MLL. Among the 15 patients, 14 actual pathogenic variants were identified in nine samples, and negative results were found in six samples. Positive findings were observed for JAK2, FLT3, SF3B1, and TET2. Interestingly, non-classical FLT3 mutations (c.1715A>C, c.2513delG, and c.2507dupT) were detected in patients who were negative for FLT3-ITD and TKD by routine molecular results. All identified variants were pathogenic, and the high coverage of the assay allowed us to predict variants at a low frequency (1%) with 1000x coverage. Utilizing a custom panel allowed us to identify variants that were not detected by routine tests or those that were not routinely investigated. Using the costuming panel will enable us to sequence all genes and discover new potential pathogenic variants that are not possible with other commercially available panels that focus only on hotspot regions. This study's strength in utilizing NGS and implanting a customized panel to identify new pathogenic variants that might be common in our population and important in routine diagnosis for providing optimal healthcare for personalized medicine.

Chapter 2 - Approaches to diagnosis for individuals with a suspected inherited white matter disorder

Leukodystrophies are heritable disorders with white matter abnormalities observed on central nervous system magnetic resonance imaging. Pediatric leukodystrophies have long been known for their classically high, "unsolved" rate. Indeed, these disorders provide a diagnostic dilemma for many clinicians as over 100 genetic disorders alone may present with white matter abnormalities, with this figure not taking into account the substantial number of infectious agents, toxicities, and acquired disorders that may affect the white matter of the brain. Achieving a diagnosis may be the single most important step in the clinical course of a leukodystrophy-affected individual, with important implications for care and quality of life. For certain disorders, prompt recognition can direct therapeutic intervention with significant implications and requires urgent recognition. In this review, we cover newborn screening efforts, standard-of-care testing methodologies, and next generation sequencing approaches that continue to change the landscape of leukodystrophy diagnosis. Early studies have shown that next generation sequencing approaches, particularly exome and now genome sequencing have proven to be powerful in helping resolve many cases that were refractory to a single gene or linkage analysis approach. In addition, other methods are required for cases that remain persistently unsolved after next generation sequencing methods have been used. In the past more than half of affected individuals never achieved an etiologic diagnosis, and when they did, the reported times to diagnosis were >5 years although molecular testing has allowed this to be reduced to closer to 16 months. For affected families, next generation sequencing technologies have finally provided a way to fill gaps in diagnosis.

Authentication and validation of key genes in the treatment of atopic dermatitis with Runfuzhiyang powder: combined RNA-seq, bioinformatics analysis, and experimental research.

Atopic dermatitis (AD) is inflammatory disease. So far, therapeutic mechanism of Runfuzhiyang powder on AD remains to be studied. This study aimed to mine key biomarkers to explore potential molecular mechanism for AD incidence and Runfuzhiyang powder treatment. The control group, AD group, treat group (AD mice treated with Runfuzhiyang powder were utilized for studying. Differentially expressed AD-related genes were acquired by intersecting of key module genes related to control group, AD group and treatment group which were screened by WGCNA and AD-related differentially expressed genes (DEGs). KEGG and GO analyses were further carried out. Next, LASSO regression analysis was utilized to screen feature genes. The ROC curves were applied to validate the diagnostic ability of feature genes to obtain AD-related biomarkers. Then protein-protein interaction (PPI) network, immune infiltration analysis and single-gene gene set enrichment analysis (GSEA) were presented. Finally, TF-mRNA-lncRNA and drug-gene networks of biomarkers were constructed. 4 AD-related biomarkers (Ddit4, Sbf2, Senp8 and Zfp777) were identified in AD groups compared with control group and treat group by LASSO regression analysis. The ROC curves revealed that four biomarkers had good distinguishing ability between AD group and control group, as well as AD group and treatment group. Next, GSEA revealed that pathways of E2F targets, KRAS signaling up and inflammatory response were associated with 4 biomarkers. Then, we found that Ddit4, Sbf2 and Zfp777 were significantly positively correlated with M0 Macrophage, and were significantly negatively relevant to Resting NK. Senp8 was the opposite. Finally, a TF-mRNA-lncRNA network including 200 nodes and 592 edges was generated, and 20 drugs targeting SENP8 were predicted. 4 AD-related and Runfuzhiyang powder treatment-related biomarkers (Ddit4, Sbf2, Senp8 and Zfp777) were identified, which could provide a new idea for targeted treatment and diagnosis of AD.

Bioinformatics-Based Identification of Key Prognostic Genes in Neuroblastoma with a Focus on Immune Cell Infiltration and Diagnostic Potential of VGF.

This study aims to identify differentially expressed genes (DEGs) in neuroblastoma (NB) through comprehensive bioinformatics analysis and machine learning techniques. We seek to elucidate these DEGs' biological functions and associated signaling pathways. Furthermore, our objective extends to predicting upstream microRNAs (miRNAs) and relevant transcription factors of pivotal genes, with the ultimate goal of guiding clinical diagnostics and informing future treatment strategies for Neuroblastoma. In this study, we sourced datasets GSE49710 and TARGET from the GEO and UCSC-XENA databases, respectively. Differentially expressed genes (DEGs) were identified using the R language "limma" package. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs were conducted using the "clusterProfiler" package. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to isolate the most significant modules associated with death and MYCN amplification, specifically MEpink and MEbrown modules. These modules were then cross-referenced with the DEGs for further GO and KEGG pathway analyses. LASSO regression analysis, facilitated by the "glmnet" package, was utilized to pinpoint three hub genes. We performed differential analysis on these genes and constructed Receiver Operating Characteristic (ROC) curves for disease diagnosis purposes. Immune infiltration analysis was conducted using the "GSVA" package's ssGSEA function. Additionally, single-gene Gene Set Enrichment Analysis (GSEA) on the hub gene was carried out based on Reactome and KEGG databases. Upstream miRNA and transcription factors associated with the hub gene were predicted using RegNetwork, with visual representations created in Cytoscape. Furthermore, to validate the three identified markers in neuroblastoma tissues, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) analysis was conducted. We identified 483 differentially expressed genes (DEGs) in neuroblastoma. These genes predominantly function in protein translation, membrane composition, and RNA transcription regulation, and are implicated in multiple signaling pathways relevant to neurodegenerative diseases. Utilizing LASSO regression analysis, we pinpointed three hub genes: VGF, DGKD, and C19orf52. The Receiver Operating Characteristic (ROC) curve analysis yielded Area Under Curve (AUC) values of 0.751 and 0.722 for VGF, 0.79 and 0.656 for DGKD, and 0.8 and 0.753 for C19orf52, respectively. Our immune infiltration analysis revealed significant correlations among monocytes, follicular helper T cells, and CD4+ T cells. Notably, in the death group, we observed heightened infiltration levels of activated CD4+ T cells, macrophages, and Th2 cells. C19orf52 exhibited a close association with the infiltration of monocytes, CD4+ T cells, and Th2 cells, with P-values less than 0.05. Furthermore, qRT-PCR analysis corroborated the upregulation of VGF in neuroblastoma tissues, further validating our findings. The hub genes (VGF, DGKD, and C19orf52) of neuroblastoma are screened. VGF, one of the hub genes, may have a high diagnostic value and is involved in the immune cell infiltration in neuroblastoma tissue, which may be used as a biomarker for the diagnosis of neuroblastoma and provides a new direction for clinical prognosis prediction and management improvement.

Cathepsin V is correlated with the prognosis and tumor microenvironment in liver cancer.

Recent studies have shown that high cell cycle activity negatively correlates with antitumor immunity in certain cancer types. However, a similar correlation has not been proven in liver cancer. We downloaded transcriptomic profiles of the cancer genome atlas-liver hepatocellular carcinoma (TCGA-LIHC) and assessed the cell cycle distribution of samples using single sample gene set enrichment analysis (ssGSEA), termed the cell cycle score (CCS). We obtained cell cycle-related differentially expressed prognostic genes and identified CENPA, CDC20, and CTSV using LASSO regression. We studied the effect of CTSV on clinical features and immune alterations in liver cancer based on TCGA-LIHC data. In vitro and in vivo experiments were performed to validate the role of CTSV in liver cancer using liver cancer cell lines and tissues. We found that the CCS closely correlated with the clinical features and prognosis of patients in TCGA-LIHC. Analysis of differentially expressed genes (DEGs), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) regression identified cathepsin V (CTSV) with prognostic significance in LIHC. Importantly, single-gene survival analysis of CTSV using microarray and sequencing data indicated that high levels of CTSV expression correlated with an unfavorable prognosis in various cancers. Gene set enrichment analysis revealed that high CTSV expression closely correlated with decreased expression of metabolic genes and increased expression of cell cycle genes. Furthermore, difference and correlation analyses of the relationship between CTSV expression and immune infiltrates, determined using CIBERSORT and TIMER algorithms, revealed that CTSV expression correlated with macrophages and CD4+ T cells. In vitro and in vivo experiments revealed that knockdown of CTSV inhibited liver cancer cells proliferation. Immunohistochemical staining showed that high CTSV expression correlated with macrophage infiltration in liver cancer tissues, predicted a poor prognosis, and is associated with the effectiveness of hepatocellular carcinoma treatment. In couclusion, CTSV is a novel cell cycle-associated gene with clinical significance in HCC.

SOX10 promotes the malignant biological behavior of basal-like breast cancer cells by regulating EMT process

BackgroundThe diagnostic utility of SRY-box transcription factor 10 (SOX10) expression in basal-like breast cancer (BLBC) has been reported previously. However, the effect of SOX10 on the malignancy of BLBC cells and the underlying molecular mechanisms remain unelucidated. Here, we investigate the regulatory mechanisms and roles of SOX10 in BLBC progression. MethodsSequencing data from patients with BLBC were extracted from the Cancer Genome Atlas database to determine the transcriptomic levels of SOX10 across breast cancer subtypes. Subsequently, the bioinformatics relevance of SOX10 in BLBC was investigated. Immunohistochemical assays were used to corroborate the protein expression of SOX10 in clinicopathological specimens (human breast cancer paraffin tissues). RNA interference was used to downregulate SOX10 expression, and the efficiency of interference was evaluated using quantitative PCR. The expression levels of molecules related to the epithelial-mesenchymal transition (EMT) pathway were determined by western blotting. Various assays, such as transwell, colony formation, and flow apoptosis assays, were conducted to assess the malignancy of BLBC cells (MDA-MB-231). ResultsBioinformatics analyses revealed the differential expression of SOX10 in various breast cancer subtypes. An association between SOX10 and immune checkpoint expression was observed in BLBC. Additionally, immune correlation analysis indicated a positive relationship between SOX10 expression and effector immune cells. SOX10 was identified as a potential immunotherapeutic target. Juxtaposed with non-basal-like breast cancer (N-BLBC) and breast adenosis, immunohistochemical analysis revealed the upregulated expression of SOX10 in BLBC, indicating its potential diagnostic significance. Single-gene functional enrichment analysis indicated that SOX10 is associated with EMT and the tumor inflammatory index. Experimental outcomes from cellular assays suggested that the downregulation of SOX10 inhibited multiple malignancy-associated behaviors in MDA-MB-231 cells, specifically affecting the EMT process, migration, invasion, proliferation, clone formation, and anti-apoptotic activities. ConclusionsWe concluded that SOX10 contributes to the malignancy of BLBC cells by modulating the EMT pathway. Moreover, we observed a notable correlation between SOX10 expression and immune responses, indicating the potential significance of SOX10 in immunotherapy.

Unraveling the Role of PIEZO1 in Stressed Erythropoiesis: Implications for Dyserythropoiesis and Potential Therapeutic Targets in Dehydrated Hereditary Stomatocytosis

PIEZO1 is a mechanosensitive cation channel that plays a crucial role in various physiological processes as a mechanical force sensor. In erythrocytes, it is responsible for regulating cellular volume and hydration. Gain-of-function (GoF) mutations in PIEZO1 lead to dehydrated hereditary stomatocytosis (DHS) by slowing down the channel's inactivation kinetics. DHS patients often exhibit a range of clinical presentations, including mild to severe hemolytic anemia, and iron overload. In addition, studies on erythroid progenitor cells from DHS patients have indicated a mutation-dependent delay in erythroid differentiation. Furthermore, studies on constitutive GoF Piezo1 mice have revealed stressed erythropoiesis characterized by increased erythrocyte turnover. The primary aim of this study is to investigate the role of PIEZO1 during stressed erythropoiesis in DHS and identify potential druggable targets. A total of 80 DHS patients were analysed, and their BMRI (reticulocyte count × patient's Hb/normal Hb), EPO, and ERFE levels were measured. The findings revealed mutation-dependent dyserythropoietic features similar to those observed in CDAII patients (characterized by dyserythropoiesis). The engineered erythroid model of DHS, Hudep2-PIEZO1-KI (Hudep2-KI), was subjected to erythroid differentiation for 12 days and compared to Hudep2-WT. A notable reduction in CD235a expression was detected on the last day of differentiation, indicating that PIEZO1-mediated alteration occurs during the late stages of differentiation. Morphological analysis of differentiating cells indicated that KI cells experienced a significant reduction in proliferation rate (1.5-fold increase vs. day 0) compared to Hudep2-WT (3.1-fold increase vs. day 0) starting from day 7. By day 12 of differentiation, KI cells displayed only half the cell count of WT cells, with a substantial decrease in the percentage of orthochromatic normoblasts (13.7% vs. WT 54.5%). This was accompanied by a relative increase in reticulocytes (Hudep2-WT: 45.5% vs. Hudep2-KI: 86.3%). To identify the altered signalling pathways involved in differentiation deregulation, RNAseq was performed at various time points during differentiation. The gene ontology analysis indicated that the most enriched biological processes were related to apoptosis, cellular response to hypoxia, and glycolysis. During the differentiation process, various apoptosis-related processes were found significantly enriched on different days. Upon conducting a single gene analysis, an imbalance between pro-apoptotic and antiapoptotic signals became evident. Specifically, in Hudep2-KI cells compared to the WT, we observed that 70.8% of proapoptotic genes were upregulated. Conversely, the regulation of apoptosis did not seem to impact antiapoptotic signals, with 57.9% of them being upregulated and 42.1% downregulated. This observation led us to suggest that PIEZO1 GoF induces apoptosis by activating pro-apoptotic signals. The findings of this study demonstrate the role of PIEZO1 GOF mutation in stressed erythropoiesis in both DHS patients and the erythroid cellular system. PIEZO1 GOF variants, through their effect on intracellular calcium concentration, influence late-stage differentiation and disrupt several critical pathways, including apoptosis, response to hypoxia, and glycolysis. Ongoing research involves confirming the data in CD34+ cells from DHS patients and PIEZO1 GOF mice. Additionally, the identified deregulated markers hold promise as potential druggable targets for dyserythropoiesis treatment.

A Patient with Target Cells and Hyperkalemia - or why Hematologists Should Still be Able to ‘Fly by Sight‘

INTRODUCTION: Hematology has changed. Both benign and malignant hematology rely heavily on new diagnostic techniques, such as flow-cytometry, cytogenetics and most recently single gene analysis by next generation sequencing. Examination of a blood smear has come out of favor and is usually performed by lab technicians and not by the hematologist. This case report describes how examination of the blood smear which showed an unexpected red blood cell morphology eventually led after 40 years to the right diagnosis. CASE: R. C. is a 42-year-old male (100 kg = 220 lbs, 1,83 m = 6 ft, BMI 29,9) diagnosed in childhood with spherocytosis. His medical history was remarkable for that after birth he needed blood exchange for kernicterus (bilirubin-induced brain dysfunction), and since age 3 months he had epileptic seizures. His blood counts were not that abnormal and since he had no or only mild anemia throughout his whole life he received no further w/u for his blood disorder. Another remarkable finding was that he always had hyperkalemia but since he had no symptoms (no palpitations, dyspnea, chest pain, nausea, or vomiting) and nobody found an explanation no further workup was pursued. In November 2022 he presented to a nearby hospital for epileptic seizures. His spleen was mildly enlarged (14,5 x 6,7 cm) and because of his h/o spherocytosis the neurologist sent him for hematologic re-evaluation. His physical was unremarkable except for that the margin of the spleen was palpable. He was neither pale nor icteric. He drinks no alcohol and does not smoke. Medications were lamotrigen, cholecalciferol, and cenobamate. His blood counts are shown in the table. Since his MCHC was normal, which does not fit spherocytosis, we ordered a blood smear shown in the figure. There were no spherocytes but many target cells. Then we sent for an eosin-5′-maleimide (EMA) binding test which was also unremarkable (124% binding, with spherocytosis usually < 80%). However, acidified glycerol lysis test showed increased osmotic fragility (lysis time 45 sec., normal > 1800 sec). This unusual combination together with his hyperkalemia led to the hypothesis of a disorder related to the xerocytosis or stomatocytosis complex. However, the morphologic picture did not fit stomatocytosis. We therefore sent for NGS analysis which revealed a heterozygous mutation of SLC2A1, variant c.857G>A; p.Gly286Asp (stomatin-deficient cryohydrocytosis). Stomatin-deficient cryohydrocytosis is a very rare congenital deficiency in GLUT1 and stomatin with moderate hemolytic anemia, pseudohyperkalemia and various neurologic defects. The occurrence of target cells (but no stomatocytes) with cryohydrocytosis has - to our knowledge - been reported only once before. CONCLUSIONS: In Germany benign hematology no longer plays the role it had 30-40 years ago. This may be due to the fact that congenital anemias and erythrocyte abnormalities occur less frequently in the relatively homogeneous German population, at least less often than in southeastern Europe or in other countries. Also, patients with mild stable anemia that do not require treatment, are often not seen by a hematologist. To the authors' opinion the most relevant change is that blood smear examination, which is reimbursed with 50 cents has fallen out of fashion in daily practice. However, in this case only the blood smear revealed, that the prior diagnosis of spherocytosis could not be correct. There were no spherocytes but target cells. The patient, though, had not the typical factors associated with target cells (no liver disease, no hyperlipidemia, no thalassemia or iron deficiency). Eventually NGS analysis revealed stomatin-deficient cryohydrocytosis. We present this case because it was the blood smear examination that eventually led to the right diagnosis. All hematologists should have a microscope “next door” and even if blood smear examination is reimbursed poorly it is a diagnostic tool still at the core of our profession

Diagnostic potential of energy metabolism-related genes in heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction (HFpEF) is associated with changes in cardiac metabolism that affect energy supply in the heart. However, there is limited research on energy metabolism-related genes (EMRGs) in HFpEF. The HFpEF mouse dataset (GSE180065, containing heart tissues from 10 HFpEF and five control samples) was sourced from the Gene Expression Omnibus database. Gene expression profiles in HFpEF and control groups were compared to identify differentially expressed EMRGs (DE-EMRGs), and the diagnostic biomarkers with diagnostic value were screened using machine learning algorithms. Meanwhile, we constructed a biomarker-based nomogram model for its predictive power, and functionality of diagnostic biomarkers were conducted using single-gene gene set enrichment analysis, drug prediction, and regulatory network analysis. Additionally, consensus clustering analysis based on the expression of diagnostic biomarkers was utilized to identify differential HFpEF-related genes (HFpEF-RGs). Immune microenvironment analysis in HFpEF and subtypes were performed for analyzing correlations between immune cells and diagnostic biomarkers as well as HFpEF-RGs. Finally, qRT-PCR analysis on the HFpEF mouse model was used to validate the expression levels of diagnostic biomarkers. We selected 5 biomarkers (Chrna2, Gnb3, Gng7, Ddit4l, and Prss55) that showed excellent diagnostic performance. The nomogram model we constructed demonstrated high predictive power. Single-gene gene set enrichment analysis revealed enrichment in aerobic respiration and energy derivation. Further, various miRNAs and TFs were predicted by Gng7, such as Gng7-mmu-miR-6921-5p, ETS1-Gng7. A lot of potential therapeutic targets were predicted as well. Consensus clustering identified two distinct subtypes of HFpEF. Functional enrichment analysis highlighted the involvement of DEGs-cluster in protein amino acid modification and so on. Additionally, we identified five HFpEF-RGs (Kcnt1, Acot1, Kcnc4, Scn3a, and Gpam). Immune analysis revealed correlations between Macrophage M2, T cell CD4+ Th1 and diagnostic biomarkers, as well as an association between Macrophage and HFpEF-RGs. We further validated the expression trends of the selected biomarkers through experimental validation. Our study identified 5 diagnostic biomarkers and provided insights into the prediction and treatment of HFpEF through drug predictions and network analysis. These findings contribute to a better understanding of HFpEF and may guide future research and therapy development.

Functional Dissection of Cellular Programs to Uncover Novel Gene Dependencies in AML

Genome-scale CRISPR-Cas9 screens have the power to unveil the Achilles' heel of neoplastic cells. Typically, analyses of such large-scale data sets focus on single gene dependencies. An alternative strategy is to evaluate functional networks enriched in a disease or molecular subset of interest. We applied this strategy to the Broad Institute's Cancer Dependency Map (DepMap) data set consisting of genome-scale CRISPR-Cas9 screens in over 1000 cancer cell line models. We hypothesized that interrogating enriched pathways with context-specific dependencies leads to the discovery of functionally informed gene dependencies. Single sample gene set enrichment analysis (ssGSEA) of over 1000 cancer cell lines in the DepMap identified hematologic malignancies to be highly enriched for signatures associated with elevated transcriptional activity. Notably, within hematologic malignancies, acute myeloid leukemia (AML) with a rearrangement in the KMT2A gene ( KMT2Ar-AML) was the most significantly enriched for dependency on transcriptional activity-associated gene signatures. We next interrogated KMT2Ar-AML gene dependencies (excluding common essential genes) with the human kinase database KinMap (http://www.kinhub.org/kinmap/) to identify readily druggable genes involved in transcriptional activity. Cyclin-dependent kinase 13 ( CDK13) was the only gene meeting these criteria. Further DepMap data analysis confirmed that KMT2Ar-AML cell lines were indeed more strongly dependent on CDK13 than all other cancer cell lines screened. Moreover, when we performed GSEA on the gene dependencies observed in CDK13-dependent versus non-dependent cell lines, we found transcriptional activity and KMT2A-target gene sets as top hits. We next validated AML cell dependency on CDK13 with orthogonal genetic approaches (CRISPR-Cas9 knock-out and shRNA) in vitro in human AML cell lines and cells from patient-derived xenograft (PDX) models of KMT2Ar-AML. Perturbation of CDK13 induced cell death with hallmarks of apoptosis. Doxycycline-inducible shRNA directed against CDK13 also impaired AML progression in the peripheral blood and bone marrow in an MV4-11- KMT2Ar xenograft. Moreover, in an orthotopic PDX model, doxycycline inducible knock-out of CDK13 in mice with established disease significantly reduced leukemia burden in the peripheral blood and bone marrow. CDK13 is reported to regulate transcriptional elongation and the clearance of prematurely terminated RNAs. To decipher why AML cells, particularly KMT2Ar, need CDK13 to survive, we identified the CDK13 binding sites and histone marks involved in transcriptional regulation in AML using CUT and RUN and ChIP-seq: We determined that CDK13 co-localizes with H3K4me3 and H3K27ac at promoter sites. Global gene expression studies following the knock-out of CDK13 revealed that the majority of CDK13 bound genes were also differentially expressed, with significantly more genes decreased than increased in expression after CDK13 knock-out. GSEA of CDK13-bound genes with decreased expression upon CDK13 knock-out identified gene signatures associated with transcriptional activation and KMT2A-targets. We next studied the characteristics of CDK13 target genes and observed a predominance of long genes (total length of gene exons > 10 kb, GRCh38.p12). GSEA of genes ranked by size, confirmed that longer genes were enriched for CDK13 target genes, CDK13 differential AML dependencies, and for KMT2A-target transcriptional signatures. Additionally, following CDK13 perturbation in KMT2Ar-AML we observed a decrease of Pol2/pPol2Ser2 occupancy at the transcription end site of long genes. In summary, using functional pathway analyses, we identified CDK13 as a candidate gene dependency enriched in KMT2Ar-AML, a target not immediately prioritized with single gene analysis. Mechanistically, our data support that CDK13 controls Pol2 processivity, with CDK13 perturbation leading to a decreased occupancy at the 3' end of long genes. KMT2Ar, CDK13 dependent cells are also enriched for dependency on long genes, thereby suggesting a molecular basis for the CDK13 dependency in KMT2Ar-AML.

AKAP12 inhibits esophageal squamous carcinoma cell proliferation, migration, and cell cycle via the PI3K/AKT signaling pathway

Esophageal squamous cell carcinoma (ESCC) consistently ranks as one of the most challenging variants of squamous cell carcinomas, primarily due to the lack of effective early detection strategies. We herein aimed to elucidate the underlying mechanisms and biological role associated with A-kinase anchoring protein 12 (AKAP12) in the context of ESCC. Bioinformatic analysis had revealed significantly lower expression level of AKAP12 in ESCC tissue samples than in their non-cancerous counterparts. To gain deeper insights into the potential role of AKAP12 in the progression of ESCC, we conducted a single-gene set enrichment analysis of AKAP12 on ESCC datasets. Our findings suggested that AKAP12 exhibits functions inhibiting cell cycle progression, tumor proliferation, and epithelial-mesenchymal transition. To further validate our findings, we subjected ESCC cell lines to AKAP12 overexpression using CRISPR/Cas9-SAM. In vitro analyses demonstrated that increased expression of AKAP12 significantly reduced cell proliferation, migration, and cell cycle progression. Simultaneously, genes associated with this biological role undergo corresponding regulatory shifts. These observations provided valuable insights into the biological role played by AKAP12 in ESCC progression. In summary, AKAP12 shows promise as a new potential biomarker for early ESCC diagnosis, offering potential advantages for subsequent therapeutic intervention and disease management.