Genotype-Tissue Expression Project Research Articles

Universal Markers for hiPSCs Residue Detection.

Residual undifferentiated induced pluripotent stem cells (iPSCs) detection is essential for both Embryonic Stem Cells (ESCs) and iPSCs application in final cell therapy products. However, specific differentiated cells require specific genes for residual detection; identifying the suitable marker is costly and time-consuming. Thus, a universal marker for iPSCs residue detection for all three germline cells would greatly benefit PSC-derived cellular therapies. Next-generation sequencing (NGS) was performed on total RNAs isolated from the iPSC cell lines and embryonic stem cells (H9), the top 30 expressed genes were selected as candidates. By analysis expression fold change comparing iPSC cells to the differentiated cells, seven genes were highly expressed in iPSCs but showed minimal background expression in differentiated cells. Tissue expression pattern of the candidate genes were explored in the Genotype-Tissue Expression (GTEx) project database, candidate genes were narrowed down to two genes. Spike-in experiments were performed to determine the detection limit and correlation with the number of iPSCs and gene expression by ddPCR. By next-generation sequencing (NGS), we identified two marker genes (ESRG and ZSCAN10) suitable for universal undifferentiated iPSC detection. Both ESRG and ZSCAN10 are highly expressed in iPSCs. ZSCAN10 is slightly expressed in the testis, pituitary, and cerebellum; ESRG is highly expressed in the vagina and scarcely expressed in the other tissues. Furthermore, the ddPCR method with a probe and primers for ESRG and ZSCAN10 detected a trace of undifferentiated hiPSCs to a spiked level of 0.0001%. These results suggest that targeting ESRG/ZSCAN10 transcripts is highly sensitive, quantitative, and could be broadly applied to quality control of almost all iPSC-derived cell therapy products.

Elevated Hexose-6-Phosphate Dehydrogenase Regulated by OSMR-AS1/hsa-miR-516b-5p Axis Correlates with Poor Prognosis and Dendritic Cells Infiltration of Glioblastoma.

Objective Glioblastoma (GBM), a type of malignant glioma, is the most aggressive type of brain tumor and is associated with high mortality. Hexose-6-phosphate dehydrogenase (H6PD) has been detected in multiple tumors and is involved in tumor initiation and progression. However, the specific role and mechanism of H6PD in GBM remain unclear. Methods We performed pan-cancer analysis of expression and prognosis of H6PD in GBM using the Genotype-Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA). Subsequently, noncoding RNAs regulating H6PD expression were obtained by comprehensive analysis, including gene expression, prognosis, correlation, and immune infiltration. Finally, tumor immune infiltrates related to H6PD and survival were performed. Results Higher expression of H6PD was statistically significantly associated with an unfavorable outcome in GBM. Downregulation of hsa-miR-124-3p and hsa-miR-516b-5p in GBM was detected from GSE90603. Subsequently, OSMR-AS1 was observed in the regulation of H6PD via hsa-miR-516b-5p. Moreover, higher H6PD expression significantly correlated with immune infiltration of dendritic cells, immune checkpoint expression, and biomarkers of dendritic cells. Conclusions The OSMR-AS1/ miR-516b-5p axis was identified as the highest-potential upstream ncRNA-related pathway of H6PD in GBM. Furthermore, the present findings demonstrated that H6PD blockading might possess antitumor roles via regulating dendritic cell infiltration and immune checkpoint expression.

Necroptosis-associated long noncoding RNAs can predict prognosis and differentiate between cold and hot tumors in ovarian cancer.

ObjectiveThe mortality rate of ovarian cancer (OC) is the highest among all gynecologic cancers. To predict the prognosis and the efficacy of immunotherapy, we identified new biomarkers.MethodsThe Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx) databases were used to extract ovarian cancer transcriptomes. By performing the co-expression analysis, we identified necroptosis-associated long noncoding RNAs (lncRNAs). We used the least absolute shrinkage and selection operator (LASSO) to build the risk model. The qRT-PCR assay was conducted to confirm the differential expression of lncRNAs in the ovarian cancer cell line SK-OV-3. Gene Set Enrichment Analysis, Kaplan-Meier analysis, and the nomogram were used to determine the lncRNAs model. Additionally, the risk model was estimated to evaluate the efficacy of immunotherapy and chemotherapy. We classified necroptosis-associated IncRNAs into two clusters to distinguish between cold and hot tumors.ResultsThe model was constructed using six necroptosis-associated lncRNAs. The calibration plots from the model showed good consistency with the prognostic predictions. The overall survival of one, three, and five-year areas under the ROC curve (AUC) was 0.691, 0.678, and 0.691, respectively. There were significant differences in the IC50 between the risk groups, which could serve as a guide to systemic treatment. The results of the qRT-PCR assay showed that AL928654.1, AL133371.2, AC007991.4, and LINC00996 were significantly higher in the SK-OV-3 cell line than in the Iose-80 cell line (P < 0.05). The clusters could be applied to differentiate between cold and hot tumors more accurately and assist in accurate mediation. Cluster 2 was more vulnerable to immunotherapies and was identified as the hot tumor.ConclusionNecroptosis-associated lncRNAs are reliable predictors of prognosis and can provide a treatment strategy by screening for hot tumors.

A Cross-Tissue Transcriptome-Wide Association Study Identifies Novel Susceptibility Genes for Juvenile Idiopathic Arthritis in Asia and Europe.

BackgroundJuvenile idiopathic arthritis (JIA) is the most common rheumatic disease in children, and its pathogenesis is still unclear. Genome-wide association studies (GWASs) of JIA have identified hundreds of risk factors, but few of them implicated specific biological mechanisms.MethodsA cross-tissue transcriptome-wide association study (TWAS) was performed with the functional summary-based imputation software (FUSION) tool based on GWAS summary datasets (898 JIA patients and 346,102 controls from BioBank Japan (BBJ)/FinnGen). The gene expression reference weights of skeletal muscle and the whole blood were obtained from the Genotype-Tissue Expression (GTExv8) project. JIA-related genes identified by TWAS findings genes were further compared with the differentially expressed genes (DEGs) identified by the mRNA expression profile of JIA from the Gene Expression Omnibus (GEO) database (accession number: GSE1402). Last, candidate genes were analyzed using functional enrichment and annotation analysis by Metascape to examine JIA-related gene sets.ResultsThe TWAS identified 535 significant genes with P < 0.05 and contains 350 for Asian and 195 for European (including 10 genes both expressed in Asian and European), such as CDC16 (P = 1.72E-03) and PSMD5-AS1 (P = 3.65E-02). Eight overlapping genes were identified based on TWAS results and DEGs of JIA patients, such as SIRPB1 (P TWAS = 4.21E-03, P DEG = 1.50E-04) and FRAT2 (P TWAS = 2.82E-02, P DEG = 1.43E-02). Pathway enrichment analysis of TWAS identified 183 pathways such as cytokine signaling in the immune system and cell adhesion molecules. By integrating the results of DEGs pathway and process enrichment analyses, 19 terms were identified such as positive regulation of T-cell activation.ConclusionBy conducting two populations TWAS, we identified a group of JIA-associated genes and pathways, which may provide novel clues to uncover the pathogenesis of JIA.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection.

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Identification of a Five-mRNA Signature as a Novel Potential Prognostic Biomarker for Glioblastoma by Integrative Analysis.

Despite the availability of advanced multimodal therapy, the prognosis of patients suffering from glioblastoma (GBM) remains poor. We conducted a genome-wide integrative analysis of mRNA expression profiles in 302 GBM tissues and 209 normal brain tissues from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project to examine the prognostic and predictive value of specific mRNAs in GBM. A total of 26 mRNAs were identified to be closely related to GBM patients’ OS (p < 0.05). Utilizing survival analysis and the Cox regression model, we discovered a set of five mRNAs (PTPRN, ABCC3, MDK, NMB, and RALYL) from these 26 mRNAs that displayed the capacity to stratify patients into high- and low-risk groups with statistically different overall survival in the training set. The model of the five-mRNA biomarker signature was successfully verified on a testing set and independent sets. Moreover, multivariate Cox regression analysis revealed that the five-mRNA biomarker signature was a prognostic factor for the survival of patients with GBM independent of clinical characteristics and molecular features (p < 0.05). Gene set enrichment analysis indicated that the five-mRNA biomarker signature might be implicated in the incidence and development of GBM through its roles in known cancer-related pathways, signaling molecules, and the immune system. Moreover, consistent with the bioinformatics analysis, NMB, ABCC3, and MDK mRNA expression was considerably higher in four human GBM cells, and the expression of PTPRN and RALYL was decreased in GBM cells (p < 0.05). Our study developed a novel candidate model that provides new prospective prognostic biomarkers for GBM.

LncRNA ZFAS1 Promotes HMGCR mRNA Stabilization via Binding U2AF2 to Modulate Pancreatic Carcinoma Lipometabolism

Being one of the most lethal malignant tumors worldwide, pancreatic carcinoma (PC) shows strong invasiveness and high mortality. In tumorigenesis and progression, the role played by long-chain noncoding RNAs (lncRNAs) cannot be ignored. This article mainly probes into the function of lncRNA ZFAS1 in PC. ZFAS1 expression in PC and normal counterparts retrieved from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) database was analysed by GEPIA2. Its expression profile in clinical specimens and human PC cell strains was quantified using qRT-PCR. Measurements of BxPC-3 cell multiplication and invasiveness employed CCK-8, plate clone formation test, and Transwell chamber assay. ZFAS1's impact on lipid content in BxPC-3 cells was detected. RNA pulldown and RIP assays analyzed the interaction of ZFAS1 with U2AF2 and HMGCR in BxPC-3 cells. Finally, the impacts of U2AF2 and HMGCR on the biological behavior of BxPC-3 were observed. ZFAS1 was kept at a high level in PC tissues versus the normal counterparts. ZFAS1 gene knockout remarkably suppressed PC cell multiplication and invasiveness and decreased the contents of free fatty acids, total cholesterol, triglycerides, and phospholipids. Mechanistically, ZFAS1 stabilized HMGCR mRNA through U2AF2, thus increasing HMGCR expression and promoting PC lipid accumulation. Meanwhile, reduced PC cell viability and invasiveness were observed after downregulating U2AF2 and HMGCR. As an oncogene of PC, ZFAS1 can modulate lipometabolism and stabilize HMGCR mRNA expression by binding with U2AF2 in PC, which is a candidate target for PC diagnosis and treatment.

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery.

A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG’s applications to Alzheimer’s disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.

POC1A, prognostic biomarker of immunosuppressive microenvironment in cancer.

POC1 centriolar protein A (POC1A) effect in pan-cancer remains uncertain. The POC1A expression in normal and tumor tissues underwent analysis in this study utilizing data from the Genotype-Tissue Expression (GTEx) project and the Cancer Genome Atlas (TCGA) database. POC1A prognostic value and clinicopathological features were assessed utilizing the TCGA cohort. The relationship between immunological cell infiltration and POC1A of TCGA samples downloaded from TIMER2 and ImmuCellAI databases were observed. The relation between POC1A and immunological checkpoints genes, microsatellite instability (MSI) as well as tumor mutation burden (TMB) was also evaluated. Additionally, gene set enrichment analysis (GSEA) was utilized for exploring POC1A potential molecular mechanism in pan-cancer. In almost all 33 tumors, POCA1 showed a high expression. In most cases, high POC1A expression was linked significantly with a poor prognosis. Additionally, Tumor immune infiltration and tumors microenvironment were correlated with the expression of POC1A. In addition, TMB and MSI, as well as immune checkpoint genes in pan-cancer, were related to POC1A expression. In GSEA analysis, POC1A is implicated in cell cycle and immune-related pathways. These results might elucidate the crucial roles of POC1A in pan-cancer as a prognostic biomarker and immunotherapy target.

Proteogenomic, Epigenetic, and Clinical Implications of Recurrent Aberrant Splice Variants in Clear Cell Renal Cell Carcinoma

BackgroundAlternative mRNA splicing can be dysregulated in cancer, resulting in the generation of aberrant splice variants (SVs). Given the paucity of actionable genomic mutations in clear cell renal cell carcinoma (ccRCC), aberrant SVs may be an avenue to novel mechanisms of pathogenesis. ObjectiveTo identify and characterize aberrant SVs enriched in ccRCC. Design, setting, and participantsUsing RNA-seq data from the Cancer Cell Line Encyclopedia, we identified neojunctions uniquely expressed in ccRCC. Candidate SVs were then checked for expression across normal tissue in the Genotype-Tissue Expression Project and primary tumor tissue from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and our institutional Total Cancer Care database. Outcome measurements and statistical analysisClinicopathologic, genomic, and survival data were available for all cohorts. Epigenetic data were available for the TCGA and CPTAC cohorts. Proteomic data were available for the CPTAC cohort. The association of aberrant SV expression with these variables was examined using the Kruskal-Wallis test, pairwise t test, Spearman correlation test, and Cox regression analysis. Results and limitationsOur pipeline identified 16 ccRCC-enriched SVs. EGFR, HPCAL1-SV and RNASET2-SV expression was negatively correlated with gene-specific CpG methylation. We derived a survival risk score based primarily on the expression of five SVs (RNASET2, FGD1, PDZD2, COBLL1, and PTPN14), which was consistent and applicable across multiple cohorts on multivariate analysis. The splicing factor RBM4, which modulates splicing of HIF-1α, exhibited significantly lower expression at the protein level in the high-risk group, as defined by our SV-based score. ConclusionsWe describe 16 aberrant SVs enriched in ccRCC, many of which are associated with disease biology and/or clinical outcomes. This study provides a novel strategy for identifying and characterizing disease-specific aberrant SVs. Patient summaryWe describe a method to identify disease targets and biomarkers using transcriptomic analysis beyond somatic mutations or gene expression. Kidney tumors express unique splice variants that may provide additional prognostic information following surgery.

Abstract 683: Identification of common germline variants associated with pediatric rhabdomyosarcoma survival: A report from the Children's Oncology Group (COG)

Abstract Background: Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children and has one of the poorest survival rates among pediatric cancers, underscoring the need to identify factors which may be leveraged to improve therapeutic options for these individuals. Methods: We carried out a genome-wide association study of overall survival (OS) and event-free survival (EFS) in 920 RMS patients from COG protocols and randomly divided them into discovery (n=642) and replication (n=278) cohorts. Genotyping was conducted using the Illumina OmniExpress or Global Screening Array and imputed using the Haplotype Reference Consortium. We used Cox proportional hazards regression to calculate an adjusted hazard ratio (aHR) and P value for each common variant (minor allele frequency [MAF]&amp;gt;5%) for OS and EFS while adjusting for age at diagnosis, tumor stage, histological subtype, and the top five principal components. Analyses were also conducted by histological subtype: embryonal RMS (ERMS, n=544) and alveolar RMS (ARMS, n=268). Finally, we performed a meta-analysis of the results from the discovery and replication cohorts to generate a summary aHR and P value for each single nucleotide polymorphism (SNP). Results: We identified an intergenic SNP at chr8q21.13 associated with worse RMS EFS across subtypes (aHR=2.08, P=2.80x10-9), which had consistent effects across the discovery (aHR=1.91, P=5.05x10-6) and replication (aHR=2.62, P=7.16x10-5) cohorts. This SNP lies in a region which spans the genomic binding site for GATA2 and GATA3, transcription factors that are recognized to contribute to cancer development. We also identified a significant association between a SNP at chr12q21.1 and worse EFS (aHR=2.04, P=3.35x10-8) with consistent effects across the discovery and replication cohorts. Based on data from the Genotype-Tissue Expression project (GTEx), this SNP is associated with expression of SLCO1B1, a gene which encodes a liver anion transporter linked to RMS treatment-related toxicities. In subtype-specific analyses, we identified a SNP at chr17q21.32 that was significantly associated with worse ARMS OS (129 events; aHR=3.18, P=3.12x10-8; discovery: aHR=3.19, P=6.23x10-4; replication: aHR=3.16, P=1.43x10-3). In GTEx, this SNP is associated with expression and splicing of genes including PITPNM3, KIAA0753, and MED31 across various tissues. No SNPs were significantly associated with ERMS OS or EFS. Conclusion: In the first GWAS of RMS survival outcomes, we identified two SNPs that were significantly associated with worse EFS across RMS subtypes. Further, we identified a SNP that was associated with OS in ARMS patients, a subtype that is associated with worse outcomes. Additional investigation of the impact of these SNPs may further support their consideration for novel risk stratification protocols. Citation Format: Bailey A. Martin-Giacalone, Michael E. Scheurer, Javed Khan, Stephen J. Chanock, Shengchao Alfred Li, Meredith Yeager, Deborah A. Marquez-Do, Donald A. Barkauskas, David Hall, Matthew T. McEvoy, Melissa A. Richard, Pagna Sok, Austin L. Brown, Aniko Sabo, Stephen X. Skapek, Douglas S. Hawkins, Rajkumar Venkatramani, Lisa Mirabello, Philip J. Lupo. Identification of common germline variants associated with pediatric rhabdomyosarcoma survival: A report from the Children's Oncology Group (COG) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 683.

Abstract 2002: A genome-wide association study identifies novel sepsis risk loci in children with Down syndrome-associated acute lymphoblastic leukemia: A report from the Children’s Oncology Group

Abstract Background: Children with Down syndrome (DS) have an increased risk of acute lymphoblastic leukemia (ALL) and are more likely to experience morbidity and mortality from infectious toxicities during treatment compared to those without DS. We sought to characterize genetic risk factors for sepsis among individuals with DS-ALL. Methods: We performed a germline genome-wide association (GWAS) study for sepsis among 264 subjects with DS-ALL treated on Children’s Oncology Group (COG) protocols AALL0932 (N=181) and AALL1131 (N=83), for newly-diagnosed standard risk and high risk B-ALL, respectively. Sepsis was defined using Common Terminology Criteria for Adverse Events (v4.0), with a microbiologically confirmed grade 4 or 5 sepsis event reported during any phase of treatment to define the case and comparison groups. Germline genotyping on the Affymetrix SNP 6.0 or Illumina Omni 2.5Exome arrays was imputed to the Haplotype Reference Consortium panel and filtered for quality control. Logistic regression models were used to compare individuals with DS-ALL who experienced sepsis to those who never developed sepsis during treatment. Models were adjusted for principal components of population structure and COG protocol. Analyses included autosomal variants with a minor allele frequency &amp;gt;1% and excluded chromosome 21. Results: We identified 29 individuals (10.9%) with DS-ALL who developed one or more sepsis events during treatment. In genome-wide analyses, we observed 52 variants in eight genomic loci associated with sepsis at P&amp;lt;10-5 among individuals with DS-ALL. Our top signal was rs77917748 (P=6.02x10-7), an intronic variant in a regulatory region of TMEM163, a protein that is essential for zinc homeostasis. Additionally, we identified a strong linkage signal at rs56288508 on chr6p21.2 (P=5.29x10-6). According to data from the Genotype-Tissue Expression (GTEx) project, this locus is associated with whole blood expression of ZFAND3, a zinc finger protein and transcriptional regulator. Additionally, this variant is associated with intestinal infections (P=0.012) in the UK Biobank. Finally, a third variant we identified in our GWAS is on chr6q24.2 (P=4.33x10-6) and shows strong linkage in GTEx with peripheral blood mononuclear cell expression of APOBEC3A, a viral deaminase that lacks zinc binding activity. Notably, this variant is associated with respiratory failure (P=0.0099) in the UK Biobank. Conclusion: We identified evidence of genetic associations for sepsis risk in DS-ALL. These findings suggest zinc homeostasis may be an important factor mediating risk of sepsis during treatment of individuals with DS-ALL. Confirmatory studies and validation in additional cohorts are ongoing. Citation Format: Melissa A. Richard, Meenakshi Devidas, Wenjian Yang, John P. Woodhouse, Vilmarie Rodriguez, Johann K. Hitzler, Reuven J. Schore, Anne L. Angiolillo, Michael J. Burke, Wanda L. Salzer, Elizabeth A. Raetz, Mignon L. Loh, Stephen P. Hunger, Jun J. Yang, Philip J. Lupo, Karen R. Rabin. A genome-wide association study identifies novel sepsis risk loci in children with Down syndrome-associated acute lymphoblastic leukemia: A report from the Children’s Oncology Group [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2002.

Abstract 2283: SLC25A46 shows potential as a novel genetic regulator in breast cancer development

Abstract SLC25A46 is a part of the mitochondrial solute carrier family (SLC25) and acts as a transporter transferring lipids between the mitochondria and endoplasmic reticulum space. SLC25A46 is expressed in the mitochondrial outer membrane, where it plays a significant role in regulating mitochondrial dynamics, cristae maintenance, and mitochondrial respiratory function. SLC25A46 prevents the formation of hyperfilamentous mitochondria and interacts with novel mitochondrial fusion proteins such as MFN-2 and OPA1 and the cristae organizing complex, MICOS. Notably, research has shown that a first-in-class, small molecule-specific OPA1 inhibitor can be used pharmacologically to curtail tumor growth; however, the OPA1 inhibitor did not explore how the interacting partner, SLC25A46, was affected by such inhibition. Indeed, novel targets are needed for advanced breast cancers as a means of therapeutic interventions. Considering the critical role of SLC25A46, we sought to investigate novel mitochondrial proteins that are understudied in breast cancer. We applied a statistical framework to calculate predicted gene expression for SLC25A46 in 70,349 patients of European descent in BioVU, the Vanderbilt University Medical Center (VUMC) biorepository linked to de-identified electronic health records (EHRs). A genetically predicted gene expression model for SLC25A46 was trained on directly measured RNA-seq data across 17 tissues from the Genotype-Tissue Expression (GTEx) Project. We then fitted a multivariable logistic regression model to test SLC25A46 predicted expression against 1,704 phenotypes for each tissue. We observed that patients with high predicted expression of SLC25A46 were more likely to receive diagnosis of ‘abnormal findings in mammograms’ (P=2.76E-06, OR=2.26) in adipose subcutaneous tissues. Likewise, we found yet another significant association with ‘lump or mass in breast’ (P=8.9E-06, OR=2.61) in adipose subcutaneous tissues. These results pass their individual Bonferroni correction that was applied separately for each tissue, but fell short of passing a strict multiple testing threshold that accounted for all tissues. Given the association between SLC25A46 and abnormal phenotypes that are commonly coded for patients in relation to breast cancer, we conducted a linear regression model with SLC25A46 and ionized calcium and found a modest association (P=0.03, OR=1.02). Elevated serum levels of calcium are frequently observed in advanced breast cancer. Additionally, proteins that interact with SLC25A46 such as OPA1, are overexpressed in breast cancer. We further constructed a polygene score to determine the effects of these genes in all breast cancer phenotypes. Together, these data suggest that SLC25A46 has implications in breast cancer as a novel target in cancer development. Citation Format: Heather K. Beasley, Ky'Era V. Actkins, Tyne Miller-Flemming, Bret C. Mobley, Lea Davis, Antentor O. Hinton, Nancy J. Cox. SLC25A46 shows potential as a novel genetic regulator in breast cancer development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2283.

Promoter Specific Methylation of SSTR4 is Associated With Alcohol Dependence in Han Chinese Males.

Alcohol dependence (AD), a disease can be affected by environmental factors with epigenetic modification like DNA methylation changes, is one of the most serious and complex public health problems in China and worldwide. Previous findings from our laboratory using the Illumina Infinium Human Methylation450 BeadChip suggested that methylation at the promoter of SSTR4 was one of the major form of DNA modification in alcohol-dependent populations. To investigate whether DNA methylation levels of the SSTR4 promoter influence alcohol-dependent behaviors, genomic DNA was extracted from the peripheral blood sample of 63 subjects with AD and 65 healthy controls, and pyrosequencing was used to verify the results of BeadChip array. Linear regression was used to analyze the correlation between the methylation levels of SSTR4 promoter and the scores of alcohol dependence scales. Gene expression of SSTR4 in brain tissue was obtained from the Genotype-Tissue Expression (GTEx) project and Human Brain Transcriptome database (HBT). We found the methylation levels of SSTR4 in AD group were significantly lower than healthy controls (two-tailed t-test, t = 14.723, p < 0.001). In addition, only weak to moderate correlations between the methylation levels of the SSTR4 promoter region and scale scores of Alcohol Use Disorders Identification Test (AUDIT), Life Events Scale (LES) and Wheatley Stress Profile (WSS) based on linear regression analyses (AUDIT: R 2 = 0.35, p < 0.001; LES: R 2 = 0.27, p < 0.001; WSS: R 2 = 0.49, p < 0.001). The hypomethylated status of SSTR4 may involve in the development of AD and increase the risk of AD persistence in Han Chinese males.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning.

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning. Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level. Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis. Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.

Impact of sweet, umami, and bitter taste receptor (TAS1R and TAS2R) genomic and expression alterations in solid tumors on survival

Originally identified on the tongue for their chemosensory role, the receptors for sweet, umami, and bitter taste are expressed in some cancers where they regulate important cellular processes including apoptosis and proliferation. We examined DNA mutations (n = 5103), structural variation (n = 7545), and expression (n = 6224) of genes encoding sweet or umami receptors (TAS1Rs) and bitter receptors (TAS2Rs) in 45 solid tumors subtypes compared to corresponding normal tissue using The Cancer Genome Atlas and the Genotype Tissue Expression Project databases. Expression of TAS1R and TAS2R genes differed between normal and cancer tissue, and nonsilent mutations occurred in many solid tumor taste receptor genes (~ 1–7%). Expression levels of certain TAS1Rs/TAS2Rs were associated with survival differences in 12 solid tumor subtypes. Increased TAS1R1 expression was associated with improved survival in lung adenocarcinoma (mean survival difference + 1185 days, p = 0.0191). Increased TAS2R14 expression was associated with worse survival in adrenocortical carcinoma (−1757 days, p < 0.001) and esophageal adenocarcinoma (−640 days, p = 0.0041), but improved survival in non-papillary bladder cancer (+ 343 days, p = 0.0436). Certain taste receptor genes may be associated with important oncologic pathways and could serve as biomarkers for disease outcomes.

Transcriptomic Characteristics Associated With Aging in the Thyroid Gland.

The aging thyroid is associated with a plethora of morphological and functional changes. Limited studies have addressed the gene expression signature in the aging thyroid, except for sporadic reports using data from postmortem samples in the Genotype-Tissue Expression (GTEx) project. In this investigation, we analyzed the RNA sequencing data of 58 samples of normal-appearing counterpart thyroid tissues from The Cancer Genome Atlas. Aging-correlated genes were identified by determining the Spearman rank-order correlation between patient age and gene expression level. Additionally, we performed gene set enrichment analysis and conducted a weighted correlation network analysis. The results were compared with those analyzed using the GTEx data. The over-represented protein class of aging-correlated genes is mainly metabolite interconversion enzymes. Our analyses identified alterations in immune and inflammatory responses, mitochondrial functions, cytoskeletal proteins, as well as amino acid and cytochrome P450 metabolism. There was no significant association between thyroid differentiation and age. Our findings may shed molecular light on thyroid disorders in the geriatric population.

Recurrence- and Malignant Progression-Associated Biomarkers in Low-Grade Gliomas and Their Roles in Immunotherapy.

Despite a generally better prognosis than high-grade glioma (HGG), recurrence and malignant progression are the main causes for the poor prognosis and difficulties in the treatment of low-grade glioma (LGG). It is of great importance to learn about the risk factors and underlying mechanisms of LGG recurrence and progression. In this study, the transcriptome characteristics of four groups, namely, normal brain tissue and recurrent LGG (rLGG), normal brain tissue and secondary glioblastoma (sGBM), primary LGG (pLGG) and rLGG, and pLGG and sGBM, were compared using Chinese Glioma Genome Atlas (CGGA) and Genotype-Tissue Expression Project (GTEx) databases. In this study, 296 downregulated and 396 upregulated differentially expressed genes (DEGs) with high consensus were screened out. Univariate Cox regression analysis of data from The Cancer Genome Atlas (TCGA) yielded 86 prognostically relevant DEGs; a prognostic prediction model based on five key genes (HOXA1, KIF18A, FAM133A, HGF, and MN1) was established using the least absolute shrinkage and selection operator (LASSO) regression dimensionality reduction and multivariate Cox regression analysis. LGG was divided into high- and low-risk groups using this prediction model. Gene Set Enrichment Analysis (GSEA) revealed that signaling pathway differences in the high- and low-risk groups were mainly seen in tumor immune regulation and DNA damage-related cell cycle checkpoints. Furthermore, the infiltration of immune cells in the high- and low-risk groups was analyzed, which indicated a stronger infiltration of immune cells in the high-risk group than that in the low-risk group, suggesting that an immune microenvironment more conducive to tumor growth emerged due to the interaction between tumor and immune cells. The tumor mutational burden and tumor methylation burden in the high- and low-risk groups were also analyzed, which indicated higher gene mutation burden and lower DNA methylation level in the high-risk group, suggesting that with the accumulation of genomic mutations and epigenetic changes, tumor cells continued to evolve and led to the progression of LGG to HGG. Finally, the value of potential therapeutic targets for the five key genes was analyzed, and findings demonstrated that KIF18A was the gene most likely to be a potential therapeutic target. In conclusion, the prediction model based on these five key genes can better identify the high- and low-risk groups of LGG and lay a solid foundation for evaluating the risk of LGG recurrence and malignant progression.

INSC Is Down-Regulated in Colon Cancer and Correlated to Immune Infiltration

Background: Previous studies have verified that Inscuteable Spindle Orientation Adaptor Protein (INSC) can regulate cell proliferation and differentiation in the developing nervous system. It also plays an important role in spindle orientation during mitosis and asymmetric division of fibroblasts and participates in the process of stratification of the squamous epithelium. The role and potential mechanism of INSC in the development of colonic adenocarcinoma (COAD) have not been fully understood. This study aimed at exploring the prognostic value of INSC in COAD and the correlation of its expression with immune infiltration. Methods: The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) project, Gene Expression Profiling Interactive Analysis (GEPIA), and Gene Expression Omnibus (GEO) database were used to analyze the expression of INSC in COAD. The INSC protein expression level was analyzed by immunohistochemistry staining and the Human Protein Atlas (HPA) database. The diagnostic and prognostic values of INSC in COAD patients were analyzed using receiver operating characteristic (ROC) and Kaplan–Meier (KM) survival curves. In order to understand whether INSC is an independent prognostic factor, we used univariable and multivariate Cox analyses to analyze INSC expression and several clinical characteristics with survival. We use STRING analysis to find INSC-related proteins and related biological events analyzed by Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. At last, GEPIA and the Tumor Immune Estimation Resource (TIMER) were employed to explore the relationship between INSC and immune infiltrates and its marker gene set. Results: INSC was lower expressed in COAD tissues than in normal colon tissues, which was correlated with tumor stage. Patients with lower expression of INSC had shorter overall survival (OS). Moreover, univariable Cox analysis demonstrated that high expression of INSC was an independent prognostic factor for COAD. ROC analysis showed INSC was an accurate marker for identifying tumors from normal colon tissue, and the AUC of the curve was 0.923. Significant GO term analysis by GSEA showed that genes correlated with INSC were found to be enriched in several immune-related pathways. Specifically, INSC expression showed significant negative correlations with infiltration levels of B cells, CD4+ T cells, macrophages, DCs, and their marker sets in COAD. Conclusion: INSC was provided with prognostic value in COAD and related to immune invasion.

Reduced expression of lncRNA DLEU7-AS1 is a novel favorable prognostic factor in acute myeloid leukemia.

The objective of our study was to measure DLEU7-AS1 expression in de novo acute myeloid leukemia (AML) whilst also analyzing its clinical relevance. We used gene expression data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Cancer Cell Line Encyclopedia (CCLE) and Genotype-Tissue Expression project (GTEx) to assess the expression profile of DLEU7-AS1 in pan-cancers, cancer cell lines and normal tissues. Reverse transcription-quantitative PCR was used to measure DLEU7-AS1 expression in bone marrow from 30 normal individuals and 110 patients with de novo AML. DLEU7-AS1 expression was found to be markedly reduced in the AML samples of the TCGA pan-cancer datasets. In our PCR validation, DLEU7-AS1 expression was significantly decreased in the AML samples compared with that in controls (P<0.001). Low DLEU7-AS1 expression (DLEU7-AS1low) correlated positively with lower blood platelet counts (P=0.029). In addition, low DLEU7-AS1 expression was more frequently observed in the intermediate (58%; 44/76) and favorable karyotypes (65%; 15/23) compared with that in the poor karyotype (10%; 1/10; P=0.005). In particular, patients with high expression levels of DLEU7-AS1 (DLEU7-AS1high) showed lower complete remission rates (P=0.002) than patients with DLEU7-AS1low. Survival analysis revealed that patients with DLEU7-AS1low had longer overall survival (OS) than patients with DLEU7-AS1high (P<0.05). Multivariate Cox analysis demonstrated that in patients with non-acute promyelocytic leukemia (non-M3) who were ≤60 years old, DLEU7-AS1 expression was an independent prognostic factor for OS. Furthermore, we found distinct correlations among the expression of DLEU7-AS1, infiltration by immune cells and immune checkpoint genes in AML.