Genotype-Tissue Expression Datasets Research Articles

P-707 Discovery of novel serum biomarkers for premature ovarian insufficiency through machine-learning bioinformatic analysis

Abstract Study question Can we develop predictive, practical biomarkers for premature ovarian insufficiency (POI) from normal ovaries and human blood, tested and validated by machine-learning (ML) algorithm? Summary answer Applying random forest and XGBoost algorithms, we pinpointed 60 significant genes from ovarian tissues; transcriptome analysis identified RPM1 as the most significant biomarker. What is known already Accelerated ovarian aging has been suggested as one of the possible underlying mechanisms of POI; previous literature has focused on identifying its cause in genetic realm, commonly adopting first-generation sequencing (Sanger) and next-generation sequencing (NGS). However, such powerful tools are still limited in accurately depicting the complete transcript information of ovarian tissue. Moreover, theoretical value of such disease-predictive gene candidates needs to be validated using human tissues and blood, and finally, underlying mechanisms should be confirmed with ideal animal model and cell line. Study design, size, duration To overcome the existing limitations and maximize clinical relevance of disease-predictive gene analysis and theoretical biomarker exploration, the current study incorporated the transcriptome expression data from both young and old ovaries obtained from the Genotype-Tissue Expression (GTEx) database. Using supervised machine learning techniques, potential marker genes were identified, and a meaningful predictive index was subsequently developed using human plasma samples of control and POI women with and without pregnancy history. Participants/materials, setting, methods The performance evaluation of two ML models were achieved using the area under receiver operating characteristic curve (AUROC), accuracy, kappa, and F1 score, among various age groups. The expression profiles of final candidate marker genes were analyzed within the GTEx datasets, in relation to their correlation pattern with age. Finally, the potential transcriptome biomarkers were quantitatively evaluated using human blood samples from 92 controls and 108 POI women visiting a tertiary center in 2023. Main results and the role of chance We found 41 significant genes using the random forest algorithm and 19 significant genes using the XGboost algorithm. We then tested the ML-characterized 7 final candidate biomarkers in human blood samples (all p < 0.05). The AUROC and interventional predictive index results demonstrated the potential of RPM1 as a predictive marker with AUROC of 0.784, comparable to the AUROC of anti-mullerian hormone (AMH) as 0.659 and inhibin B (INHB) as 0.646. Limitations, reasons for caution To avoid potential biases, RPMs should be tested in a larger, multinational cohort with different genetic and geographic backgrounds, possibly incorporating Mendelian randomization method to estimate the causal effect of RPMs on POI. Finally, to ultimately confirm their therapeutic potential, preclinical studies should be considered using RPM recombinant proteins. Wider implications of the findings Our results proposed that predictive biomarkers for POI could be associated with premature, accelerated ovarian aging of the disease mechanism, and these biomarkers can be developed as evaluation metrics for the efficacy, progress, and prognosis of interventions and treatment modalities assigned to POI women seeking fertility. Trial registration number 202302240001

Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues

Tissue gene expression studies are impacted by biological and technical sources of variation, which can be broadly classified into wanted and unwanted variation. The latter, if not addressed, results in misleading biological conclusions. Methods have been proposed to reduce unwanted variation, such as normalization and batch correction. A more accurate understanding of all causes of variation could significantly improve the ability of these methods to remove unwanted variation while retaining variation corresponding to the biological question of interest. We used 17,282 samples from 49 human tissues in the Genotype-Tissue Expression data set (v8) to investigate patterns and causes of expression variation. Transcript expression was transformed to z-scores, and only the most variable 2% of transcripts were evaluated and clustered based on coexpression patterns. Clustered gene sets were assigned to different biological or technical causes based on histologic appearances and metadata elements. We identified 522 variable transcript clusters (median: 11 per tissue) among the samples. Of these, 63% were confidently explained, 16% were likely explained, 7% were low confidence explanations, and 14% had no clear cause. Histologic analysis annotated 46 clusters. Other common causes of variability included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), disease status, and age. Technical causes included blood draw timing and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens data set of single-cell expression. This is among the largest explorations of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression and demonstrated the utility of matched histologic specimens. It further demonstrated the value of acquiring meaningful tissue harvesting metadata elements to use for improved normalization, batch correction, and analysis of both bulk and single-cell RNA-seq data.

4-Hydroxyphenylpyruvate Dioxygenase-Like predicts the prognosis and the immunotherapy response of cancers: a pan-cancer analysis.

The 4-Hydroxyphenylpyruvate Dioxygenase-Like (HPDL) protein plays a crucial role in safeguarding cells from oxidative stress by orchestrating metabolic reprogramming. New research suggests that HPDL is considerably increased in pancreatic ductal adenocarcinoma, although its impact on cancer immunotherapy is still unclear. Pancancer transcriptional data were obtained from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression datasets. The cBioPortal webtool was utilized to examine genomic changes in different cancer types. The prognostic significance of HPDL in pancancer was evaluated using univariate Cox regression analysis. Extensive utilization of the CTRP and PRISM databases was performed to forecast potential medications that specifically target HPDL in LUAD. In summary, studies were conducted to evaluate the impact of HPDL on the proliferation and movement of LUAD cells using loss-of-function experiments. HPDL is expressed excessively in a wide variety of cancer types, indicating its prognostic and predictive value. Moreover, we emphasized the strong correlation between HPDL and indicators of immune stimulation, infiltration of immune cells, and expression of immunoregulators. The remarkable finding of the HPDL was its capacity to precisely anticipate responses to cancer therapies using anti-PDL1 and anti-PD1 antibodies among individuals. Moreover, HPDL can function as a predictive marker for specific inhibitors in instances of cancer. Suppression of HPDL resulted in reduced growth and movement of LUAD cells. To summarize, our results suggest that HPDL acts as a prospective predictor of outcomes and a positive indication of response to immunotherapy in patients undergoing treatment with immune checkpoint inhibitors (ICIs).

Cholesterol homeostasis confers glioma malignancy triggered by hnRNPA2B1-dependent regulation of SREBP2 and LDLR.

Dysregulation of cholesterol metabolism is a significant characteristic of glioma, yet the underlying mechanisms are largely unknown. N6-methyladenosine (m6A) modification has been implicated in promoting tumor development and progression. The aim of this study was to determine the key m6A regulatory proteins involved in the progression of glioma, which is potentially associated with the reprogramming of cholesterol homeostasis. Bioinformatics analysis was performed to determine the association of m6A modification with glioma malignancy from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Glioma stem cell (GSC) self-renewal was determined by tumor sphere formation and bioluminescence image assay. RNA sequencing and lipidomic analysis were performed for cholesterol homeostasis analysis. RNA immunoprecipitation and luciferase reporter assay were performed to determine hnRNPA2B1-dependent regulation of sterol regulatory element-binding protein 2 (SREBP2) and low-density lipoprotein receptor (LDLR) mRNA. The methylation status of hnRNPA2B1 promoter was determined by bioinformatic analysis and methylation-specific PCR assay. Among the m6A-regulatory proteins, hnRNPA2B1 was demonstrated the most important independent prognostic risk factor for glioma. hnRNPA2B1 ablation exhibited a significant tumor-suppressive effect on glioma cell proliferation, GSC self-renewal and tumorigenesis. hnRNPA2B1 triggers de novo cholesterol synthesis by inducing HMGCR through the stabilization of SREBP2 mRNA. m6A modification of SREBP2 or LDLR mRNA is required for hnRNPA2B1-mediated mRNA stability. The hypomethylation of cg21815882 site on hnRNPA2B1 promoter confers elevated expression of hnRNPA2B1 in glioma tissues. The combination of targeting hnRNPA2B1 and cholesterol metabolism exhibited remarkable antitumor effects, suggesting valuable clinical implications for glioma treatment. hnRNPA2B1 facilitates cholesterol uptake and de novo synthesis, thereby contributing to glioma stemness and malignancy.

Predictive Roles of ADAM17 in Patient Survival and Immune Cell Infiltration in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the deadliest malignant tumour worldwide. The metalloproteinase ADAM17 is associated with tumour formation and development; however, its significance in HCC is unclear. This study aimed to investigate the role of ADAM17 in HCC and the correlation between its expression and immune cell infiltration. ADAM17 expression was analysed in pan-cancer and HCC tissues using The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Kaplan–Meier survival analysis displayed a negative association between ADAM17 expression and the overall survival of patients with HCC. High ADAM17 expression was linked to poor tumour/node (T/N) stage and alpha fetoprotein (AFP) levels. Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopaedia of Genes and Genomes analyses revealed the enrichment of several pathways, including epithelial–mesenchymal transition, inflammatory response, Hedgehog, and KRAS signalling, in patients with upregulated ADAM17. ADAM17 was shown to be positively correlated with immune cell infiltration and immune checkpoint expression via the Tumour Immune Estimation Resource (TIMER) database and immunohistochemistry analyses. Protein–protein interaction (PPI) network analysis revealed that ADAM17 plays a core role in cancer development and immune evasion. In vitro and in vivo experiments demonstrated that ADAM17 influences HCC growth and metastasis. In conclusion, ADAM17 is upregulated in most cancers, particularly HCC, and is critical in the development and immune evasion of HCC.

Establishment and validation of a prognostic signature for pancreatic ductal adenocarcinoma based on lactate metabolism-related genes.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal malignancy with poor prognosis. To improve patient outcomes, it is necessary to gain a better understanding of the oncogenesis and progression of this disease. Metabolic reprogramming, particularly the regulation of lactate metabolism, is known to have a significant impact on tumor microenvironment and could provide valuable insights for the management of PDAC patients. In this study, we aimed to investigate the prognostic potential of lactate metabolism-related genes (LMRGs). Methods: Transcriptomic data of patients with PDAC along with the clinical outcomes were retrieved from The Cancer Genome Atlas database, and the expression data in normal pancreas from Genotype-Tissue Expression dataset were adopted as the normal control. By using Cox and LASSO regression models, we identified key genes that are differentially expressed in cancerous tissues and related to prognosis. To determine the prognostic value of LMRGs in PDAC, we evaluated their clinical significance and model performance using both the area under the receiver operator characteristic curve (AUC) and calibration curves. In addition, we evaluated the drug sensitivity prediction and immune infiltration by using oncoPredict algorithm, single sample gene set enrichment analysis and Tumor Immune Estimation Resource. Results: A total of 123 LMRGs were identified through differential gene screening analysis, among which 7 LMRGs were identified to comprise a LMRGs signature that independently predict overall survival of these PDAC patient. The AUC values for the LMRGs signature were 0.786, 0.820, 0.837, and 0.816 for predicting 1-, 2-, 3- and 5-year overall survival respectively. Furthermore, this prognostic signature was used to stratify patients into high-risk and low-risk groups, with the former having worse clinical outcomes. This observation was further validated through analysis of the International Cancer Genome Consortium database. In addition, lower sensitivity to gemcitabine and infiltration of immune effector cells were observed in the cancer tissue of patients in the high-risk group. Conclusion: In conclusion, our data suggests that a genomic signature comprised of these LMRGs may be a novel predictor of overall clinical outcomes and present therapeutic potential for PDAC patients.

LINC00638/hsa-miR-29b-3p axis-mediated high expression of CDCA4 correlates with tumor immune infiltration and hepatocellular carcinoma progression.

This study sought to explore the role of cell division cycle-associated protein 4 (CDCA4) in liver hepatocellular carcinoma (LIHC) patients. The RNA-sequencing raw count data and the respective clinical information of 33 different LIHC cancer and normal tissues were collected from the Genotype Tissue Expression (GTEX) and The Cancer Genome Atlas (TCGA) databases. The expression of CDCA4 in LIHC was determined via the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) database. The PrognoScan database was used to examine the correlation between CDCA4 and overall survival (OS) in LIHC. The interaction between the potential upstream microRNAs and the long non-coding RNAs (lncRNAs) and CDCA4 was explored using the Encyclopedia of RNA Interactomes (ENCORI) database. Finally, the biological role of CDCA4 in LIHC was investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)analyses. CDCA4 RNA expression was elevated in the LIHC tumor tissues and linked to adverse clinical characteristics. It was also upregulated in most tumor tissues in the GTEX and TCGA data sets. According to the receiver operating characteristic (ROC) curve analysis, CDCA4 is a potential a biomarker for the diagnosis of LIHC. According to the Kaplan-Meier (KM) curve analysis, patients with LIHC in TCGA data set with low expression levels of CDCA4 had better than high expression levels in OS, disease-specific survival (DSS), and progression free interval (PFI). The gene set enrichment analysis (GSEA) suggested that CDCA4 mainly affected the biological events of LIHC by participating in the cell cycle, T cell receptor signaling pathway, DNA replication, glucose metabolism, and mitogen activated protein kinase (MAPK) signaling pathway. Based on the competing endogenous RNA concept and the correlation, expression, and survival analysis results, we believe that LINC00638/hsa miR-29b-3p/CDCA4 should be a potential regulatory pathway in LIHC. The low expression of CDCA4 significantly improves the prognosis of LIHC patients, and CDCA4 is a potential new biomarker for LIHC prognosis prediction. CDCA4-mediated LIHC carcinogenesis may involve tumor immune evasion and anti-tumor immunity. LINC00638/hsa-miR-29b-3p/CDCA4 should be a potential regulatory pathway in LIHC, and these findings provide a new perspective for the development of anti-cancer strategies in LIHC.

EDEM2 is a diagnostic and prognostic biomarker and associated with immune infiltration in glioma: A comprehensive analysis.

Glioma is a highly common pathological brain tumor. Misfolded protein response, which is strongly associated with the growth of cancerous tumors, is mediated by the gene, endoplasmic reticulum degradation-enhancing alpha-mannosidase-like protein 2. However, this gene has not been linked to glioma. To assess the same, we used The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Genotype-Tissue Expression datasets. The gene was overexpressed in gliomas. This overexpression was linked to unfavorable clinical characteristics, such as the World Health Organization grade, isocitrate dehydrogenase mutation, and the combined loss of the short arm chromosome 1 and the long arm of chromosome 19. Quantitative polymerase chain reaction experiments and immunohistochemistry on clinical samples from our institution verified the gene's expression and clinical importance. The Human Protein Atlas website verified the messenger ribonucleic acid expression of the gene in glioma cell lines, and immunohistochemistry verified the presence of its protein. A previous survival study indicated that its high expression is substantially related to a bad prognosis. It was identified as an independent predictor of primary glioma prognosis using multivariate Cox regression analysis. To forecast individual survival, we created a nomogram based on this (concordance-index = 0.847). Additionally, functional annotation demonstrated its major role in the control of the extracellular matrix and immune system. The scratch assay and transwell migration assay confirmed the decreased invasive ability of U251 glioma cells with the gene knockdown. Its increased expression was found to be related to the extent of macrophage infiltration using the CIBERSORT, ESTIMATE, Single-sample Gene Set Enrichment Analysis, and Tumor Immune Single-Cell Hub (TISCH) algorithms. The Tumor Immune Dysfunction and Exclusion algorithm revealed that the gene can accurately predict the response of immunotherapy (area under the receiver operating characteristic curve = 0.857). Further, isocitrate dehydrogenase 1 mutation is typically more frequent when the gene expression is high. Finally, five medicines targeting this gene were discovered utilizing the molecular docking program and drug sensitivity analysis of the RNAactDrug website. Low expression of the gene inhibited glioma cell invasion. Therefore, the gene is helpful for the diagnosis, prognosis, and case-specific immunotherapy of glioma.

Genetic Regulation of SMC Gene Expression and Splicing Predict Causal CAD Genes.

Coronary artery disease (CAD) is the leading cause of death worldwide. Recent meta-analyses of genome-wide association studies have identified over 175 loci associated with CAD. The majority of these loci are in noncoding regions and are predicted to regulate gene expression. Given that vascular smooth muscle cells (SMCs) play critical roles in the development and progression of CAD, we aimed to identify the subset of the CAD loci associated with the regulation of transcription in distinct SMC phenotypes. We measured gene expression in SMCs isolated from the ascending aortas of 151 heart transplant donors of various genetic ancestries in quiescent or proliferative conditions and calculated the association of their expression and splicing with ~6.3 million imputed single-nucleotide polymorphism markers across the genome. We identified 4910 expression and 4412 splicing quantitative trait loci (sQTLs) representing regions of the genome associated with transcript abundance and splicing. A total of 3660 expression quantitative trait loci (eQTLs) had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 eQTLs were SMC-specific and sex-biased, respectively. We made these results available for public query on a user-friendly website. To identify the effector transcript(s) regulated by CAD loci, we used 4 distinct colocalization approaches. We identified 84 eQTL and 164 sQTL that colocalized with CAD loci, highlighting the importance of genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. Notably, 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. One CAD locus colocalized with a sex-specific eQTL (TERF2IP), and another locus colocalized with SMC-specific eQTL (ALKBH8). The most significantly associated CAD locus, 9p21, was an sQTL for the long noncoding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs. Collectively, our results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in distinct SMC phenotypes.

TEx-MST: tissue expression profiles of MANE select transcripts.

Recently, a new reference transcript dataset [Matched Annotation from the NCBI and EMBL-EBI (MANE) select] was released by NCBI and EMBL-EBI to make available a new unified representative transcript for human protein-coding genes. While the main purpose of MANE project is to provide a harmonized gene and transcript information standard, there is no explicit tissue expression information about these MANE select transcripts. In this report, we tried to provide useful expression profiles of MANE select transcripts in various normal human tissues to allow further interrogation of their molecular modulations and functional significance. We obtained the new V9 transcript expression dataset from the Genotype-Tissue Expression (GTEx) web portal. This new GTEx dataset, based on a long-read sequencing platform, affords better assessment of the expression of alternative spliced transcripts. This tissue expression profiles of MANE select transcripts (TEx-MST) database not only provides the basic information of MANE select transcripts but also tissue expression profiles on alternative transcripts in protein-coding genes. Users can initiate the interrogation by gene symbol searches or by browsing the MANE genes with various criteria (such as genome locations or expression rankings). We further utilized the GENCODE biotype feature to identify the top-ranked protein-coding transcripts by choosing the most expressed protein-coding transcripts from GTEx datasets (both V8 and V9 datasets). In summary, there are 18 083 genes matched between MANE and GTEx. Among them, 13 245 MANE select transcripts matched with the top-ranked protein-coding transcripts in GTEx V9 dataset, which underlined the dominate expression of MANE select transcripts. This TEx-MST web bioinformatic database provides a visualized user interface for the normal tissue expression patterns of MANE select transcripts using the newly released GTEx dataset. Database URL: TEx-MST is available at https://texmst.ibms.sinica.edu.tw/

Pan-cancer analysis of the angiotensin II receptor-associated protein as a prognostic and immunological gene predicting immunotherapy responses in pan-cancer.

Background: Understanding interior molecular mechanisms of tumorigenesis and cancer progression contributes to antitumor treatments. The angiotensin II receptor-associated protein (AGTRAP) has been confirmed to be related with metabolic products in metabolic diseases and can drive the progression of hepatocellular carcinoma and colon carcinoma. However, functions of AGTRAP in other kinds of cancers are unclear, and a pan-cancer analysis of AGTRAP has not been carried out. Methods and materials: We downloaded data from The Cancer Genome Atlas and Genotype-Tissue Expression dataset and The Human Protein Atlas databases and then used R software (version 4.1.1) and several bioinformatic tools to conduct the analysis. Results: In our study, we evaluated the expression of AGTRAP in cancers, such as high expression in breast cancer, lung adenocarcinoma, and glioma and low expression in kidney chromophobe. Furthermore, our study revealed that high expression of AGTRAP is significantly related with poor prognosis in glioma, liver cancer, kidney chromophobe, and so on. We also explored the putative functional mechanisms of AGTRAP across pan-cancer, such as endoplasmic reticulum pathway, endocytosis pathway, and JAK-STAT signaling pathway. In addition, the connection between AGTRAP and tumor microenvironment, tumor mutation burden, and immune-related genes was proven. Conclusion: Our study provided comprehensive evidence of the roles of AGTRAP in different kinds of cancers and supported the relationship of AGTRAP and tumorous immunity.

Identification of an immune-related gene signature as a prognostic target and the immune microenvironment for adrenocortical carcinoma.

BackgroundAdrenocortical carcinoma (ACC) is a rare endocrine malignancy. Even with complete tumor resection and adjuvant therapies, the prognosis of patients with ACC remains unsatisfactory. In the microtumor environment, the impact of a disordered immune system and abnormal immune responses is enormous. To improve treatment, novel prognostic predictors and treatment targets for ACC need to be identified. Hence, credible prognostic biomarkers of immune‐associated genes (IRGs) should be explored and developed.Material and methodsWe downloaded RNA‐sequencing data and clinical data from The Cancer Genome Atlas (TCGA) data set, Genotype‐Tissue Expression data set, and Gene Expression Omnibus data set. Gene set enrichment analysis (GSEA) was applied to reveal the potential functions of differentially expressed genes.ResultsGSEA indicated an association between ACC and immune‐related functions. We obtained 332 IRGs and constructed a prognostic signature on the strength of 3 IRGs (INHBA, HELLS, and HDAC4) in the training cohort. The high‐risk group had significantly poorer overall survival than the low‐risk group (p < .001). Multivariate Cox regression was performed with the signature as an independent prognostic indicator for ACC. The testing cohort and the entire TCGA ACC cohort were utilized to validate these findings. Moreover, external validation was conducted in the GSE10927 and GSE19750 cohorts. The tumor‐infiltrating immune cells analysis indicated that the quantity of T cells, natural killer cells, macrophage cells, myeloid dendritic cells, and mast cells in the immune microenvironment differed between the low‐risk and high‐risk groups.ConclusionOur three‐IRG prognostic signature and the three IRGs can be used as prognostic indicators and potential immunotherapeutic targets for ACC. Inhibitors of the three novel IRGs might activate immune cells and play a synergistic role in combination therapy with immunotherapy for ACC in the future.

Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia.

BackgroundPancreatic cancer is one of the most fatal malignancies of the gastrointestinal cancer, with a challenging early diagnosis due to lack of distinctive symptoms and specific biomarkers. The exact etiology of pancreatic cancer is unknown, making the development of reliable biomarkers difficult. The accumulation of patient-derived omics data along with technological advances in artificial intelligence is giving way to a new era in the discovery of suitable biomarkers.MethodsWe performed machine learning (ML)-based modeling using four independent transcriptomic datasets, including GSE16515, GSE62165, GSE71729, and the pancreatic adenocarcinoma (PAC) dataset of the Cancer Genome Atlas. To find candidates for circulating biomarkers, we exported expression profiles of 1,703 genes encoding secretory proteins. Integrating three transcriptomic datasets into either a training or test set, ML-based modeling distinguishing PAC from normal was carried out. Another ML-model classifying long-lived and short-lived patients with PAC was also built to select prognosis-associated features. Finally, circulating level of SCG5 in the plasma was determined from the independent cohort (non-tumor = 25 and pancreatic cancer = 25). We also investigated the impact of SCG5 on adipocyte biology using recombinant protein.ResultsThree distinctive ML-classifiers selected 29-, 64- and 18-featured genes, recognizing the only common gene, SCG5. As per the prediction of ML-models, the SCG5 transcripts was significantly reduced in PAC and decreased further with the progression of the tumor, indicating its potential as a diagnostic as well as prognostic marker for PAC. External validation of SCG5 using plasma samples from patients with PAC confirmed that SCG5 was reduced significantly in patients with PAC when compared to controls. Interestingly, plasma SCG5 levels were correlated with the body mass index and age of donors, implying pancreas-originated SCG5 could regulate energy metabolism systemically. Additionally, analyses using publicly available Genotype-Tissue Expression datasets, including adipose tissue histology and pancreatic SCG5 expression, further validated the association between pancreatic SCG5 expression and the size of subcutaneous adipocytes in humans. However, we could not observe any definite effect of rSCG5 on the cultured adipocyte, in 2D in vitro culture.ConclusionCirculating SCG5, which may be associated with adipopenia, is a promising diagnostic biomarker for PAC.

Role of CD68 in tumor immunity and prognosis prediction in pan-cancer

CD68 plays a critical role in promoting phagocytosis; however, the function of CD68 in tumor immunity and prognosis remains unknown. We analyzed CD68 expression among 33 tumor and normal tissues from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. The relationship between CD68 expression and cancer prognosis, immune infiltration, checkpoint markers, and drug response was explored. Upregulated CD68 levels were observed in various cancer types, which were verified through tumor tissue chips using immunohistochemistry. High levels of CD68 in tumor samples correlated with an adverse prognosis in glioblastoma, kidney renal clear cell carcinoma, lower-grade glioma, liver hepatocellular carcinoma, lung squamous cell carcinoma, thyroid carcinoma, and thymoma and a favorable prognosis in kidney chromophobe. The top three negatively enriched Kyoto Encyclopedia of Genes and Genomes terms in the high CD68 subgroup were chemokine signaling pathway, cytokine-cytokine receptor interaction, and cell adhesion molecule cams. The top negatively enriched HALLMARK terms included complement, allograft rejection, and inflammatory response. A series of targeted drugs and small-molecule drugs with promising therapeutic effects were predicted. The clinical prognosis and immune infiltration of high expression levels of CD68 differ across tumor types. Inhibiting CD68-dependent signaling could be a promising therapeutic strategy for immunotherapy in many tumor types.

Tumorigenic and immunological roles of Heat shock protein A2 in pancreatic cancer: a bioinformatics analysis.

Heat shock protein A2 has been reported to be tightly associated with tumorigenesis and tumor progression. This study aimed to determine the oncogenic and immunological roles of Heat shock protein A2 in pancreatic cancer by bioinformatics. Expression of Heat shock protein A2 in tumorous and normal specimens of pancreatic cancer was analyzed using the Cancer Genome Atlas and the Cancer Genome Atlas + Genotype-Tissue Expression data sets, respectively. Relationships of Heat shock protein A2 expression with immune infiltrates in pancreatic cancer were assessed. Heat shock protein A2-associated coexpressed genes in pancreatic cancer were obtained, followed by the implementation of enrichment analysis. The data demonstrated that Heat shock protein A2 was significantly overexpressed in tumorous samples compared with normal samples. Heat shock protein A2 expression was remarkably positively interrelated with CD8+ T cell, neutrophil, dendritic cell, and macrophage, but not with CD4+ T and B cells. Heat shock protein A2 expression was markedly positively relevant to both cancer-associated fibroblast and endothelial cell. Enrichment data revealed that Heat shock protein A2 was intimately involved in the tumorigenesis and progression of pancreatic cancer. Heat shock protein A2 is upregulated in pancreatic cancer and is closely associated with tumor immunity and aggressive progression.

Widespread choroid plexus contamination in sampling and profiling of brain tissue

The choroid plexus, a tissue responsible for producing cerebrospinal fluid, is found predominantly in the lateral and fourth ventricles of the brain. This highly vascularized and ciliated tissue is made up of specialized epithelial cells and capillary networks surrounded by connective tissue. Given the complex structure of the choroid plexus, this can potentially result in contamination during routine tissue dissection. Bulk and single-cell RNA sequencing studies, as well as genome-wide in situ hybridization experiments (Allen Brain Atlas), have identified several canonical markers of choroid plexus such as Ttr, Folr1, and Prlr. We used the Ttr gene as a marker to query the Gene Expression Omnibus database for transcriptome studies of brain tissue and identified at least some level of likely choroid contamination in numerous studies that could have potentially confounded data analysis and interpretation. We also analyzed transcriptomic datasets from human samples from Allen Brain Atlas and the Genotype-Tissue Expression (GTEx) database and found abundant choroid contamination, with regions in closer proximity to choroid more likely to be impacted such as hippocampus, cervical spinal cord, substantia nigra, hypothalamus, and amygdala. In addition, analysis of both the Allen Brain Atlas and GTEx datasets for differentially expressed genes between likely “high contamination” and “low contamination” groups revealed a clear enrichment of choroid plexus marker genes and gene ontology pathways characteristic of these ciliated choroid cells. Inclusion of these contaminated samples could result in biological misinterpretation or simply add to the statistical noise and mask true effects. We cannot assert that Ttr or other genes/proteins queried in targeted assays are artifacts from choroid contamination as some of these differentials may be due to true biological effects. However, for studies that have an unequal distribution of choroid contamination among groups, investigators may wish to remove contaminated samples from analyses or incorporate choroid marker gene expression into their statistical modeling. In addition, we suggest that a simple RT-qPCR or western blot for choroid markers would mitigate unintended choroid contamination for any experiment, but particularly for samples intended for more costly omic profiling. This study highlights an unexpected problem for neuroscientists, but it is also quite possible that unintended contamination of adjacent structures occurs during dissections for other tissues but has not been widely recognized.

SERPINH1 is a Potential Prognostic Biomarker and Correlated With Immune Infiltration: A Pan-Cancer Analysis.

Background: Serpin peptidase inhibitor clade H, member 1 (SERPINH1) is a gene encoding a member of the serpin superfamily of serine proteinase inhibitors. The upregulated of SERPINH1 was associated with poor prognosis in breast cancer, stomach adenocarcinoma, and esophageal carcinoma. However, the role of SERPINH1 in pan-cancer is largely unexplored. Methods: SERPINH1 expression and the correlation with prognosis in human pan-cancer were analyzed by the Cancer Genome Atlas and the Genotype-Tissue Expression dataset. Pearson correlation analysis was applied to evaluate the role of SERPINH1 expression in tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), DNA methyltransferase, and common immunoregulators. Spearman’s correlation test was used to analysis SERPINH1 expression in tumor immune infiltration and infiltrating immune cells via the Tumor Immune Evaluation Resource database. Furtherly, immunohistochemistry staining of SERPINH1 was acquired from the Human Protein Atlas database for validation. Results: SERPINH1 was abnormally expressed in fourteen cancers. The high expression of SERPINH1 significantly reduced the overall survival (OS), disease-specific survival, and progression free interval in eleven cancers. Moreover, SERPINH1 expression was correlated with MMR, MSI, TMB, and DNA methylation in multiple types of cancer. Also, SERPINH1 expression showed strong association with immunoregulators and immune checkpoint markers in testicular germ cell tumors, brain lower grade glioma (LGG), pheochromocytoma and paraganglioma. In addition, SERPINH1 expression was related to immune cell infiltration in multiple cancers, particularly in breast invasive carcinoma, LGG, and liver hepatocellular carcinoma. The result of immunohistochemistry verification shown that SERPINH1 staining was higher in tumor samples than in normal tissue in colon adenocarcinoma, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma and cervical squamous cell carcinoma, which was consistent with the result of OS. Conclusion: Overall, these results indicate that SERPINH1 may serve as an important prognostic biomarker and correlate with tumor immunity in human pan-cancer.

Identifying cancer pathway dysregulations using differential causal effects.

MotivationSignaling pathways control cellular behavior. Dysregulated pathways, for example, due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer.ResultsWe introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, technical artifacts have less influence on the results and dce is more likely to detect the true biological signals. We extend the approach to handle unobserved dense confounding, where each latent variable, such as, for example, batch effects or cell cycle states, affects many covariates. We show that dce outperforms competing methods on synthetic datasets and on CRISPR knockout screens. We validate its latent confounding adjustment properties on a GTEx (Genotype–Tissue Expression) dataset. Finally, in an exploratory analysis on breast cancer data from TCGA (The Cancer Genome Atlas), we recover known and discover new genes involved in breast cancer progression.Availability and implementationThe method dce is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/dce.html) as well as on https://github.com/cbg-ethz/dce. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.Supplementary information Supplementary data are available at Bioinformatics online.

Sex-Specific Associations of Genetically Predicted Circulating Lp(a) (Lipoprotein(a)) and Hepatic LPA Gene Expression Levels With Cardiovascular Outcomes: Mendelian Randomization and Observational Analyses.

Elevated Lp(a) (Lipoprotein(a)) levels are associated with coronary artery disease (CAD), ischemic stroke (IS), and calcific aortic valve stenosis (CAVS). Studies investigating the association between Lp(a) levels and these diseases in women have yielded inconsistent results. To investigate the association of Lp(a) with sex-specific cardiovascular outcomes, we determined the association between genetically predicted Lp(a) levels (using 27 single nucleotide polymorphisms at the LPA locus) and hepatic LPA expression (using 80 single nucleotide polymorphisms at the LPA locus associated with LPA mRNA expression in liver samples from the Genotype-Tissue Expression dataset) on CAD, IS, and CAVS using individual participant data from the UK Biobank: 408 403 participants of European ancestry (37 102, 4283, and 2574 with prevalent CAD, IS, and CAVS, respectively). The long-term association between Lp(a) levels and incident CAD, IS, and CAVS was also investigated in European Prospective Investigation into Cancer and Nutrition-Norfolk: 18 721 participants (3964, 846, and 424 with incident CAD, IS, and CAVS, respectively). Genetically predicted plasma Lp(a) levels were positively and similarly associated with prevalent and incident CAD and CAVS in men and women. Genetically predicted plasma Lp(a) levels were associated with prevalent and incident IS when we studied men and women pooled together, and in men only. Genetically predicted LPA expression levels were associated with prevalent CAD and CAVS in men and women but not with IS. Genetically predicted blood Lp(a) and hepatic LPA gene expression as well as serum Lp(a) levels predict the risk of CAD and CAVS in men and in women. Whether RNA interference therapies aiming at lowering Lp(a) levels could be useful in reducing cardiovascular disease risk in both men and women with high Lp(a) levels needs to be determined in large-scale cardiovascular outcomes trials.

RNA-Binding Proteins Play an Important Role in the Prognosis of Patients With Testicular Germ Cell Tumor.

Testicular germ cell tumors (TGCTs) are common urological neoplasms in young adult males. The outcome of TGCT depends on pathologic type and tumor stage. RNA-binding proteins (RBPs) influence numerous cancers via post-transcriptional regulation. The prognostic importance of RBPs in TGCT has not been fully investigated. In this study, we set up a prognostic risk model of TGCT using six significantly differentially expressed RBPs, namely, TRMT61A, POLR2J, DIS3L2, IFIH1, IGHMBP2, and NPM2. The expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression datasets. We observed by performing least absolute shrinkage and selection operator (LASSO) regression analyses that in the training cohort, the expression of six RBPs was correlated with disease-free survival in patients with TGCT. We assessed the specificity and sensitivity of 1-, 3-, 5-, and 10-year survival status prediction using receiver operating characteristic curve analysis and successfully validated using the test cohorts, the entire TCGA cohort, and Gene Expression Omnibus (GEO) datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analyses were carried out to seek the possible signaling pathways related with risk score. We also examined the association between the model based on six RBPs and different clinical characteristics. A nomogram was established for TGCT recurrence prediction. Consensus clustering analysis was carried out to identify the clusters of TGCT with different clinical outcomes. Ultimately, external validations of the six-gene risk score were performed by using the GSE3218 and GSE10783 datasets downloaded from the GEO database. In general, our study constructed a prognostic model based on six RBPs, which could serve as independent risk factor in TGCT, especially in seminoma, and might have brilliant clinical application value.