Alterations of type II classical cadherin, cadherin-10 (CDH10), is associated with pancreatic ductal adenocarcinomas

Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas

Background: Aldosterone-producing adenomas (APA) are a common cause of primary aldosteronism (PA), a clinical syndrome characterized by hypertension and electrolyte disturbances. If untreated, it may lead to serious cardiovascular complications. Therefore, there is an urgent need for potential biomarkers and targeted drugs for the diagnosis and treatment of aldosteronism. Methods: We downloaded two datasets (GSE156931 and GSE60042) from the GEO database and merged them by de-batch effect, then screened the top50 of differential genes using PPI and enriched them, followed by screening the Aldosterone adenoma-related genes (ARGs) in the top50 using three machine learning algorithms. We performed GSEA analysis on the ARGs separately and constructed artificial neural networks based on the ARGs. Finally, the Enrich platform was utilized to identify drugs with potential therapeutic effects on APA by tARGseting the ARGs. Results: We identified 190 differential genes by differential analysis, and then identified the top50 genes by PPI, and the enrichment analysis showed that they were mainly enriched in amino acid metabolic pathways. Then three machine learning algorithms identified five ARGs, namely, SST, RAB3C, PPY, CYP3A4, CDH10, and the ANN constructed on the basis of these five ARGs had better diagnostic effect on APA, in which the AUC of the training set is 1 and the AUC of the validation set is 0.755. And then the Enrich platform identified drugs tARGseting the ARGs with potential therapeutic effects on APA. Conclusion: We identified five ARGs for APA through bioinformatic analysis and constructed Artificial neural network (ANN) based on them with better diagnostic effects, and identified drugs with potential therapeutic effects on APA by tARGseting these ARGs. Our study provides more options for the diagnosis and treatment of APA.

An 8q24 Gain in Pancreatic Juice Is a Candidate Biomarker for the Detection of Pancreatic Cancer

Secretin-stimulated pancreatic juice (PJ), collected from the duodenum, presents a valuable biomarker source for the (earlier) detection of pancreatic cancer (PC). Here, we evaluate the feasibility and performance of shallow sequencing to detect copy number variations (CNVs) in cell-free DNA (cfDNA) from PJ for PC detection. First, we confirmed the feasibility of shallow sequencing in PJ (n = 4), matched plasma (n = 3) and tissue samples (n = 4, microarray). Subsequently, shallow sequencing was performed on cfDNA from PJ of 26 cases (25 sporadic PC, 1 high-grade dysplasia) and 19 controls with a hereditary or familial increased risk of PC. 40 of the 45 PJ samples met the quality criteria for cfDNA analysis. Nine individuals had an 8q24 gain (oncogene MYC; 23%; eight cases (33%) and one control (6%), p = 0.04); six had both a 2q gain (STAT1) and 5p loss (CDH10; 15%; four cases (7%) and two controls (13%), p = 0.72). The presence of an 8q24 gain differentiated the cases and controls, with a sensitivity of 33% (95% CI 16-55%) and specificity of 94% (95% CI 70-100%). The presence of either an 8q24 or 2q gain with a 5p loss was related to a sensitivity of 50% (95% CI 29-71%) and specificity of 81% (95% CI 54-96%). Shallow sequencing of PJ is feasible. The presence of an 8q24 gain in PJ shows promise as a biomarker for the detection of PC. Further research is required with a larger sample size and consecutively collected samples in high-risk individuals prior to implementation in a surveillance cohort.

Characterization of Immune-Related Molecular Subtypes and a Prognostic Signature Correlating With the Response to Immunotherapy in Patients With Gastric Cancer

Gastric cancer (GC) is a disease characterized by high molecular and phenotypic heterogeneity and represents a leading cause of cancer-related death worldwide. The tumor immune microenvironment (TIME) affects the response to immunotherapy and the prognosis of patients with GC. Explorations of the TIME in GC and characterization of molecular subtypes might enhance personalized treatment and facilitate clinical decision-making. In this study, two molecular subtypes were defined through unsupervised consensus clustering based on immune-related dysregulated genes. Then, patients with different molecular subtypes of GC were shown to have distinct differences in sensitivity to immune checkpoint blockers (ICBs). The immune-related prognostic signature was established utilizing least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. Three independent external cohorts and the IMvigor210 cohort were introduced to validate the robustness of IPRS. scRNA-seq data of GC samples were used to decipher the underlying mechanisms of how IPRS contributes to the TIME. GC biospecimens were collected for RT-qPCR to further validate our findings. In summary, we characterized the abnormal TIME of GC and constructed a reliable immune-related prognostic signature correlating with the response to immunotherapy. This study may provide new strategies for developing individualized treatments for patients with GC.

Identification and verification of the molecular mechanisms and prognostic values of the cadherin gene family in gastric cancer

While cadherin (CDH) genes are aberrantly expressed in cancers, the functions of CDH genes in gastric cancer (GC) remain poorly understood. The clinical significance and molecular mechanisms of CDH genes in GC were assessed in this study. Data from a total of 1226 GC patients included in The Cancer Genome Atlas (TCGA) and Kaplan–Meier plotter database were used to independently explore the value of CDH genes in clinical application. The TCGA RNA sequencing dataset was used to explore the molecular mechanisms of CDH genes in GC. Using enrichment analysis tools, CDH genes were found to be related to cell adhesion and calcium ion binding in function. In TCGA cohort, 12 genes were found to be differentially expressed between GC para-carcinoma and tumor tissue. By analyzing GC patients in two independent cohorts, we identified and verified that CDH2, CDH6, CDH7 and CDH10 were significantly associated with a poor GC prognosis. In addition, CDH2 and CDH6 were used to construct a GC risk score signature that can significantly improve the accuracy of predicting the 5-year survival of GC patients. The GSEA approach was used to explore the functional mechanisms of the four prognostic CDH genes and their associated risk scores. It was found that these genes may be involved in multiple classic cancer-related signaling pathways, such as the Wnt and phosphoinositide 3-kinase signaling pathways in GC. In the subsequent CMap analysis, three small molecule compounds (anisomycin, nystatin and bumetanide) that may be the target molecules that determine the risk score in GC, were initially screened. In conclusion, our current study suggests that four CDH genes can be used as potential biomarkers for GC prognosis. In addition, a prognostic signature based on the CDH2 and CDH6 genes was constructed, and their potential functional mechanisms and drug interactions explored.

Theragnostic chromosomal rearrangements in treatment-naive pancreatic ductal adenocarcinomas obtained via endoscopic ultrasound

A crucial mutational mechanism in malignancy is structural variation, in which chromosomal rearrangements alter gene functions that drive cancer progression. Herein, the presence and pattern of structural variations were investigated in twelve prospectively acquired treatment‐naïve pancreatic cancers specimens obtained via endoscopic ultrasound (EUS). In many patients, this diagnostic biopsy procedure and specimen is the only opportunity to identify somatic clinically relevant actionable alterations that may impact their care and outcome. Specialized mate pair sequencing (MPseq) provided genome‐wide structural variance analysis (SVA) with a view to identifying prognostic markers and possible therapeutic targets. MPseq was successfully performed on all specimens, identifying highly rearranged genomes with complete SVA on all specimens with > 20% tumour content. SVA identified chimeric fusion proteins and potentially immunogenic readthrough transcripts, change of function truncations, gains and losses of key genes linked to tumour progression. Complex localized rearrangements, termed chromoanagenesis, with broad pattern heterogeneity were observed in 10 (83%) specimens, impacting multiple genes with diverse cellular functions that could influence theragnostic evaluation and responsiveness to immunotherapy regimens. This study indicates that genome‐wide MPseq can be successfully performed on very limited clinically EUS obtained specimens for chromosomal rearrangement detection and potential theragnostic targets.

G9a-mediated repression of CDH10 in hypoxia enhances breast tumour cell motility and associates with poor survival outcome

Rationale: Epigenetic mechanisms are fundamental processes that can modulate gene expression, allowing cellular adaptation to environmental conditions. Hypoxia is an important factor known to initiate the metastatic cascade in cancer, activating cell motility and invasion by silencing cell adhesion genes. G9a is a histone methyltransferase previously shown to accumulate in hypoxic conditions. While its oncogenic activity has been previously reported, not much is known about the role G9a plays in the hypoxia-mediated metastatic cascade.

Methods: The role of G9a in cell motility in hypoxic condition was determined by inhibiting G9a either by short-hairpin mediated knock down or pharmacologically using a small molecule inhibitor. Through gene expression profiling, we identified CDH10 to be an important G9a target that regulates breast cancer cell motility. Lung metastasis assay in mice was used to determine the physiological significance of G9a.

Results: We demonstrate that, while hypoxia enhances breast cancer migratory capacity, blocking G9a severely reduces cellular motility under both normoxic and hypoxic conditions and prevents the hypoxia-mediated induction of cellular movement. Moreover, inhibition of G9a histone methyltransferase activity in mice using a specific small molecule inhibitor significantly reduced growth and colonisation of breast cancer cells in the lung. We identify the type-II cadherin CDH10 as being a novel hypoxia-dependent gene, directly repressed by G9a through histone methylation. CDH10 overexpression significantly reduces cellular movements in breast cancer cell lines and prevents the hypoxia-mediated increase in cell motility. In addition, we show that CDH10 expression is prognostic in breast cancer and that it is inversely correlated to EHMT2 (G9a) transcript levels in many tumor-types, including breast cancer.

Conclusion: We propose that G9a promotes cellular motility during hypoxic stress through the silencing of the cell adhesion molecule CDH10 and we describe CDH10 as a novel prognostic biomarker for breast cancer.

The Transcriptome of Pig Spermatozoa, and Its Role in Fertility

In the study presented here we identified transcriptomic markers for fertility in the cargo of pig ejaculated spermatozoa using porcine-specific micro-arrays (GeneChip^® miRNA 4.0 and GeneChip^® Porcine Gene 1.0 ST). We report (i) the relative abundance of the ssc-miR-1285, miR-16, miR-4332, miR-92a, miR-671-5p, miR-4334-5p, miR-425-5p, miR-191, miR-92b-5p and miR-15b miRNAs, and (ii) the presence of 347 up-regulated and 174 down-regulated RNA transcripts in high-fertility breeding boars, based on differences of farrowing rate (FS) and litter size (LS), relative to low-fertility boars in the (Artificial Insemination) AI program. An overrepresentation analysis of the protein class (PANTHER) identified significant fold-increases for C-C chemokine binding (GO:0019957): CCR7, which activates B- and T-lymphocytes, 8-fold increase), XCR1 and CXCR4 (with ubiquitin as a natural ligand, 1.24-fold increase), cytokine receptor activity (GO:0005126): IL23R receptor of the IL23 protein, associated to JAK2 and STAT3, 3.4-fold increase), the TGF-receptor (PC00035) genes ACVR1C and ACVR2B (12-fold increase). Moreover, two micro-RNAs (miR-221 and mir-621) were down- and up-regulated, respectively, in high-fertility males. In conclusion, boars with different fertility performance possess a wide variety of differentially expressed RNA present in spermatozoa that would be attractive targets as non-invasive molecular markers for predicting fertility.

OMICfpp: a fuzzy approach for paired RNA-Seq counts

Background

RNA sequencing is a widely used technology for differential expression analysis. However, the RNA-Seq do not provide accurate absolute measurements and the results can be different for each pipeline used. The major problem in statistical analysis of RNA-Seq and in the omics data in general, is the small sample size with respect to the large number of variables. In addition, experimental design must be taken into account and few tools consider it.

Results

We propose OMICfpp, a method for the statistical analysis of RNA-Seq paired design data. First, we obtain a p-value for each case-control pair using a binomial test. These p-values are aggregated using an ordered weighted average (OWA) with a given orness previously chosen. The aggregated p-value from the original data is compared with the aggregated p-value obtained using the same method applied to random pairs. These new pairs are generated using between-pairs and complete randomization distributions. This randomization p-value is used as a raw p-value to test the differential expression of each gene. The OMICfpp method is evaluated using public data sets of 68 sample pairs from patients with colorectal cancer. We validate our results through bibliographic search of the reported genes and using simulated data set. Furthermore, we compared our results with those obtained by the methods edgeR and DESeq2 for paired samples. Finally, we propose new target genes to validate these as gene expression signatures in colorectal cancer. OMICfpp is available at http://www.uv.es/ayala/software/OMICfpp_0.2.tar.gz.

Conclusions

Our study shows that OMICfpp is an accurate method for differential expression analysis in RNA-Seq data with paired design. In addition, we propose the use of randomized p-values pattern graphic as a powerful and robust method to select the target genes for experimental validation.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5496-5) contains supplementary material, which is available to authorized users.