Identification of genes highly downregulated in pancreatic cancer through a meta-analysis of microarray datasets: implications for discovery of novel tumor-suppressor genes and therapeutic targets

Phospholipase A2 expression in prostate cancer as a biomarker of good prognosis: A comprehensive study in patients with long follow-up

BACKGROUND

Phospholipase A2 (PLA2) is a large family of enzymes involved in the inflammatory process that catalyzes the hydrolysis of membrane phospholipids, leading to the production of free fatty acids and lysophospholipids, starting the arachidonic acid cascade. Their expression has been related to the behavior of several cancers. Our objective is to search for PLA2 expression in prostate cancer (PCa) tissue that correlates with prognosis and survival.

METHODS

Using qRT-PCR, we analyzed the expression levels of PLA2G1B, PLA2G2A, PLA2G2D, PLA2G4A, PLA2G4B, PLA2G4C, PLA2G4D, PLA2G4E, PLA2G4F, PLA2G6, PLA2G7, PLA2G16, PNPLA1, and PNPLA2 in PCa tissue from 108 patients submitted to radical prostatectomy, followed by a mean time of 163 months.

RESULTS

All PLA2 was overexpressed in PCa compared to normal tissue. Interestingly, higher expression of some PLA2 was related to favorable prognostic factors: lower levels of PSA (PLA2G2A, PLA2G4D), lower rates of lymph node metastasis (PLA2G16 and PLA2G1B), and organ-confined disease (PLA2G4A). Most importantly, PLAG4B was independently related to longer disease-free survival.

CONCLUSION

This is the first study exploring comprehensively the expression levels of PLA2 in PCa, showing that the higher expression of some PLA2 should be used as biomarkers of good prognosis and longer disease-free survival.

Identification of biomarkers and potential therapeutic targets for pancreatic cancer by proteomic analysis in two prospective cohorts

Pancreatic cancer (PC) is the deadliest malignancy due to late diagnosis. Aberrant alterations in the blood proteome might serve as biomarkers to facilitate early detection of PC. We designed a nested case-control study of incident PC based on a prospective cohort of 38,295 elderly Chinese participants with ∼5.7 years' follow-up. Forty matched case-control pairs passed the quality controls for the proximity extension assay of 1,463 serum proteins. With a lenient threshold of p < 0.005, we discovered regenerating family member 1A (REG1A), REG1B, tumor necrosis factor (TNF), and phospholipase A2 group IB (PLA2G1B) in association with incident PC, among which the two REG1 proteins were replicated using the UK Biobank Pharma Proteomics Project, with effect sizes increasing steadily as diagnosis time approaches the baseline. Mendelian randomization analysis further supported the potential causal effects of REG1 proteins on PC. Taken together, circulating REG1A and REG1B are promising biomarkers and potential therapeutic targets for the early detection and prevention of PC.

An integrated study to decipher immunosuppressive cellular communication in the PDAC environment

Pancreatic ductal adenocarcinoma (PDAC) is one the most aggressive cancers and characterized by a highly rigid and immunosuppressive tumor microenvironment (TME). The extensive cellular interactions are known to play key roles in the immune evasion, chemoresistance, and poor prognosis. Here, we used the spatial transcriptomics, scRNA-seq, and bulk RNA-seq datasets to enhance the insights obtained from each to decipher the cellular communication in the TME. The complex crosstalk in PDAC samples was revealed by the single-cell and spatial transcriptomics profiles of the samples. We show that tumor-associated macrophages (TAMs) are the central cell types in the regulation of microenvironment in PDAC. They colocalize with the cancer cells and tumor-suppressor immune cells and take roles to provide an immunosuppressive environment. LGALS9 gene which is upregulated in PDAC tumor samples in comparison to healthy samples was also found to be upregulated in TAMs compared to tumor-suppressor immune cells in cancer samples. Additionally, LGALS9 was found to be the primary component in the crosstalk between TAMs and the other cells. The widespread expression of P4HB gene and its interaction with LGALS9 was also notable. Our findings point to a profound role of TAMs via LGALS9 and its interaction with P4HB that should be considered for further elucidation as target in the combinatory immunotherapies for PDAC.

Genetic analysis of pancreatic phospholipase A2 (PLA2G1B) in patients with chronic pancreatitis

BACKGROUND

Genetic mutations in various pancreatic enzymes or their counteracting proteins have been linked to chronic pancreatitis. In particular, variants in the genes encoding pancreatic lipase (PNLIP) and carboxyl ester lipase (CEL) have been associated with pancreatitis. Therefore, we investigated pancreatic phospholipase A2 (PLA2G1B) as a promising candidate gene in patients with chronic pancreatitis.

METHODS

We analyzed all coding exons and adjacent intronic regions of PLA2G1B in 416 German patients with non-alcoholic chronic pancreatitis (NACP) and 186 control subjects by direct DNA sequencing.

RESULTS

We detected 2 frequent synonymous variants in exon 3: c.222T>C (p.Y74 = ) and c.294G>A (p.S98 = ). The genotype and allele frequencies of these variants were similar between patients and controls (c.222 TC: 9.6% in NACP vs. 9.7% in controls; c.222CC: 0.2% in NACP vs. 0% in controls; c.294 GA: 31.3% in NACP vs. 28.0% in controls; c.294AA: 2.4% in NACP vs. 1.1% in controls). All p-values were non-significant. In addition, we found one synonymous variant, c.138C>T (p.N46 = ) and one non-synonymous variant, c.244A>G (p.S82G), in a single case each.

CONCLUSIONS

Our results suggest that genetic alterations in PLA2G1B do not predispose to the development of non-alcoholic chronic pancreatitis.

An Integrated Data Analysis of mRNA, miRNA and Signaling Pathways in Pancreatic Cancer

Although many genes and miRNAs have been reported for various cancers, pancreatic cancer's specific genes or miRNAs have not been studied precisely yet. Therefore, we have analyzed the gene and miRNA expression profile of pancreatic cancer data in the gene expression omnibus (GEO) database. The microarray-derived miRNAs and mRNAs were annotated by gene ontology (GO) and signaling pathway analysis. We also recognized mRNAs that were targeted by miRNA through the mirDIP database. An integrated analysis of the microarray revealed that only 6 out of 43 common miRNAs had significant differences in their expression profiles between the tumor and normal groups (P value < 0.05 and |log Fold Changes (logFC)|> 1). The hsa-miR-210 had upregulation, whereas hsa-miR-375, hsa-miR-216a, hsa-miR-217, hsa-miR-216b and hsa-miR-634 had downregulation in pancreatic cancer (PC). The analysis results also revealed 109 common mRNAs by microarray and mirDIP 4.1 databases. Pathway analysis showed that amoebiasis, axon guidance, PI3K-Akt signaling pathway, absorption and focal adhesion, adherens junction, platelet activation, protein digestion, human papillomavirus infection, extracellular matrix (ECM) receptor interaction, and riboflavin metabolism played important roles in pancreatic cancer. GO analysis revealed the significant enrichment in the three terms of biological process, cellular component, and molecular function, which were identified as the most important processes associated strongly with pancreatic cancer. In conclusion, DTL, CDH11, COL5A1, ITGA2, KIF14, SMC4, VCAN, hsa-mir-210, hsa-mir-217, hsa-mir-216a, hsa-mir-216b, hsa-mir-375 and hsa-mir-634 can be reported as the novel diagnostic or even therapeutic markers for the future studies. Also, the hsa-mir-107 and hsa-mir-125a-5p with COL5A1, CDH11 and TGFBR1 genes can be introduced as major miRNA and genes on the miRNA-drug-mRNA network. The new regulatory network created in our study could give a deeper knowledge of the pancreatic cancer.

The Case for GNMT as a Biomarker and a Therapeutic Target in Pancreatic Cancer

The high mortality rate for pancreatic cancer (PC) is due to the lack of specific symptoms at early tumor stages and a high biological aggressiveness. Reliable biomarkers and new therapeutic targets would help to improve outlook in PC. In this study, we analyzed the expression of GNMT in a panel of pancreatic cancer cell lines and in early-stage paired patient tissue samples (normal and diseased) by quantitative reverse transcription-PCR (qRT-PCR). We also investigated the effect of 1,2,3,4,6-penta-O-galloyl-β-d-glucopyranoside (PGG) as a therapeutic agent for PC. We find that GNMT is markedly downregulated (p < 0.05), in a majority of PC cell lines. Similar results are observed in early-stage patient tissue samples, where GNMT expression can be reduced by a 100-fold or more. We also show that PGG is a strong inhibitor of PC cell proliferation, with an IC₅₀ value of 12 ng/mL, and PGG upregulates GNMT expression in a dose-dependent manner. In conclusion, our data show that GNMT has promise as a biomarker and as a therapeutic target for PC.

A systematic analysis of a potential metabolism-related prognostic signature for breast cancer patients

Background

Metabolic pathways play an essential role in breast cancer. However, the role of metabolism-related genes in the early diagnosis of breast cancer remains unknown.

Methods

In our study, RNA sequencing (RNA-seq) expression data and clinicopathological information from The Cancer Genome Atlas (TCGA) and GSE20685 were obtained. Univariate cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expressed metabolism-related genes. Then, the formula of the metabolism-related risk model was composed, and the risk score of each patient was calculated. The breast cancer patients were divided into high-risk and low-risk groups with a cutoff of the median expression value of the risk score, and the prognostic analysis was also used to analyze the survival time between these two groups. In the end, we also analyzed the expression, interaction, and correlation among genes in the metabolism-related gene risk model.

Results

The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group in both TCGA and GSE20685 datasets. In addition, after adjusting for different clinicopathological features in multivariate analysis, the metabolism-related risk model remained an independent prognostic indicator in TCGA dataset.

Conclusions

In summary, we systematically developed a potential metabolism-related gene risk model for predicting prognosis in breast cancer patients.

Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues

A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed and 168 repressed genes in PDAC compared to the adjacent-to-tumor samples. The validation analysis using TCGA combined GTEx data confirmed 98.24% of the identified up-regulated and 73.88% of the down-regulated protein-coding genes in PDAC. Pathway enrichment analysis showed that “ECM-receptor interaction”, “PI3K-Akt signaling pathway”, and “focal adhesion” are the most enriched KEGG pathways in PDAC. Protein-protein interaction analysis identified FN1, TIMP1, and MSLN as the most highly ranked hub genes among the DEGs. Transcription factor enrichment analysis revealed that TCF7, CTNNB1, SMAD3, and JUN are significantly activated in PDAC, while SMAD7 is inhibited. The prognostic significance of the identified and validated differentially expressed genes in PDAC was evaluated via survival analysis of TCGA Pan-Cancer pancreatic ductal adenocarcinoma data. The identified candidate prognostic biomarkers were then validated in four external validation datasets (GSE21501, GSE50827, GSE57495, and GSE71729) to further improve reliability. A total of 28 up-regulated genes were found to be significantly correlated with worse overall survival in patients with PDAC. Twenty-one of the identified prognostic genes (ITGB6, LAMC2, KRT7, SERPINB5, IGF2BP3, IL1RN, MPZL2, SFTA2, MET, LAMA3, ARNTL2, SLC2A1, LAMB3, COL17A1, EPSTI1, IL1RAP, AK4, ANXA2, S100A16, KRT19, and GPRC5A) were also found to be significantly correlated with the pathological stages of the disease. The results of this study provided promising prognostic biomarkers that have the potential to differentiate PDAC from both healthy and adjacent-to-tumor pancreatic tissues. Several novel dysregulated genes merit further study as potentially promising candidates for the development of more effective treatment strategies for PDAC.

A Potent Branched-Tail Lipid Nanoparticle Enables Multiplexed mRNA Delivery and Gene Editing In Vivo

The clinical translation of messengerRNA (mRNA) drugs has been slowed by a shortage of delivery vehicles that potently and safely shuttle mRNA into target cells. Here, we describe the properties of a particularly potent branched-tail lipid nanoparticle that delivers mRNA to >80% of three major liver cell types. We characterize mRNA delivery spatially, temporally, and as a function of injection type. Following intravenous delivery, our lipid nanoparticle induced greater protein expression than two benchmark lipids, C12-200 and DLin-MC3-DMA, at an mRNA dose of 0.5 mg/kg. Lipid nanoparticles were sufficiently potent to codeliver three distinct mRNAs (firefly luciferase, mCherry, and erythropoietin) and, separately, Cas9 mRNA and single guide RNA (sgRNA) for proof-of-concept nonviral gene editing in mice. Furthermore, our branched-tail lipid nanoparticle was neither immunogenic nor toxic to the liver. Together, these results demonstrate the unique potential of this lipid material to improve the management of diseases rooted in liver dysfunction.

Machine learning analysis identifies genes differentiating triple negative breast cancers

Triple negative breast cancer (TNBC) is one of the most aggressive form of breast cancer (BC) with the highest mortality due to high rate of relapse, resistance, and lack of an effective treatment. Various molecular approaches have been used to target TNBC but with little success. Here, using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and have identified two potential genes, TBC1D9 (TBC1 domain family member 9) and MFGE8 (Milk Fat Globule-EGF Factor 8 Protein), that could successfully differentiate TNBC from non-TNBC, irrespective of their heterogeneity. TBC1D9 is under-expressed in TNBC as compared to non-TNBC patients, while MFGE8 is over-expressed. Overexpression of TBC1D9 has a better prognosis whereas overexpression of MFGE8 correlates with a poor prognosis. Protein-protein interaction analysis by affinity purification mass spectrometry (AP-MS) and proximity biotinylation (BioID) experiments identified a role for TBC1D9 in maintaining cellular integrity, whereas MFGE8 would be involved in various tumor survival processes. These promising genes could serve as biomarkers for TNBC and deserve further investigation as they have the potential to be developed as therapeutic targets for TNBC.

Genome-wide CRISPR knockout screens identify ADAMTSL3 and PTEN genes as suppressors of HCC proliferation and metastasis, respectively

PURPOSE

It is important for hepatocellular carcinoma (HCC) treatment that the targets related to its progression are identified. Clustered regularly interspaced short palindromic repeat (CRISPR)-associated nuclease 9 (Cas9)-based genetic screening is a powerful tool for identifying genes with loss-of-function mutations that are critical for tumour growth and metastasis.

METHODS

We transduced the human SMMC7721 HCC cell line expressing Cas9 with a human genome-scale CRISPR-Cas9 knockout (GeCKO) lentiviral library A (hGeCKOa) of 65,383 single-guide RNAs (sgRNAs) targeting 19,050 human genes; we then subcutaneously transplanted the transduced cells into nude mice.

RESULTS

The transduced cells were found to proliferate and metastasize faster than the untransduced cells. Through next-generation sequencing, the genes potentially related to HCC proliferation and metastasis were identified. The sgRNAs targeting the ADAMTSL3 and PTEN genes appeared twice on the list of genes related to HCC proliferation and metastasis, respectively. Analysis based on the data mining of Oncomine revealed that the ADAMTSL3 and PTEN genes were expressed at lower levels in HCC cells than they were in normal liver cells, indicating their tumour-suppressive roles. Downregulation of ADAMTSL3 and PTEN displayed poor overall survival (OS) and predicted poor relapse-free survival (RFS), further supporting their tumour-suppressive roles. Moreover, knocking out either the ADAMTSL3 or PTEN genes promoted either the proliferation or metastasis of HCC cells, respectively.

CONCLUSIONS

Using both in vitro and in vivo approaches, we described the profound role of the ADAMTSL3 and PTEN genes. This study indicates novel candidate targets for use in HCC treatment and therapy.

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

BACKGROUND

The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.

METHODS

Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.

RESULTS

Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = - 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count).

CONCLUSION

Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

Loss of ALDH1L1 folate enzyme confers a selective metabolic advantage for tumor progression

ALDH1L1 (cytosolic 10-formyltetrahydrofolate dehydrogenase) is the enzyme in folate metabolism commonly downregulated in human cancers. One of the mechanisms of the enzyme downregulation is methylation of the promoter of the ALDH1L1 gene. Recent studies underscored ALDH1L1 as a candidate tumor suppressor and potential marker of aggressive cancers. In agreement with the ALDH1L1 loss in cancer, its re-expression leads to inhibition of proliferation and to apoptosis, but also affects migration and invasion of cancer cells through a specific folate-dependent mechanism involved in invasive phenotype. A growing body of literature evaluated the prognostic value of ALDH1L1 expression for cancer disease, the regulatory role of the enzyme in cellular proliferation, and associated metabolic and signaling cellular responses. Overall, there is a strong indication that the ALDH1L1 silencing provides metabolic advantage for tumor progression at a later stage when unlimited proliferation and enhanced motility become critical processes for the tumor expansion. Whether the ALDH1L1 loss is involved in tumor initiation is still an open question.

Group 1B phospholipase A2 in metabolic and inflammatory disease modulation

The group 1B phospholipase A₂ (PLA2G1B) is a secreted phospholipase that catalyzes the hydrolytic removal of the sn-2 fatty acyl moiety from phospholipids. This enzyme is synthesized most abundantly in the pancreas and is also expressed in the lung. The first part of this review article focuses on the role of pancreatic-derived PLA2G1B in mediating lipid absorption and discusses how the PLA2G1B-derived metabolic product contributes to cardiometabolic diseases, including obesity, hyperinsulinemia, hyperlipidemia, and atherosclerosis. The anti-helminth properties of PLA2G1B will also be discussed. The second part of this review will focus on PLA2G1B expressed in the lung, and in vitro data suggest that how this enzyme may modulate lung inflammation via both hydrolytic activity-dependent and -dependent mechanisms. Finally, recent studies revealing a relationship between PLA2G1B and cancer will also be discussed. This article is part of a Special Issue entitled Novel functions of phospholipase A2 Guest Editors: Makoto Murakami and Gerard Lambeau.