Abnormal expression of ATP1A1 and ATP1A2 in breast cancer

Hypomethylation of ATP1A1 Is Associated with Poor Prognosis and Cancer Progression in Triple-Negative Breast Cancer

Dysregulated DNA methylation in cancer is critical in the transcription machinery associated with cancer progression. Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, but no treatment targeting TNBC biomarkers has yet been developed. To identify specific DNA methylation patterns in TNBC, methyl-binding domain protein 2 (MBD) sequencing data were compared in TNBC and the three other major breast cancer subtypes. Integrated analysis of DNA methylation and gene expression identified a gene set showing a correlation between DNA methylation and gene expression. ATPase Na+/K+-transporting subunit alpha 1 (ATP1A1) was found to be specifically hypomethylated in the coding sequence (CDS) region and to show increased expression in TNBC. The Cancer Genome Atlas (TCGA) database also showed that hypomethylation and high expression of ATP1A1 were strongly associated with poor survival in patients with TNBC. Furthermore, ATP1A1 knockdown significantly reduced the viability and tumor-sphere formation of TNBC cells. These results suggest that the hypomethylation and overexpression of ATP1A1 could be a prognostic marker in TNBC and that the manipulation of ATP1A1 expression could be a therapeutic target in this disease.

ATP1A1 is a promising new target for melanoma treatment and can be inhibited by its physiological ligand bufalin to restore targeted therapy efficacy

Despite advancements in treating metastatic melanoma, many patients exhibit resistance to targeted therapies. Our study focuses on ATP1A1, a sodium pump subunit associated with cancer development. We aimed to assess ATP1A1 prognostic value in melanoma patients and examine the impact of its ligand, bufalin, on melanoma cell lines in vitro and in vivo. High ATP1A1 expression (IHC) correlated with reduced overall survival in melanoma patients. Resistance to BRAF inhibitor was linked to elevated ATP1A1 levels in patient biopsies (IHC, qPCR) and cell lines (Western blot, qPCR). Additionally, high ATP1A1 mRNA expression positively correlated with differentiation/pigmentation markers based on data from The Cancer Genome Atlas (TCGA) databases and Verfaillie proliferative gene signature analysis. Bufalin specifically targeted ATP1A1 in caveolae, (proximity ligation assay) and influenced Src phosphorylation (Western blot), thereby disrupting multiple signaling pathways (phosphokinase array). In vitro, bufalin induced apoptosis in melanoma cell lines by acting on ATP1A1 (siRNA experiments) and, in vivo, significantly impeded melanoma growth using a nude mouse xenograft model with continuous bufalin delivery via an osmotic pump. In conclusion, our study demonstrates that ATP1A1 could serve as a prognostic marker for patient survival and a predictive marker for response to BRAF inhibitor therapy. By targeting ATP1A1, bufalin inhibited cell proliferation, induced apoptosis in vitro, and effectively suppressed tumor development in mice. Thus, our findings strongly support ATP1A1 as a promising therapeutic target, with bufalin as a potential agent to disrupt its tumor-promoting activity.

Non-B DNA structures as a booster of genome instability

Non-canonical secondary structures (NCSs) are alternative nucleic acid structures that differ from the canonical B-DNA conformation. NCSs often occur in repetitive DNA sequences and can adopt different conformations depending on the sequence. The majority of these structures form in the context of physiological processes, such as transcription-associated R-loops, G4s, as well as hairpins and slipped-strand DNA, whose formation can be dependent on DNA replication. It is therefore not surprising that NCSs play important roles in the regulation of key biological processes. In the last years, increasing published data have supported their biological role thanks to genome-wide studies and the development of bioinformatic prediction tools. Data have also highlighted the pathological role of these secondary structures. Indeed, the alteration or stabilization of NCSs can cause the impairment of transcription and DNA replication, modification in chromatin structure and DNA damage. These events lead to a wide range of recombination events, deletions, mutations and chromosomal aberrations, well-known hallmarks of genome instability which are strongly associated with human diseases. In this review, we summarize molecular processes through which NCSs trigger genome instability, with a focus on G-quadruplex, i-motif, R-loop, Z-DNA, hairpin, cruciform and multi-stranded structures known as triplexes.

Identification of Novel Prognostic Biomarkers That are Associated with Immune Microenvironment Based on GABA-Related Molecular Subtypes in Gastric Cancer

Background

Gamma-aminobutyric acid (GABA) plays an important role in tumorigenesis and progression. Despite this, the role of Reactome GABA receptor activation (RGRA) on gastric cancer (GC) remains unclear. This study was intended to screen RGRA-related genes in GC and investigate their prognostic value.

Methods

GSVA algorithm was used to assess the score of RGRA. GC patients were divided into two subtypes based on the median score of RGRA. GSEA, functional enrichment analysis, and immune infiltration analysis were performed between the two subgroups. Then, differentially expressed analysis, and weighted gene co-expression network analysis (WGCNA) were used to identify RGRA-related genes. The prognosis and expression of core genes were analyzed and validated in the TCGA database, GEO database, and clinical samples. ssGSEA and ESTIMATE algorithms were used to assess the immune cell infiltration in the low- and high-core genes subgroups.

Results

High-RGRA subtype had a poor prognosis and activated immune-related pathways, as well as an activated immune microenvironment. ATP1A2 was identified to be the core gene. The expression of ATP1A2 was associated with the overall survival rate and tumor stage, and its expression was down-regulated in GC patients. Furthermore, ATP1A2 expression was positively correlated with the level of immune cells, including B cells, CD8 T cells, cytotoxic cells, DC, eosinophils, macrophages, mast cells, NK cells, and T cells.

Conclusion

Two RGRA-related molecular subtypes were identified that could predict the outcome in GC patients. ATP1A2 was a core immunoregulatory gene and was associated with prognosis and immune cell infiltration in GC.

BRPCA: Bounded Robust Principal Component Analysis to Incorporate Similarity Network for N7-Methylguanosine(m7G) Site-Disease Association Prediction

Recent studies have revealed that N7-methylguanosine(m⁷G) plays a pivotal role in various biological processes and disease pathogenesis. To date, transcriptome-wide m⁷G modification sites have been identified by high-throughput sequencing approaches, and some related information has been recorded in a few biological databases. However, the mechanism of site action in disease remains uncharted. Wet experiments can help identify true m⁷G sites with high confidence, but it is time-consuming to find the true ones in such a large number of sites, which will also cost too much. Thus, computational methods are emergently needed to predict the associations between m⁷G sites and various diseases, thus help to uncover potential active sites for specific diseases. In this article, we proposed a bounded robust principal component analysis (BRPCA) method to predict unknown m⁷G-disease association based on similarity information. Importantly, BRPCA tolerates the noise and redundancy existing in association and similarity information. Moreover, a suitable bounded constraint is incorporated into BRPCA to ensure that the predicted association scores locate in a meaningful interval. The extensive experiments demonstrate the superiority and robustness of the BRPCA.

Homophilic ATP1A1 binding induces activin A secretion to promote EMT of tumor cells and myofibroblast activation

Tumor cells with diverse phenotypes and biological behaviors are influenced by stromal cells through secretory factors or direct cell-cell contact. Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive desmoplasia with fibroblasts as the major cell type. In the present study, we observe enrichment of myofibroblasts in a juxta-tumoral position with tumor cells undergoing epithelial-mesenchymal transition (EMT) that facilitates invasion and correlates with a worse clinical prognosis in PDAC patients. Direct cell-cell contacts forming heterocellular aggregates between fibroblasts and tumor cells are detected in primary pancreatic tumors and circulating tumor microemboli (CTM). Mechanistically, ATP1A1 overexpressed in tumor cells binds to and reorganizes ATP1A1 of fibroblasts that induces calcium oscillations, NF-κB activation, and activin A secretion. Silencing ATP1A1 expression or neutralizing activin A secretion suppress tumor invasion and colonization. Taken together, these results elucidate the direct interplay between tumor cells and bound fibroblasts in PDAC progression, thereby providing potential therapeutic opportunities for inhibiting metastasis by interfering with these cell-cell interactions.

Identification of Potential Biomarkers for Progression and Prognosis of Bladder Cancer by Comprehensive Bioinformatics Analysis

Background. Bladder cancer (BLCA) is a highly malignant tumor that develops in the urinary system. Identification of biomarkers in progression and prognosis is crucial for the treatment of BLCA. BLCA-related differentially expressed genes (DEGs) were authenticated by screening the DEGs and weighted gene coexpression network analysis (WGCNA). LASSO and SVM-RFE algorithms were utilized to screen the feature genes in BLCA. Survival analysis was performed using the Kaplan–Meier curve provided by the ‘survival' R package. The BLCA samples were clustered by hclust based on the immune score matrix calculated by the single-sample GSEA (ssGSEA) algorithm. The immune, stromal, and ESTIMATE scores of each BLCA patient were calculated by applying the ESTIMATE algorithm. ssGSEA was conducted to explore the function of characteristic genes in BLCA. The expression of characteristic genes in clinical cancer tissue, and the pericancerous tissue of BLCA patients was verified using qRT-PCR assays. A total of 189 BLCA-related DEGs were identified. Fourteen feature genes were defined by LASSO and SVM-RFE algorithms. Five characteristic genes, including SMYD2, GAPDHP1, ATP1A2, CILP, and THSD4, were related to the OS of BLCA. The correlation analysis of five characteristic genes and clinicopathological factors showed that five genes played a role in the progression of BLCA. Additionally, the expression of five characteristic genes in clinical cancer tissues and pericarcinomatous tissues from BLCA patients was verified by qRT-PCR, which was consistent with the result from the public database. Finally, we discovered five prognostic genes linked to BLCA progression, which might serve as a theoretical basis for prognosis and treatment targets for BLCA patients.

Identification of ITPR1 as a Hub Gene of Group 3 Medulloblastoma and Coregulated Genes with Potential Prognostic Values

The Group 3 Medulloblastoma (Grp3-MB) is an aggressive molecular subtype with a high incidence of metastasis and deaths. In this study, were used an RNA sequencing data (RNA-Seq) from a Brazilian cohort of MBs to identify hub genes associated with the metastatic risk. Data validation were performed by using multiple large datasets from MBs (GSE85217, GSE37418, and EGAS00001001953). DESeq2 package in R software was used to identify the differentially expressed genes (DEGs) in our RNA-Seq data. The DEGs data were accessed to construct the modules/graphs of co-expression and to identify hub genes through Cytoscape platform. The coregulated genes were enriched by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the protein-protein interaction (PPI) network was visualized by Cytoscape. The Kaplan-Meier plotter and ROC curves were used to validate the diagnostic and prognostic values of specific biomarkers identified through this model. We identified that inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) as a downregulated hub gene, with a high diagnostic accuracy to Grp3-MBs and associated with tumor metastasis. In addition, we identified genes significantly correlated with ITPR1 that were associated with metastasis in Grp3-MB (ATP1A2, MTTL7A, and RGL1) and worst overall survival in MBs (ANTXR1 and RGL1). Our findings suggest that the ITPR1 hub gene is potentially involved in the metastatic process for Grp3-MB. Our data also provide evidence of targets that may serve as prognostic predictors and/or regulators for the metastatic process that maybe explored for further research of individualized therapy to Grp3-MBs.

Clinical significance of P-class pumps in cancer

P-class pumps are specific ion transporters involved in maintaining intracellular/extracellular ion homeostasis, gene transcription, and cell proliferation and migration in all eukaryotic cells. The present review aimed to evaluate the role of P-type pumps [Na⁺/K⁺ ATPase (NKA), H⁺/K⁺ ATPase (HKA) and Ca2⁺-ATPase] in cancer cells across three fronts, namely structure, function and genetic expression. It has been shown that administration of specific P-class pumps inhibitors can have different effects by: i) Altering pump function; ii) inhibiting cell proliferation; iii) inducing apoptosis; iv) modifying metabolic pathways; and v) induce sensitivity to chemotherapy and lead to antitumor effects. For example, the NKA β2 subunit can be downregulated by gemcitabine, resulting in increased apoptosis of cancer cells. The sarcoendoplasmic reticulum calcium ATPase can be inhibited by thapsigargin resulting in decreased prostate tumor volume, whereas the HKA α subunit can be affected by proton pump inhibitors in gastric cancer cell lines, inducing apoptosis. In conclusion, the present review highlighted the central role of P-class pumps and their possible use and role as anticancer cellular targets for novel therapeutic chemical agents.

IMPAD1 and KDELR2 drive invasion and metastasis by enhancing Golgi-mediated secretion

Non-small cell lung cancer (NSCLC) is the deadliest form of cancer worldwide, due in part to its proclivity to metastasize. Identifying novel drivers of invasion and metastasis holds therapeutic potential for the disease. We conducted a gain-of-function invasion screen, which identified two separate hits, IMPAD1 and KDELR2, as robust, independent drivers of lung cancer invasion and metastasis. Given that IMPAD1 and KDELR2 are known to be localized to the ER-Golgi pathway, we studied their common mechanism of driving in vitro invasion and in vivo metastasis and demonstrated that they enhance Golgi-mediated function and secretion. Therapeutically inhibiting matrix metalloproteases (MMPs) suppressed both IMPAD1- and KDELR2-mediated invasion. The hits from this unbiased screen and the mechanistic validation highlight Golgi function as one of the key cellular features altered during invasion and metastasis.

RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer that currently lacks effective biomarkers and therapeutic targets required to investigate the diagnosis and treatment of TNBC. Here we performed a comprehensive differential analysis of 165 TNBC samples by integrating RNA-seq data of breast tumor tissues and adjacent normal tissues from both our cohort and The Cancer Genome Atlas (TCGA). Pathway enrichment analysis was conducted to evaluate the biological function of TNBC-specific expressed genes. Further multivariate Cox proportional hazard regression was performed to evaluate the effect of these genes on TNBC prognosis. In this report, we identified a total of 148 TNBC-specific expressed genes that were primarily enriched in mammary gland morphogenesis and hormone levels related pathways, suggesting that mammary gland morphogenesis might play a unique role in TNBC patients differing from other breast cancer types. Further survival analysis revealed that nine genes ( FSIP1, ADCY5, FSD1, HMSD, CMTM5, AFF3, CYP2A7, ATP1A2, and C11orf86) were significantly associated with the prognosis of TNBC patients, while three of them ( ADCY5, CYP2A7, and ATP1A2) were involved in the hormone-related pathways. These findings indicated the vital role of the hormone-related genes in TNBC tumorigenesis and may provide some independent prognostic markers as well as novel therapeutic targets for TNBC.

DNLC: differential network local consistency analysis

BACKGROUND

The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages.

RESULTS

In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible.

CONCLUSIONS

The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug–gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug–gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug–gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast–human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

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.

Identification of a sodium pump Na+/K+ ATPase α1-targeted peptide for PET imaging of breast cancer

The sodium pump Na⁺/K⁺ ATPase a1 subunit(NKA a1), an attractive cancer-related biomarker and therapeutic target, is closely related to the development and progression of several cancers including breast cancer. Currently, a NKA a1 inhibitor, UNBS1450, has already evidenced its great therapeutic potential in personalized cancer treatment. The ability of non-invasive imaging of NKA a1 expression would be useful for selecting cancer patients who may benefit from this drug. Here, we identified an S3 peptide that is specifically homed to breast cancer by phage display. All data of in vitro and in vivo experiments suggested the excellent targeting character of the S3 peptide. As the binding activity of the S3 phage was positively correlated to the level of NKA α1 expression in various breast cancer cells, NKA α1 was validated as the primary target of the S3 peptide. Based on immunohistochemistry staining result of 107 breast cancer patients, NKA α1 was verified to be a novel tracking marker and a prognostic predictor for breast cancer. Importantly, we proposed and validated an S3 peptide-based radiotracer ¹⁸F-ALF-NOTA-S3 for PET (Positron Emission Tomography) imaging of breast cancer and other NKA α1-overexpressing cancers, including hepatocellular carcinoma and non-small cell lung cancer, in mouse models. Our findings demonstrated the potential application of ¹⁸F-ALF-NOTA-S3 for visualization of NKA α1-positive lesions, which provide a new approach to character tumor phenotypic imaging.

Discovery of Tamoxifen and N-Desmethyl Tamoxifen Protein Targets in MCF-7 Cells Using Large-Scale Protein Folding and Stability Measurements

The proteins in an MCF-7 cell line were probed for tamoxifen (TAM) and n-desmethyl tamoxifen (NDT) induced stability changes using the Stability of Proteins from Rates of Oxidation (SPROX) technique in combination with two different quantitative proteomics strategies, including one based on SILAC and one based on isobaric mass tags. Over 1000 proteins were assayed for TAM- and NDT-induced protein stability changes, and a total of 163 and 200 protein hits were identified in the TAM and NDT studies, respectively. A subset of 27 high-confidence protein hits were reproducibly identified with both proteomics strategies and/or with multiple peptide probes. One-third of the high-confidence hits have previously established experimental links to the estrogen receptor, and nearly all of the high-confidence hits have established links to breast cancer. One high-confidence protein hit that has known estrogen receptor binding properties, Y-box binding protein 1 (YBX1), was further validated as a direct binding target of TAM using both the SPROX and pulse proteolysis techniques. Proteins with TAM- and/or NDT-induced expression level changes were also identified in the SILAC-SPROX experiments. These proteins with expression level changes included only a small fraction of those with TAM- and/or NDT-induced stability changes.