Disruption of serine/threonine protein phosphatase 5 inhibits tumorigenesis of urinary bladder cancer cells

A screening method for ultra-high dimensional features with overlapped partition structures

Ultra-high dimensional data, such as gene and neuroimaging data, are becoming increasingly important in biomedical science. Identifying important biomarkers from the huge number of features can help us gain better insights into further researches. Variable screening is an efficient tool to achieve this goal under the large scale cases, which reduces the dimension of features into a moderate size by removing the major part of inactive ones. Developing novel variable screening methods for high-dimensional features with group structures is challenging, especially under the overlapped cases. For example, the huge-scaled genes usually can be partitioned into hundreds of pathways according to background knowledge. One primary characteristic for this type of data is that many genes may appear across more than one pathway, which means that different pathways are overlapped. However, existing variable screening methods only could deal with disjoint group structure cases. To fill this gap, we propose a novel variable screening method for the generalized linear model by incorporating overlapped partition structures with theoretical guarantee. Besides the sure screening property, we also test the performance of the proposed method through a series of numerical studies and apply it to statistical analysis of a breast cancer data.

Alpha-2-Heremans-Schmid-glycoprotein (AHSG) a potential biomarker associated with prognosis of chromophobe renal cell carcinoma: The PROPOLIS study

BACKGROUND AND AIMS

Chromophobe renal cell carcinoma (chRCC) is the third common pathological subtype in renal cancers. However, the underlying mechanisms of specific genetic characteristics of chRCC are currently unclear. In this study, protein expression profiles, gene ontology (GO), and survival plots were provided by integrated bioinformatics analysis to investigate key genes associated with the mechanism of tumorigenesis and prognosis of chRCC.

METHODS

The chRCC data set of gene expression profiles and clinical data were obtained from the gdc-client (https://portal.gdc.cancer.gov) deposited on The Cancer Genome Atlas (TCGA) data portal. Differentially expressed genes (DEGs) in chRCC, compared with normal samples, were analyzed by R packages "DESeq2," "edgeR," and "limma." Heat maps, volcano plots, and principal component analysis (PCA) were performed for integrated analyses. GUniGO, mutant analysis, and survival plots were performed by R packages. A protein-protein interaction (PPI) network was generated and analyzed by R packages, online String software, and Cytoscape software. Survival analysis and gene expressing comparison in tumor and normal samples were used to detect the core genes of chRCC. Furthermore, the top interacting proteins were reanalyzed.

RESULTS

A total of 306 upregulated genes and 678 downregulated genes were identified by a Venn diagram. Ten hub genes were extracted from PPI network. Furthermore, Alpha-2-Heremans-Schmid-glycoprotein (AHSG), one of 10 hub genes, was found to be associated with chRCC, and had a big difference in expression between survival and dead events. AHSG could predict potential prognostic and may be a diagnostic biomarker in chRCC.

CONCLUSION

This study illustrated that AHSG may be a potential therapeutic target and prognostic genetic marker for chRCC.

MTHFD2 is a potential oncogene for its strong association with poor prognosis and high level of immune infiltrates in urothelial carcinomas of bladder

BACKGROUND

The bifunctional methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2, methenyltetrahydrofolate cyclohydrolase (MTHFD2) has been reported to play an oncogenic role in various types of cancers. However, the function of MTHFD2 in urothelial carcinomas of bladder (UCB) and its association with tumor immune infiltration remains unknown. We aim to examine the suitability of MTHFD2 to be a novel biomarker of bladder cancer and whether MTHFD2 is linked to immune infiltration.

METHODS

RNA sequencing data and clinical information (bladder cancer samples: normal samples = 414: 19) were downloaded from The Cancer Genome Atlas official website. Western blot analysis was performed to detect MTHFD2 expression in human bladder cancer (BLCA) cells and normal urothelial cell line SV-HUC-1. Associations between MTHFD2 expression and clinicopathological features were analyzed using Mann Whitney U test or Kruskal-Wallis H test. The "survival" and "survminer" packages were utilized to plot Kaplan-Meier survival curves. Moreover, the gene set enrichment analysis (GSEA) was conducted using a clusterProfiler package. The correlation of MTHFD2 expression with immune infiltration level was estimated using the single sample GSEA (ssGSEA) algorithm. Furthermore, associations between MTHFD2 and immune checkpoint genes were evaluated using the correlation analysis.

RESULTS

Transcriptome analysis manifested that MTHFD2 was highly expressed in UCB tissues than normal bladder tissues, which was further confirmed by western blot analysis in human BLCA cells and SV-HUC-1 cells. Moreover, MTHFD2 high expression was significantly associated with the advanced disease progression. Also, the high expression of MTHFD2 was correlated with poor prognosis, and MTHFD2 was considered as an independent prognostic factor for disease specific survival. Furthermore, a number of cancer-related pathways were enriched in MTHFD2 high group, including NF-κB activation, JAK/STAT, and cancer immunotherapy by PD1 blockade. Several immune checkpoint molecules were also strongly associated with MTHFD2 expression, including PDCD1, CD274, CTLA4, CD276, LAG3, HAVCR2, and TIGIT.

CONCLUSIONS

MTHFD2 expression was remarkably elevated in UCB, suggesting that MTHFD2 could be a promising biomarker for BLCA as well as novel target for anti-cancer immunotherapy since its close association with immune infiltration.

Functional analysis of a novel de novo variant in PPP5C associated with microcephaly, seizures, and developmental delay

We describe a proband evaluated through the Undiagnosed Diseases Network (UDN) who presented with microcephaly, developmental delay, and refractory epilepsy with a de novo p.Ala47Thr missense variant in the protein phosphatase gene, PPP5C. This gene has not previously been associated with a Mendelian disease, and based on the population database, gnomAD, the gene has a low tolerance for loss-of-function variants (pLI = 1, o/e = 0.07). We functionally evaluated the PPP5C variant in C. elegans by knocking the variant into the orthologous gene, pph-5, at the corresponding residue, Ala48Thr. We employed assays in three different biological processes where pph-5 was known to function through opposing the activity of genes, mec-15 and sep-1. We demonstrated that, in contrast to control animals, the pph-5 Ala48Thr variant suppresses the neurite growth phenotype and the GABA signaling defects of mec-15 mutants, and the embryonic lethality of sep-1 mutants. The Ala48Thr variant did not display dominance and behaved similarly to the reference pph-5 null, indicating that the variant is likely a strong hypomorph or complete loss-of-function. We conclude that pph-5 Ala48Thr is damaging in C. elegans. By extension in the proband, PPP5C p.Ala47Thr is likely damaging, the de novo dominant presentation is consistent with haplo-insufficiency, and the PPP5C variant is likely responsible for one or more of the proband's phenotypes.

The role of phosphoprotein phosphatases catalytic subunit genes in pancreatic cancer

Compelling evidence suggests that phosphoprotein phosphatases (PPPs) are involved in a large spectrum of physiological and pathological processes, but little is known about their roles in pancreatic cancer. We investigated the expression level, prognostic value, and potential function of PPPs with data from Oncomine, GEPIA, THPA, and TCGA databases and an independent cohort of patients with pancreatic cancer. Among all the PPP catalytic subunits (PPPcs), the transcription levels of PPP1CA, PPP1CB, PPP3CA, PPP3CB, and PPP4C were higher in pancreatic cancer than in normal pancreas (P<0.01, fold change > 2). Kaplan–Meier analysis showed that high transcription levels of PPP1CA, PPP1CB, PPP2CA, PPP2CB, PPP3CA, and PPP4C correlated with poorer survival. In contrast, patients with high levels of PPP3CB, PPP3CC, PPP5C, PPP6C, and PPEF2 had much better prognoses. Data from THPA and patients with pancreatic cancer enrolled in our hospital also confirmed the prognostic value of PPP1CA, PPP1CB, PPP2CA, PPP2CB, PPP3CA, PPP3CB, and PPP6C at the protein level. In addition, the Pearson Chi-square test showed that PPP3CB level was significantly correlated with T and N stages. GO and KEGG analyses showed that the genes and pathways related to the pathogenesis and progression of pancreatic cancer were greatly affected by alterations in PPPcs. Results of the present study suggest that PPP1CA, PPP1CB, PPP2CA, PPP2CB, and PPP3CA have deleterious effects but PPP3CB, PPP5C, and PPP6C have beneficial effects on pancreatic cancer.

Biomarker discovery by feature ranking: Evaluation on a case study of embryonal tumors

The task of biomarker discovery is best translated to the machine learning task of feature ranking. Namely, the goal of biomarker discovery is to identify a set of potentially viable targets for addressing a given biological status. This is aligned with the definition of feature ranking and its goal - to produce a list of features ordered by their importance for the target concept. This differs from the task of feature selection (typically used for biomarker discovery) in that it catches viable biomarkers that have redundant or overlapping information with often highly important biomarkers, while with feature selection this is not the case. We propose to use a methodology for evaluating feature rankings to assess the quality of a given feature ranking and to discover the best cut-off point. We demonstrate the effectiveness of the proposed methodology on 10 datasets containing data about embryonal tumors. We evaluate two most commonly used feature ranking algorithms (Random forests and RReliefF) and using the evaluation methodology identifies a set of viable biomarkers that have been confirmed to be related to cancer.

Molecular characterization of serine/threonine protein phosphatase of Eimeria tenella

Avian coccidiosis is a widespread and economically significant poultry disease caused by several Eimeria species, including Eimeria tenella. Previously, E. tenella serine/threonine protein phosphatase (EtSTP) was found to be differentially expressed in drug-sensitive (DS) and drug-resistant strains using RNA-seq. In the present study, we found that transcription and translation levels of EtSTP were higher in diclazuril-resistant (DZR) strains and maduramicin-resistant (MRR) strains than in DS strains using quantitative real-time PCR (qPCR) and Western blotting. Enzyme activity results indicated that the catalytic activity of EtSTP was higher in the two drug-resistant strains than in DS strains. Western blot and qPCR analysis also showed that expression levels of EtSTP were higher in unsporulated oocysts (UO) and second-generation merozoites (SM). Indirect immunofluorescence localization showed that EtSTP was located in most areas of the parasite with the exception of refractile bodies, and fluorescence intensity was enhanced during development. In vitro inhibition experiments showed that the ability of sporozoites (SZ) to invade cells was significantly decreased after treatment with anti-rEtSTP antibody. These results indicated that EtSTP acted mainly during the developmental and reproductive stages of the parasite and may be related to the resistance of coccidia to external drug pressure.

Downregulation of HDGF inhibits the tumorigenesis of bladder cancer cells by inactivating the PI3K-AKT signaling pathway

Background

Hepatoma-derived growth factor (HDGF) is a heparin-binding protein that has been observed to be abnormally expressed in numerous malignancies, but the definite role of HDGF in bladder cancer (BCa) has not been clarified. Here, we conduct the present study to evaluate correlations between HDGF and BCa.

Methods

Bioinformatics analysis was used to evaluate HDGF expression levels in BCa tissues. The effect of HDGF on cell proliferation, migration, invasion, cell cycle and apoptosis was analyzed utilizing CCK-8, clone formation, Transwell assays and flow cytometry, respectively. In addition, the xenograft tumor model was established.

Results

Based on bioinformatics analysis, we noticed that HDGF was highly expressed in BCa tissues and was positively correlated with poor prognosis in patients. Knockdown of HDGF markedly reduced tumorigenesis in BCa cells. Furthermore, the results of flow cytometry showed that HDGF deletion enhanced apoptosis in T24 and 253J cells and led to cell cycle arrest in G1 phase. In further studies, we found that tumor growth was inhibited in xenograft nude mouse models with HDGF deletion. The results of RNA-seq analysis revealed that the PI3K-AKT signaling pathway-related genes were obviously changed in HDGF-deficient 253J cells, and this result was further confirmed by Western blot analysis.

Conclusion

In summary, we suggest that HDGF plays a substantial role in BCa and promotes tumor development and progression by regulating the PI3K-AKT signaling pathway, which provides a promising target for BCa treatment.