Identification of differentially expressed genes and biological pathways in bladder cancer

Exploiting protein family and protein network data to identify novel drug targets for bladder cancer

Bladder cancer remains one of the most common forms of cancer and yet there are limited small molecule targeted therapies. Here, we present a computational platform to identify new potential targets for bladder cancer therapy. Our method initially exploited a set of known driver genes for bladder cancer combined with predicted bladder cancer genes from mutationally enriched protein domain families. We enriched this initial set of genes using protein network data to identify a comprehensive set of 323 putative bladder cancer targets. Pathway and cancer hallmarks analyses highlighted putative mechanisms in agreement with those previously reported for this cancer and revealed protein network modules highly enriched in potential drivers likely to be good targets for targeted therapies. 21 of our potential drug targets are targeted by FDA approved drugs for other diseases — some of them are known drivers or are already being targeted for bladder cancer (FGFR3, ERBB3, HDAC3, EGFR). A further 4 potential drug targets were identified by inheriting drug mappings across our in-house CATH domain functional families (FunFams). Our FunFam data also allowed us to identify drug targets in families that are less prone to side effects i.e., where structurally similar protein domain relatives are less dispersed across the human protein network. We provide information on our novel potential cancer driver genes, together with information on pathways, network modules and hallmarks associated with the predicted and known bladder cancer drivers and we highlight those drivers we predict to be likely drug targets.

Accompaniment of Time-Lapse Parameters and Cumulus Cell RNA-Sequencing in Embryo Evaluation

The aim of this study was to investigate the use of time-lapse morphokinetic parameters and cumulus cells transcriptomic profile to achieve a more accurate and non-invasive method in embryo evaluation. Two hundred embryos from 20 couples were evaluated based on morphokinetic characteristics using time-lapse. Embryos were divided into the high-quality, moderate-quality, and bad-quality groups. Non-fertilized oocytes were considered as the fourth group. T5 (time to five cells), S2 (time from three to four cells), and CC2 (time from two to three cells) were recorded. Also, the cumulus cells of the respective oocytes were divided into high-quality, moderate-quality, bad-quality, and non-fertilized groups based on the grading of the embryos. Then their transcriptomic profiles were analyzed by RNA-sequencing. Finally, the correlation between differentially expressed genes and embryo time-lapse parameters was investigated. T5 was the only timing that showed a statistically significant difference between high-quality group and other groups. RNA-sequencing results showed that 37 genes were downregulated and 106 genes were upregulated in moderate, bad-quality, and non-fertilized groups compared to high-quality group (q value < 0.05). These genes were involved in the main biological processes such as cell cycle, DNA repair, cell signaling and communication, transcription, and cell metabolism. Embryos graded in different groups showed different transcriptomic profiles in the related cumulus cells. Therefore, it seems that embryo selection using the combination of cytokinetics and cumulus cells gene expression can improve the accuracy of the embryo selection and pregnancy rate in ART clinics.

A two-way rectification method for identifying differentially expressed genes by maximizing the co-function relationship

BACKGROUND

The identification of differentially expressed genes (DEGs) is an important task in many biological studies. The currently widely used methods often calculate a score for each gene by estimating the significance level in terms of the differential expression. However, biological experiments often have only three duplications, plus plenty of noises contain in gene expression datasets, which brings a great challenge to statistical analysis methods. Moreover, the abundance of gene expression levels are not evenly distributed. Thus, those low expressed genes are more easily to be detected by fold-change based methods, which may results in high false positives among the DEG list. Since phenotypical changes result from DEGs should be strongly related to several distinct cellular functions, a more robust method should be designed to increase the true positive rate of the functional related DEGs.

RESULTS

In this study, we propose a two-way rectification method for identifying DEGs by maximizing the co-function relationships between genes and their enriched cellular pathways. An iteration strategy is employed to sequentially narrow down the group of identified DEGs and their associated biological functions. Functional analyses reveal that the identified DEGs are well organized in the form of functional modules, and the enriched pathways are very significant with lower p-value and larger gene count.

CONCLUSIONS

An integrative rectification method was proposed to identify key DEGs and their related functions simultaneously. The experimental validations demonstrate that the method has high interpretability and feasibility. It performs very well in terms of the identification of remarkable functional related genes.

LPAR1, Correlated With Immune Infiltrates, Is a Potential Prognostic Biomarker in Prostate Cancer

Prostate cancer is a common malignancy in men worldwide. Lysophosphatidic acid receptor 1 (LPAR1) is a critical gene and it mediates diverse biologic functions in tumor. However, the correlation between LPAR1 and prognosis in prostate cancer, as well as the potential mechanism, remains unclear. In the present study, LPAR1 expression analysis was based on The Cancer Genome Atlas (TCGA) and the Oncomine database. The correlation of LPAR1 on prognosis was also analyzed based on R studio. The association between LPAR1 and tumor-infiltrating immune cells were evaluated in the Tumor Immune Estimation Resource site, ssGSEA, and MCPcounter packages in R studio. Gene Set Enrichment Analysis and Gene Ontology analysis were used to analyze the function of LPAR1. TCGA datasets and the Oncomine database revealed that LPAR1 was significantly downregulated in prostate cancer. High LPAR1 expression was correlated with favorable overall survival. LPAR1 was involved in the activation, proliferation, differentiation, and migration of immune cells, and its expression was positively correlated with immune infiltrates, including CD4+ T cells, B cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, and natural killer cells. Moreover, LPAR1 expression was positively correlated with those chemokine/chemokine receptors, indicating that LPAR1 may regulate the migration of immune cells. In summary, LPAR1 is a potential prognostic biomarker and plays an important part in immune infiltrates in prostate cancer.

Promising diagnostic and prognostic value of six genes in human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Ample data have been reported to unravel the carcinogenesis over the past decades. Although pinpointing the cause of the HCC is challenging, this in and of itself may not be an insuperable problem. Indeed, the emergence of novel molecular targets has given rise to targeted therapy for HCC. Compared to traditional treatments, drugs with molecularly targeted agents are considered an optimal way to treat HCC. However, targeted approaches are currently limited among HCC patients. In our work, we explored more potential genes for targeted treatment of HCC. Initially, differentially expressed genes (DEGs) were identified in gene expression profiling interactive analysis (GEPIA) and NetworkAnalyst. Subsequently, 10 key genes were selected through enrichment analysis and PPI network construction. Based on the GEPIA and Oncomine databases, six upregulated genes were selected. High protein expression of these six genes were confirmed through the Human Protein Atlas database. In addition, these six genes were associated with unfavorable overall survival and progression-free survival based on Kaplan-Meier plotter bioinformatics. Moreover, gene expression was closely related to the tumor stages and pathological grades, as determined with UALCAN. More importantly, PTTG1, UBE2C, and ZWINT were identified as potential targets of anti-cancer drugs using cBioPortal. qPCR and western blot assays were used to show the high expression levels of the latter three genes in HCC cell lines. Collectively, these findings are expected to provide a theoretical basis for and give novel insights into clinical research of HCC.

Upregulation of ASPM, BUB1B and SPDL1 in tumor tissues predicts poor survival in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a major cause of cancer-associated mortality, with poor patient outcome. The present study aimed to identify key candidate genes and investigate the potential molecular mechanisms associated with the progression of PDAC. The GSE46234 dataset was downloaded from the Gene Expression Omnibus database, in order to identify the upregulated differentially expressed genes (DEGs) in PDAC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the biological functions and pathways of the upregulated DEGs, and a protein-protein interaction (PPI) network was subsequently constructed to screen the hub genes. Subsequently, survival analyses of the hub genes were undertaken in patients with PDAC, using The Cancer Genome Atlas dataset. Reverse transcription-quantitative (RT-q)PCR analysis was performed to assess the mRNA expression levels of the hub genes associated with the prognosis of patients with PDAC. In the present study, 65 upregulated DEGs were identified. GO analysis suggested that the DEGs were enriched in response to hypoxia, calcium ion and negative regulation of catecholamine. KEGG analysis demonstrated that the DEGs were enriched in gastric acid secretion, the ECM-receptor interaction and the cGMP-PKG signaling pathway. Among the 18 hub genes determined by module screening of the PPI network, upregulation of three key genes, abnormal spindle-like microcephaly-associated protein (ASPM), mitotic checkpoint serine/threonine-protein kinase BUB1 β (BUB1B) and protein spindly (SPDL1), was significantly associated with worse overall survival and disease-free survival time in patients with PDAC. Furthermore, ASPM, BUB1B and SPDL1 were demonstrated to be associated with advanced tumor stage, and their upregulation in PDAC tumor tissues was validated using RT-qPCR analysis. Taken together, the results of the present study demonstrate that ASPM, BUB1B and SPDL1 may have the potential to function as prognostic markers and therapeutic targets for PDAC.

Down-expression of GOLM1 enhances the chemo-sensitivity of cervical cancer to methotrexate through modulation of the MMP13/EMT axis

The highly refractory nature of cervical cancer to chemotherapeutic drugs and its epithelial-to-mesenchymal transition (EMT) are the key reasons contributing to the poor prognosis of this disease. Golgi Membrane Protein 1 (GOLM1), a protein involved in the trafficking of proteins through the Golgi apparatus, has been shown to be oncogenic in a variety of human cancers. Herein, we found GOLM1 was markedly up-regulated in cervical cancer and GOLM1 down-expression enhanced the anti-tumor effect of methotrexate. By performing mechanistic studies using both in vitro and in vivo models, we found that GOLM1 could target matrix metallopeptidase 13 (MMP13), a member of the MMPs, and regulate the EMT process. Moreover, altered EMT progression compromised the chemotherapy-enhancing effects of GOLM1 knock-down. Finally, we found significantly higher levels of GOLM1 and MMP13 in cervical cancer tissues compared with adjacent noncancerous tissues, and this was also associated with poor cervical cancer patients' prognosis. Taken together, our results suggest that the GOLM1/MMP13/EMT axis is an important factor involved in regulating methotrexate in cervical cancer, and highlights the potential of novel GOLM1-based clinical modalities as a therapeutic approach in cervical cancer patients.