ZNF385B and VEGFA are strongly differentially expressed in serous ovarian carcinomas and correlate with survival

Discovery of differentially expressed proteins for CAR-T therapy of ovarian cancers with a bioinformatics analysis

Target antigens are crucial for developing chimeric antigen receptor (CAR)-T cells, but their application to ovarian cancers is limited. This study aimed to identify potential genes as CAR-T-cell antigen candidates for ovarian cancers. A differential gene expression analysis was performed on ovarian cancer samples from four datasets obtained from the GEO datasets. Functional annotation, pathway analysis, protein localization, and gene expression analysis were conducted using various datasets and tools. An oncogenicity analysis and network analysis were also performed. In total, 153 differentially expressed genes were identified in ovarian cancer samples, with 60 differentially expressed genes expressing plasma membrane proteins suitable for CAR-T-cell antigens. Among them, 21 plasma membrane proteins were predicted to be oncogenes in ovarian cancers, with nine proteins playing crucial roles in the network. Key genes identified in the oncogenic pathways of ovarian cancers included MUC1, CXCR4, EPCAM, RACGAP1, UBE2C, PRAME, SORT1, JUP, and CLDN3, suggesting them as recommended antigens for CAR-T-cell therapy for ovarian cancers. This study sheds light on potential targets for immunotherapy in ovarian cancers.

Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer

Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

PTEN deficiency exposes a requirement for an ARF GTPase module for integrin-dependent invasion in ovarian cancer

Dysregulation of the PI3K/AKT pathway is a common occurrence in high-grade serous ovarian carcinoma (HGSOC), with the loss of the tumour suppressor PTEN in HGSOC being associated with poor prognosis. The cellular mechanisms of how PTEN loss contributes to HGSOC are largely unknown. We here utilise time-lapse imaging of HGSOC spheroids coupled to a machine learning approach to classify the phenotype of PTEN loss. PTEN deficiency induces PI(3,4,5)P3 -rich and -dependent membrane protrusions into the extracellular matrix (ECM), resulting in a collective invasion phenotype. We identify the small GTPase ARF6 as a crucial vulnerability of HGSOC cells upon PTEN loss. Through a functional proteomic CRISPR screen of ARF6 interactors, we identify the ARF GTPase-activating protein (GAP) AGAP1 and the ECM receptor β1-integrin (ITGB1) as key ARF6 interactors in HGSOC regulating PTEN loss-associated invasion. ARF6 functions to promote invasion by controlling the recycling of internalised, active β1-integrin to maintain invasive activity into the ECM. The expression of the CYTH2-ARF6-AGAP1 complex in HGSOC patients is inversely associated with outcome, allowing the identification of patient groups with improved versus poor outcome. ARF6 may represent a therapeutic vulnerability in PTEN-depleted HGSOC.

The myo-inositol biosynthesis rate-limiting enzyme ISYNA1 suppresses the stemness of ovarian cancer via Notch1 pathway

Cancer stem cells (CSCs) play a central role in ovarian cancer (OC), understanding regulatory mechanisms governing their stemness is critical. Here, we report ISYNA1, the rate-limiting enzyme in myo-inositol biosynthesis, as a suppressor of OC regulating cancer stemness. We identified ISYNA1 as a differentially expressed gene in normal ovary and ovarian cancer tissues, as well as OC cells and OCSCs. Low ISYNA1 expression correlated with poor prognosis in OC patients. In addition, ISYNA1 was negatively correlated with CSC markers, and ISYNA1-related pathways were enriched in Wnt, Notch, and other critical cancer pathways. ISYNA1 deficiency promoted OC cell growth, migration, and invasion ability in vitro and in vivo. Knockdown of ISYNA1 increased stemness of OC cells, including self-renewal, CSC markers expression, ALDH activity, and proportion of CD44⁺/CD117⁺ CSCs. Conversely, ectopic overexpression of ISYNA1 suppresses cell proliferation, migration, invasion and stemness of OC cells. Mechanistically, ISYNA1 inhibits OC stemness by regulating myo-inositol to suppress Notch1 signaling. In summary, these data provide evidence that ISYNA1 act as a tumor suppressor in OC and a regulator of stemness, providing insight into potentially targetable pathways for ovarian cancer therapy.

Hypoxia‐induced ELF3 promotes tumor angiogenesis through IGF1/IGF1R

Epithelial ovarian cancer (EOC) is one of the most lethal gynecological cancers despite a relatively low incidence. Angiogenesis, one of the hallmarks of cancer, is essential for the pathogenesis of EOC, which is related to the induction of angiogenic factors. We found that ELF3 was highly expressed in EOCs under hypoxia and functioned as a transcription factor for IGF1. The ELF3‐mediated increase in the secretion of IGF1 and VEGF promoted endothelial cell proliferation, migration, and EOC angiogenesis. Although this situation was much exaggerated under hypoxia, ELF3 silencing under hypoxia significantly attenuated angiogenic activity in endothelial cells by reducing the expression and secretion of IGF1 and VEGF. ELF3 silencing attenuated angiogenesis and tumorigenesis in ex vivo and xenograft mouse models. Consequently, ELF3 plays an important role in the induction of angiogenesis and tumorigenesis in EOC as a transcription factor of IGF1. A detailed understanding of the biological mechanism of ELF3 may both improve current antiangiogenic therapies and have anticancer effects for EOC.

RIPK4 Is an Immune Regulating-Associated Biomarker for Ovarian Cancer and Possesses Generalization Value in Pan-Cancer

Ovarian cancer (OC) is the most lethal gynecologic cancer. Many studies have reported that RIPK4 (receptor interacting serine/threonine kinase 4) displayed a dysregulated level in many types of tumors. However, its expressions and functions in OC were rarely reported. The levels of RIPK4 were detected in OC and nontumor specimens using TCGA and GEO datasets. The prognostic values of RIPK4 in patients were determined using Kaplan-Meier methods and Kaplan-Meier assays. GO assays and KEGG pathway assays were carried out for functional enrichments. CIBERSORT was applied for estimating the fractions of immune cell types. Finally, RIPK4 was validated in pan-cancer. In this study, our group found that RIPK4 exhibited a higher level of RIPK4 in OC specimens than nontumor specimens. Survival studies revealed that patients with high RIPK4 expressions showed a shorter overall survival than those with low RIPK4 expression. Multivariate assays further confirmed that RIPK4 expression was an independent prognostic element for OC. KEGG pathway analysis displayed that the dysregulated genes in specimens with high RIPK4 expressions were enriched in focal adhesion, proteoglycans in cancer, central carbon metabolism in cancer, and insulin secretion. Correlation analyses showed that several TICs were positively correlated with RIPK4 expression. The pan-cancer validation results showed that RIPK4 was associated with survival in five tumors. Overall, our findings suggested RIPK4 as a prognostic marker in OC.

Bioinformatic Analysis for Influential Core Gene Identification and Prognostic Significance in Advanced Serous Ovarian Carcinoma

Background and objectives: Ovarian cancer is one of the leading causes of death among women worldwide. Most newly diagnosed ovarian cancer patients are diagnosed in advanced stages of the disease. Despite various treatments, most patients with advanced stage ovarian cancer, including serous ovarian cancer (the most common subtype of ovarian cancer), experience recurrence, which is associated with extremely poor prognoses. In the present study, we aimed to identify core genes involved in ovarian cancer and their associated molecular mechanisms, as well as to investigate related clinicopathological implications in ovarian cancer. Materials and methods: Three gene expression cohorts (GSE14407, GSE36668, and GSE38666) were obtained from the Gene Expression Omnibus databases to explore potential therapeutic biomarkers for ovarian cancer. Nine up-regulated and six down-regulated genes were screened. Three publicly available gene expression datasets (GSE14407, GSE36668, and GSE38666) were analyzed. Results: A total of 14 differently expressed genes (DEGs) were identified, among which nine genes were upregulated (BIRC5, CDCA3, CENPF, KIF4A, NCAPG, RRM2, UBE2C, VEGFA, and NR2F6) and were found to be significantly enriched in cell cycle regulation by gene ontology analysis. Further protein–protein interaction network analysis revealed seven hub genes among these DEGs. Moreover, Kaplan–Meier survival analysis showed that a higher expression of CDCA3 and UBE2C was associated with poor overall patient survival regardless of tumor stage and a higher tumor histologic grade. Conclusion: Altogether, our study suggests that CDCA3 and UBE2C may be valuable biomarkers for predicting the outcome of patients with advanced serous ovarian cancer.

FOXM1: A Multifunctional Oncoprotein and Emerging Therapeutic Target in Ovarian Cancer

Forkhead box M1 (FOXM1) is a member of the conserved forkhead box (FOX) transcription factor family. Over the last two decades, FOXM1 has emerged as a multifunctional oncoprotein and a robust biomarker of poor prognosis in many human malignancies. In this review article, we address the current knowledge regarding the mechanisms of regulation and oncogenic functions of FOXM1, particularly in the context of ovarian cancer. FOXM1 and its associated oncogenic transcriptional signature are enriched in >85% of ovarian cancer cases and FOXM1 expression and activity can be enhanced by a plethora of genomic, transcriptional, post-transcriptional, and post-translational mechanisms. As a master transcriptional regulator, FOXM1 promotes critical oncogenic phenotypes in ovarian cancer, including: (1) cell proliferation, (2) invasion and metastasis, (3) chemotherapy resistance, (4) cancer stem cell (CSC) properties, (5) genomic instability, and (6) altered cellular metabolism. We additionally discuss the evidence for FOXM1 as a cancer biomarker, describe the rationale for FOXM1 as a cancer therapeutic target, and provide an overview of therapeutic strategies used to target FOXM1 for cancer treatment.

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets.

Methods: In the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation.

Results: A total of 972 DEGs with P-value < 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer.

Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

Downregulated mRNA Expression of ZNF385B Is an Independent Predictor of Breast Cancer

Background

ZNF385B, a zinc finger protein, has been known as a potential biomarker in some neurological and hematological studies recently. Although numerous studies have demonstrated the potential function of zinc finger proteins in tumor progression, the effects of ZNF385B in breast cancer (BC) are less studied.

Methods

The Oncomine database and “ESurv” tool were used to explore the differential expression of ZNF385B in pan-cancer. Furthermore, data of patients with BC were downloaded from The Cancer Genome Atlas (TCGA). The receiver operating characteristic (ROC) curve of ZNF385B expression was established to explore the diagnostic value of ZNF385B and to obtain the cut-off value of high or low ZNF385B expression in BC. The chi-square test as well as Fisher exact test was used for identification of the relationships between clinical features and ZNF385B expression. Furthermore, the effects of ZNF385B on BC patients' survival were evaluated by the Kaplan-Meier and Cox regression. Data from the Gene Expression Omnibus (GEO) database were employed to validate the results of TCGA. Protein expression of ZNF385B in BC patient specimens was detected by immunohistochemistry (IHC) staining.

Results

ZNF385B expression was downregulated in most types of cancer including BC. Low ZNF385B expression was related with survival status, overall survival (OS), and recurrence of BC. ZNF385B had modest diagnostic value, which is indicated by the area under the ROC curve (AUC = 0.671). Patients with lower ZNF385B expression had shorter OS and RFS (relapse-free survival). It had been demonstrated that low ZNF385B expression represented independent prognostic value for OS and RFS by multivariate survival analysis. The similar results were verified by datasets from the GEO database as well. The protein expression of ZNF385B was decreased in patients' samples compared with adjacent tissues by IHC.

Conclusions

Low ZNF385B expression was an independent predictor for worse prognosis of BC patients.

Knockdown of KIF15 promotes cell apoptosis by activating crosstalk of multiple pathways in ovarian cancer: bioinformatic and experimental analysis

BACKGROUND

Ovarian cancer (OC) is the most lethal malignancy of women. Unlimited proliferation is a fundamental feature of OC cells. The genes associated with cell proliferation may be histopathologic biomarkers and targets of anti-tumor therapeutic strategies. The present study aimed to identify proliferation-associated biomarkers with prognostic, diagnostic, and therapeutic value and reveal the underlying molecular mechanism of candidate genes involved in OC by a combination of bioinformatic and experimental methods.

RESULTS

KIF15 was upregulated in early-stage OC tissues and could predict poor prognosis of patients of Stage I and II. The knockdown of KIF15 significantly inhibited cell proliferation, tumor formation, and growth as well as promoting apoptosis of OC cells. A combination of experimental and bioinformatic analyses revealed KIF15 knockdown promoted cell apoptosis by activating crosstalk of multiple pathways in OC.

CONCLUSION

KIF15, an early-stage prognostic gene, was identified as a candidate histopathologic biomarker and therapeutic target of OC.

Identification of core genes for early diagnosis and the EMT modulation of ovarian serous cancer by bioinformatics perspective

Ovarian serous carcinoma (OSC), as a common malignant tumor, poses a serious threat to women's health in that epithelial-mesenchymal transformation (EMT)-related modulation becomes heavily implicated in the invasion and progression of OSC. In this study, two core genes (BUB1B and NDC80) among the 16 hub genes have been identified to be involved in the molecular regulation of EMT and associated with the poor early survival of OSC at stages I+II. Through the Gene Regulatory Networks (GRN) analysis of 15 EMT regulators and core genes, it was revealed that TFAP2A and hsa-miR-655 could elaborately modulate EMT development of OSC. Next genetic variation analysis indicated that EMT regulator ELF3 would also serve as a crucial part in the occurrence and progression of OSC. Eventually, survival investigation suggested that TFAP2A, ELF3 and hsa-miR-655 were significantly associated with the overall survival of progressive OSC patients. Thus, combined with diversified bioinformatic analyses, BUB1B, NDC80, TFAP2A, ELF3 and hsa-miR-655 may act as the key biomarkers for early clinical diagnosis and prognosis evaluation of OSC patients as well as potential therapeutic target-points.

A Molecular Test for Quantifying Functional Notch Signaling Pathway Activity in Human Cancer

Simple Summary

The Notch signal transduction pathway is important for various physiological processes, including immune responses, and plays a role in many diseases, for example cancer. We have developed a new assay to quantitatively measure Notch pathway activity, and we validated it using data from various human cancer cell lines. The assay can be applied across different cell types, and offers numerous possibilities to explore the contribution of the Notch pathway to tumor formation and the stratification of cancer patients. We assessed Notch pathway activity in a cohort of T cell acute lymphoblastic leukemia (T-ALL) patient samples, and found that the pathway activity score more accurately reflects Notch pathway activity than a prediction on the basis of NOTCH1 mutations alone. Finally, we found that patients with low Notch pathway activity had a significantly shorter event-free survival compared to patients who had T-ALL cells with higher activity.

Abstract

Background: The Notch signal transduction pathway is pivotal for various physiological processes, including immune responses, and has been implicated in the pathogenesis of many diseases. The effectiveness of various targeted Notch pathway inhibitors may vary due to variabilities in Notch pathway activity among individual patients. The quantitative measurement of Notch pathway activity is therefore essential to identify patients who could benefit from targeted treatment. Methods: We here describe a new assay that infers a quantitative Notch pathway activity score from the mRNA levels of generally conserved direct NOTCH target genes. Following the calibration and biological validation of our Notch pathway activity model over a wide spectrum of human cancer types, we assessed Notch pathway activity in a cohort of T-ALL patient samples and related it to biological and clinical parameters, including outcome. Results: We developed an assay using 18 select direct target genes and high-grade serous ovarian cancer for calibration. For validation, seven independent human datasets (mostly cancer series) were used to quantify Notch activity in agreement with expectations. For T-ALL, the median Notch pathway activity was highest for samples with strong NOTCH1-activating mutations, and T-ALL patients of the TLX subtype generally had the highest levels of Notch pathway activity. We observed a significant relationship between ICN1 levels and the absence/presence of NOTCH1-activating mutations with Notch pathway activity scores. Patients with the lowest Notch activity scores had the shortest event-free survival compared to other patients. Conclusions: High Notch pathway activity was not limited to T-ALL samples harboring strong NOTCH1 mutations, including juxtamembrane domain mutations or hetero-dimerization combined with PEST-domain or FBXW7 mutations, indicating that additional mechanisms may activate Notch signaling. The measured Notch pathway activity was related to intracellular NOTCH levels, indicating that the pathway activity score more accurately reflects Notch pathway activity than when it is predicted on the basis of NOTCH1 mutations. Importantly, patients with low Notch pathway activity had a significantly shorter event-free survival compared to patients showing higher activity.

Bioinformatics analysis of differentially expressed genes in subchondral bone in early experimental osteoarthritis using microarray data

Background

Osteoarthritis (OA) is the most common arthritic disease in humans, affecting the majority of individuals over 65 years of age. The aim of this study is to identify the gene expression profile specific to subchondral bone in OA by comparing the different expression profiles in experimental and sham-operation groups.

Methods

Gene expression profile GSE30322 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by limma package. And Database for Annotation, Visualization and Integrated Discovery (DAVID) databases were further used to identify the potential gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, a protein–protein interaction (PPI) network was constructed and significant modules were extracted.

Results

Totally, 588 DEGs were identified including 199 upregulated DEGs and 389 downregulated DEGs screened in OA and sham-operation. GO showed that DEGs were significantly enhanced for ribosomal subunit export from nucleus and molting cycle. KEGG pathway analysis revealed that target genes were enriched in thiamine metabolism.

Conclusion

These key candidate DEGs that affect the progression of OA, and these genes might serve as potential therapeutic targets for OA.

Transcriptome analysis reveals GPNMB as a potential therapeutic target for gastric cancer

Gastric cancer has the fifth highest incidence of disease and is the third leading cause of cancer-associated mortality in the world. The etiology of gastric cancer is complex and needs to be fully elucidated. Thus, it is necessary to explore potential pathogenic genes and pathways that contribute to gastric cancer. Gene expression profiles of the GSE33335 and GSE54129 datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were compared and identified using R software. The DEGs were then subjected to gene set enrichment analysis and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Survival analyses based on The Cancer Genome Atlas database were used to further screen the essential DEGs. A knockdown assay was performed to determine the function of the candidate gene in gastric cancer. Finally, the association between the candidate gene and immune-related genes was investigated. We found that GPNMB serves as an essential gene, with a high expression level, and predicts a worse outcome of gastric cancer. Knockdown of GPNMB inhibited gastric cancer cell proliferation and migration. In addition, GPNMB may augment the immunosuppressive ability of gastric cancer by recruiting immunosuppressive cells and promoting immune cell exhaustion through PI3K/AKT/CCL4 signaling axis. Collectively, these data suggest that GPNMB acts as an important positive mediator of tumor progression in gastric cancer, and GPNMB could exert multimodality modulation of gastric cancer-mediated immune suppression.

The epigenetic factor BORIS (CTCFL) controls the androgen receptor regulatory network in ovarian cancer

The identification of prognostic biomarkers is a priority for patients suffering from high-grade serous ovarian cancer (SOC), which accounts for >70% of ovarian cancer (OC) deaths. Meanwhile, borderline ovarian cancer (BOC) is a low malignancy tumor and usually patients undergo surgery with low probabilities of recurrence. However, SOC remains the most lethal neoplasm due to the lack of biomarkers for early diagnosis and prognosis. In this regard, BORIS (CTCFL), a CTCF paralog, is a promising cancer biomarker that is overexpressed and controls transcription in several cancer types, mainly in OC. Studies suggest that BORIS has an important function in OC by altering gene expression, but the effect and extent to which BORIS influences transcription in OC from a genome-wide perspective is unclear. Here, we sought to identify BORIS target genes in an OC cell line (OVCAR3) with potential biomarker use in OC tumor samples. To achieve this, we performed in vitro knockout and knockdown experiments of BORIS in OVCAR3 cell line followed by expression microarrays and bioinformatics network enrichment analysis to identify relevant BORIS target genes. In addition, ex vivo expression data analysis of 373 ovarian cancer patients were evaluated to identify the expression patterns of BORIS target genes. In vitro, we uncovered 130 differentially expressed genes and obtained the BORIS-associated regulatory network, in which the androgen receptor (AR) acts as a major transcription factor. Also, FN1, FAM129A, and CD97 genes, which are related to chemoresistance and metastases in OC, were identified. In SOC patients, we observed that malignancy is associated with high levels of BORIS expression while BOC patients show lower levels. Our study suggests that BORIS acts as a main regulator, and has the potential to be used as a prognostic biomarker and to yield novel drug targets among the genes BORIS controls in SOC patients.

ChIP-seq analysis reveals alteration of H3K4 trimethylation occupancy in cancer-related genes by cold atmospheric plasma

Cold atmospheric plasma (CAP) has gained attention for use in cancer treatment owing to its ability to preferentially induce cancer cell death; however, the involved molecular mechanism remains to be elucidated. Herein, an epigenetic effect of CAP on cancer cells was examined by performing a genome-wide ChIP-seq for H3K4me3 in MCF-7 breast cancer cell line. Consequently, 899 genes showed significantly changed methylation level at H3K4 with constructing "Cellular Compromise, DNA Replication, Recombination, Repair, and Cell Cycle" as the top network. Comparisons with expression array data revealed a coincidence between histone modification and gene expression for 18 genes, and the association was confirmed by ChIP-PCR and qRT-PCR for selected genes. The expression of the affected genes, such as HSCB and PRPS1, was recovered when a histone demethylase JARID1A was inhibited. Furthermore, JARID1A was induced by CAP via the reactive oxygen species signaling. The two genes are known as oncogenes and show a higher expression in breast cancer tissue, and this was supported by the decreased colony formation ability of MCF-7 cells when the cells were treated with siRNAs against each gene. Taken together, these data indicate that CAP inhibits cancer cell proliferation by modulating the methylation level of H3K4 corresponding to oncogenes.

PMLPR: A novel method for predicting subcellular localization based on recommender systems

The importance of protein subcellular localization problem is due to the importance of protein's functions in different cell parts. Moreover, prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. But, since some proteins move between different subcellular locations, they can have multiple locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. In this paper, we introduced a method, PMLPR, to predict locations for a protein. PMLPR predicts a list of locations for each protein based on recommender systems and it can properly overcome the multiple location prediction problem. For evaluating the performance of PMLPR, we considered six datasets RAT, FLY, HUMAN, Du et al., DBMLoc and Höglund. The performance of this algorithm is compared with six state-of-the-art algorithms, YLoc, WOLF-PSORT, prediction channel, MDLoc, Du et al. and MultiLoc2-HighRes. The results indicate that our proposed method is significantly superior on RAT and Fly proteins, and decent on HUMAN proteins. Moreover, on the datasets introduced by Du et al., DBMLoc and Höglund, PMLPR has comparable results. For the case study, we applied the algorithms on 8 proteins which are important in cancer research. The results of comparison with other methods indicate the efficiency of PMLPR.

Reciprocal modulation of mesenchymal stem cells and tumor cells promotes lung cancer metastasis

Metastasis is a multi-step process in which direct crosstalk between cancer cells and their microenvironment plays a key role. Here, we assessed the effect of paired tumor-associated and normal lung tissue mesenchymal stem cells (MSCs) on the growth and dissemination of primary human lung carcinoma cells isolated from the same patients. We show that the tumor microenvironment modulates MSC gene expression and identify a four-gene MSC signature that is functionally implicated in promoting metastasis. We also demonstrate that tumor-associated MSCs induce the expression of genes associated with an aggressive phenotype in primary lung cancer cells and selectively promote their dissemination rather than local growth. Our observations provide insight into mechanisms by which the stroma promotes lung cancer metastasis.

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Distinct gene expression profiles distinguish normal lung and tumor-associated MSCs.

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MSCs induce EMT- and hypoxia-related genes in primary tumor cells and promote their metastatic potential.

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A 4-gene T-MSC signature is involved in MSC-induced metastasis promotion.

The tumor microenvironment, which includes mesenchymal stem cells (MSCs) among many other stromal cell types, plays a fundamental role in cancer metastasis. Although MSCs are suggested to participate in tumor progression, most studies thus far have been performed on bone marrow-derived MSCs and cancer cell lines. Using primary human pulmonary MSCs and paired lung cancer cells, we show that tumor cells modulate MSCs to acquire properties, including a four-gene signature, which allow them to promote tumor dissemination. Our results provide insight into the mutual cancer cell-stromal cell modulation that drives tumor dissemination.

Novel Mad2-targeting miR-493-3p controls mitotic fidelity and cancer cells' sensitivity to paclitaxel

The molecular pathways that contribute to the proliferation and drug response of cancer cells are highly complex and currently insufficiently characterized. We have identified a previously unknown microRNA-based mechanism that provides cancer cells means to stimulate tumorigenesis via increased genomic instability and, at the same time, evade the action of clinically utilized microtubule drugs. We demonstrate miR-493-3p to be a novel negative regulator of mitotic arrest deficient-2 (MAD2), an essential component of the spindle assembly checkpoint that monitors the fidelity of chromosome segregation. The microRNA targets the 3′ UTR of Mad2 mRNA thereby preventing translation of the Mad2 protein. In cancer cells, overexpression of miR-493-3p induced a premature mitotic exit that led to increased frequency of aneuploidy and cellular senescence in the progeny cells. Importantly, excess of the miR-493-3p conferred resistance of cancer cells to microtubule drugs. In human neoplasms, miR-493-3p and Mad2 expression alterations correlated with advanced ovarian cancer forms and high miR-493-3p levels were associated with reduced survival of ovarian and breast cancer patients with aggressive tumors, especially in the paclitaxel therapy arm. Our results suggest that intratumoral profiling of miR-493-3p and Mad2 levels can have diagnostic value in predicting the efficacy of taxane chemotherapy.

Altered expression of different GalNAc‑transferases is associated with disease progression and poor prognosis in women with high-grade serous ovarian cancer

Protein glycosylation perturbations are implicated in a variety of diseases, including cancer. Aberrant glycosylation in cancer is frequently attributed to altered expression of polypeptide GalNAc transferases (GalNAc‑Ts) - enzymes initiating mucin-type O-glycosylation. A previous study from our group demonstrated that one member of this family (GALNT3) is overexpressed in epithelial ovarian cancer (EOC), and GALNT3 expression correlated with shorter progression-free survival (PFS) in EOC patients with advanced disease. As considerable degree of redundancy between members of the GalNAc‑Ts gene family has been frequently observed, we decided to investigate whether other members of this family are essential in EOC progression. In silico analysis based on publically available data was indicative for altered expression of five GalNAc‑Ts (GALNT2, T4, T6, T9 and T14) in ovarian high-grade serous carcinoma (HGSC) samples compared to non-tumoral (control) ovarian tissue. We analyzed protein expression of these GalNAc‑Ts in EOC cells and tumors by western blotting, followed by immunohistochemical (IHC) evaluation of their expression in EOC tumor and control samples using tissue microarrays (TMAs). Western blot analyses were indicative for low expression of GALNT2 and strong expression of GALNT6, T9 and T14 in both EOC cells and tumors. These observations were confirmed by IHC. GALNT2 displayed significantly lower expression, while GALNT6, GALNT9 and GALNT14 showed significantly higher expression in HGSC tumors compared to control tissue. Importantly, GALNT6 and GALNT14 expression correlated with poor prognosis of serous EOC patients. Moreover, our results suggest for overlapping functions of some GalNAc‑Ts, more specifically GALNT3 and GALNT6, in directing EOC progression. Our results are indicative for a possible implication of different members of the GalNAc‑T gene family in modulating EOC progression, and the potential use of GALNT6 and GALNT14 as novel prognostic EOC biomarkers. These data warrant future studies on the role of members of the GalNAc‑Ts gene family in ovarian tumorigenesis.

Bioinformatics analysis of fibroblasts exposed to TGF‑β at the early proliferation phase of wound repair

The aim of the current study was to identify gene signatures during the early proliferation stage of wound repair and the effect of TGF-β on fibroblasts and reveal their potential mechanisms. The gene expression profiles of GSE79621 and GSE27165 were obtained from GEO database. Differentially expressed genes (DEGs) were identified using Morpheus and co-expressed DEGs were selected using Venn Diagram. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. Protein-protein interaction (PPI) networks of the DEGs were constructed using Cytoscape software. PPI interaction network was divided into subnetworks using the MCODE algorithm and the function of the top one module was analyzed using DAVID. The results revealed that upregulated DEGs were significantly enriched in biological process, including the Arp2/3 complex-mediated actin nucleation, positive regulation of hyaluronan cable assembly, purine nucleobase biosynthetic process, de novo inosine monophosphate biosynthetic process, positive regulation of epithelial cell proliferation, whereas the downregulated DEGs were enriched in the regulation of blood pressure, negative regulation of cell proliferation, ossification, negative regulation of gene expression and type I interferon signaling pathway. KEGG pathway analysis showed that the upregulated DEGs were enriched in shigellosis, pathogenic Escherichia coli infection, the mitogen-activated protein kinase signaling pathway, Ras signaling pathway and bacterial invasion of epithelial cells. The downregulated DEGs were enriched in systemic lupus erythematosus, lysosome, arachidonic acid metabolism, thyroid cancer and allograft rejection. The top 10 hub genes were identified from the PPI network. The top module analysis revealed that the included genes were involved in ion channel, neuroactive ligand-receptor interaction pathway, purine metabolism and intestinal immune network for IgA production pathway. The functional analysis revealed that TGF-β may promote fibroblast migration and proliferation and defend against microorganisms at the early proliferation stage of wound repair. Furthermore, these results may provide references for chronic wound repair.

Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Intense efforts have been made to elucidate the pathogeny, but the molecular mechanisms of HCC are still not well understood. To identify the candidate genes in the carcinogenesis and progression of HCC, microarray datasets GSE19665, GSE33006 and GSE41804 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. A total of 273 DEGs were identified, consisting of 189 downregulated genes and 84 upregulated genes. The enriched functions and pathways of the DEGs include protein activation cascade, complement activation, carbohydrate binding, complement and coagulation cascades, mitotic cell cycle and oocyte meiosis. Sixteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell division, cell cycle and nuclear division. Survival analysis showed that BUB1, CDC20, KIF20A, RACGAP1 and CEP55 may be involved in the carcinogenesis, invasion or recurrence of HCC. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of HCC, and provide candidate targets for diagnosis and treatment of HCC.

Analysis of long non-coding RNA expression profiles in ovarian cancer

Ovarian cancer is one of the major threats to female health. Identifying cancer cases at an early stage and selecting effective therapeutic drugs for patients is challenging. The number of studies concerning long non-coding RNAs (lncRNAs) is increasing rapidly; there is a large body of evidence indicating that lncRNAs are crucial in oncogenic and tumor-suppression mechanisms. Therefore, in the present study, lncRNA expression in ovarian cancer was considered. All of the existing ovarian cancer microarray datasets in the Gene Expression Omnibus database were assessed and two met the criteria for the present study; these were designated the training and validation sets. A re-annotation pipeline method was established to annotate lncRNAs from existing probe sets. When comparing ovarian cancer with normal ovarian tissues, seven lncRNAs from the RefSeq database, based on their combined ability to classify tissue in the training set, were identified and validated with the validation set. Research into the molecular functions of the seven identified lncRNAs may contribute to the understanding of ovarian cancer oncogenesis; they may also be candidates for novel ovarian cancer biomarkers.

Identification of candidate biomarkers and analysis of prognostic values in ovarian cancer by integrated bioinformatics analysis

Ovarian cancer is the first leading cause of mortality in gynecological malignancies. To identify key genes and microRNAs in ovarian cancer, mRNA microarray dataset GSE36668, GSE18520, GSE14407 and microRNA dataset GSE47841 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained using GEO2R. Functional and pathway enrichment analysis were performed for DEGs using DAVID database. Protein-protein interaction (PPI) network was established by STRING and visualized by Cytoscape. Following, overall survival (OS) analysis of hub genes was performed by the Kaplan-Meier plotter online tool. Module analysis of the PPI network was performed using MCODE. Moreover, miRecords was applied to predict the targets of the DEMs. A total of 345 DEGs were obtained, which were mainly enriched in the terms related to cell cycle, mitosis, and ovulation cycle process. A PPI network was constructed, consisting of 141 nodes and 296 edges. Sixteen genes had high degrees in the network. High expression of four genes of the 16 genes was associated with worse OS of patients with ovarian cancer, including CCNB1, CENPF, KIF11, and ZWINT. A significant module was detected from the PPI network. The enriched functions and pathways included cell cycle, nuclear division, and oocyte meiosis. Additionally, a total of 36 DEMs were identified. The expression of KIF11 was negatively correlated with that of has-miR-424 and has-miR-381, and it was also the potential target of two microRNAs. In conclusion, these results identified key genes, which could provide potential targets for ovarian cancer diagnosis and treatment.

Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning

OBJECTIVES

Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diagnosing SBOTs with accuracy of 48% to 79%. Using DNA microarray technology, we designed multicategory classification models to support frozen-section diagnosis within 30 minutes.

MATERIALS AND METHODS

We systematically evaluated 6 machine learning algorithms and 3 feature selection methods using 5-fold cross-validation and a grid search on microarray data obtained from the National Center for Biotechnology Information. To validate the models and selected biomarkers, expression profiles were analyzed in tissue samples obtained from the Yonsei University College of Medicine.

RESULTS

The best accuracy of the optimal machine learning model was 97.3%. In addition, 5 features, including the expression of the putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and serous ovarian carcinoma groups. Different expression levels of SNTN and AOX1 were validated by real-time quantitative reverse-transcription polymerase chain reaction, Western blotting, and immunohistochemistry. A multinomial logistic regression model using SNTN and AOX1 alone was used to construct a simple-to-use equation that gave a diagnostic test accuracy of 91.9%.

CONCLUSIONS

We identified 2 biomarkers, SNTN and AOX1, that are likely involved in the pathogenesis and progression of ovarian tumors. An accurate diagnosis of ovarian tumor subclasses by application of the equation in conjunction with expression analysis of SNTN and AOX1 would offer a new accurate diagnosis tool in conjunction with frozen-section diagnosis within 30 minutes.

FOXM1 is a downstream target of LPA and YAP oncogenic signaling pathways in high grade serous ovarian cancer

Lysophosphatidic acid (LPA), a prototypical ligand for G protein coupled receptors, and Forkhead box protein M1 (FOXM1), a transcription factor that regulates expression of a wide array of genes involved in cancer initiation and progression, are two important oncogenic signaling molecules in human epithelial ovarian cancers (EOC). We conducted in vitro mechanistic studies using pharmacological inhibitors, genetic forms of the signaling molecules, and RNAi-mediated gene knock-down to uncover the molecular mechanisms of how these two molecules interact in EOC cells. Additionally, in vivo mouse studies were performed to confirm the functional involvement of FOXM1 in EOC tumor formation and progression. We show for the first time that LPA up-regulates expression of active FOXM1 splice variants in a time- and dose-dependent manner in the human EOC cell lines OVCA433, CAOV3, and OVCAR5. Gi-PI3K-AKT and G12/13-Rho-YAP signaling pathways were both involved in the LPA receptor (LPA1-3) mediated up-regulation of FOXM1 at the transcriptional level. In addition, down-regulation of FOXM1 in CAOV3 xenografts significantly reduced tumor and ascites formation, metastasis, and expression of FOXM1 target genes involved in cell proliferation, migration, or invasion. Collectively, our data link the oncolipid LPA, the oncogene YAP, and the central regulator of cell proliferation/mutagenesis FOXM1 in EOC cells. Moreover, these results provide further support for the importance of these pathways as potential therapeutic targets in EOC.

Classification of serous ovarian tumors based on microarray data using multicategory support vector machines

Ovarian cancer, the most fatal of reproductive cancers, is the fifth leading cause of death in women in the United States. Serous borderline ovarian tumors (SBOTs) are considered to be earlier or less malignant forms of serous ovarian carcinomas (SOCs). SBOTs are asymptomatic and progression to advanced stages is common. Using DNA microarray technology, we designed multicategory classification models to discriminate ovarian cancer subclasses. To develop multicategory classification models with optimal parameters and features, we systematically evaluated three machine learning algorithms and three feature selection methods using five-fold cross validation and a grid search. The study included 22 subjects with normal ovarian surface epithelial cells, 12 with SBOTs, and 79 with SOCs according to microarray data with 54,675 probe sets obtained from the National Center for Biotechnology Information gene expression omnibus repository. Application of the optimal model of support vector machines one-versus-rest with signal-to-noise as a feature selection method gave an accuracy of 97.3%, relative classifier information of 0.916, and a kappa index of 0.941. In addition, 5 features, including the expression of putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and SOC groups. An accurate diagnosis of ovarian tumor subclasses by application of multicategory machine learning would be cost-effective and simple to perform, and would ensure more effective subclass-targeted therapy.

Chromosome 19 rearrangements in ovarian carcinomas: zinc finger genes are particularly targeted

Chromosome 19 is frequently rearranged in ovarian carcinomas, but the pathogenetic consequences of this are not clearly understood. We performed microarray gene expression analysis on 12 ovarian carcinomas that carry a rearranged chromosome 19 in their karyotype. These aberrant chromosomes have previously been microdissected and analyzed by array-based CGH. In the current study, we wanted to explore whether the genomic alterations thus detected correlated with changes in gene expression. The microarray gene expression analysis gave information on 407 genes mapping in gained genomic regions on chromosome 19, of which 92 showed association between DNA gain and upregulated expression. Of the genes showing this association, 39 (42%) showed gain in at least two samples. The majority of these 39 genes of interest (n = 24, 62%) encode zinc finger proteins, which otherwise make up only 15% of the approximately 1,400 genes on chromosome 19. The strongest association was found for ZNF223 which was upregulated in samples with genomic gain compared with samples without gain. We suggest that DNA copy number changes brought about by rearrangements of chromosome 19 contribute to ovarian carcinogenesis by leading to upregulation of ZNF223 and other zinc finger genes. © 2014 Wiley Periodicals, Inc.

Global miRNA expression analysis of serous and clear cell ovarian carcinomas identifies differentially expressed miRNAs including miR-200c-3p as a prognostic marker

BACKGROUND

Improved insight into the molecular characteristics of the different ovarian cancer subgroups is needed for developing a more individualized and optimized treatment regimen. The aim of this study was to a) identify differentially expressed miRNAs in high-grade serous ovarian carcinoma (HGSC), clear cell ovarian carcinoma (CCC) and ovarian surface epithelium (OSE), b) evaluate selected miRNAs for association with clinical parameters including survival and c) map miRNA-mRNA interactions.

METHODS

Differences in miRNA expression between HGSC, CCC and OSE were analyzed by global miRNA expression profiling (Affymetrix GeneChip miRNA 2.0 Arrays, n = 12, 9 and 9, respectively), validated by RT-qPCR (n = 35, 19 and 9, respectively), and evaluated for associations with clinical parameters. For HGSC, differentially expressed miRNAs were linked to differentially expressed mRNAs identified previously.

RESULTS

Differentially expressed miRNAs (n = 78) between HGSC, CCC and OSE were identified (FDR < 0.01%), of which 18 were validated (p < 0.01) using RT-qPCR in an extended cohort. Compared with OSE, miR-205-5p was the most overexpressed miRNA in HGSC. miR-200 family members and miR-182-5p were the most overexpressed in HGSC and CCC compared with OSE, whereas miR-383 was the most underexpressed. miR-205-5p and miR-200 members target epithelial-mesenchymal transition (EMT) regulators, apparently being important in tumor progression. miR-509-3-5p, miR-509-5p, miR-509-3p and miR-510 were among the strongest differentiators between HGSC and CCC, all being significantly overexpressed in CCC compared with HGSC. High miR-200c-3p expression was associated with poor progression-free (p = 0.031) and overall (p = 0.026) survival in HGSC patients. Interacting miRNA and mRNA targets, including those of a TP53-related pathway presented previously, were identified in HGSC.

CONCLUSIONS

Several miRNAs differentially expressed between HGSC, CCC and OSE have been identified, suggesting a carcinogenetic role for these miRNAs. miR-200 family members, targeting EMT drivers, were mostly overexpressed in both subgroups, among which miR-200c-3p was associated with survival in HGSC patients. A set of miRNAs differentiates CCC from HGSC, of which miR-509-3-5p and miR-509-5p are the strongest classifiers. Several interactions between miRNAs and mRNAs in HGSC were mapped.

The transcription factor FOXM1 (Forkhead box M1): proliferation-specific expression, transcription factor function, target genes, mouse models, and normal biological roles

FOXM1 (Forkhead box M1) is a typical proliferation-associated transcription factor, which stimulates cell proliferation and exhibits a proliferation-specific expression pattern. Accordingly, both the expression and the transcriptional activity of FOXM1 are increased by proliferation signals, but decreased by antiproliferation signals, including the positive and negative regulation by protooncoproteins or tumor suppressors, respectively. FOXM1 stimulates cell cycle progression by promoting the entry into S-phase and M-phase. Moreover, FOXM1 is required for proper execution of mitosis. Accordingly, FOXM1 regulates the expression of genes, whose products control G1/S-transition, S-phase progression, G2/M-transition, and M-phase progression. Additionally, FOXM1 target genes encode proteins with functions in the execution of DNA replication and mitosis. FOXM1 is a transcriptional activator with a forkhead domain as DNA binding domain and with a very strong acidic transactivation domain. However, wild-type FOXM1 is (almost) inactive because the transactivation domain is repressed by three inhibitory domains. Inactive FOXM1 can be converted into a very potent transactivator by activating signals, which release the transactivation domain from its inhibition by the inhibitory domains. FOXM1 is essential for embryonic development and the foxm1 knockout is embryonically lethal. In adults, FOXM1 is important for tissue repair after injury. FOXM1 prevents premature senescence and interferes with contact inhibition. FOXM1 plays a role for maintenance of stem cell pluripotency and for self-renewal capacity of stem cells. The functions of FOXM1 in prevention of polyploidy and aneuploidy and in homologous recombination repair of DNA-double-strand breaks suggest an importance of FOXM1 for the maintenance of genomic stability and chromosomal integrity.

FOXM1 (Forkhead box M1) in tumorigenesis: overexpression in human cancer, implication in tumorigenesis, oncogenic functions, tumor-suppressive properties, and target of anticancer therapy

FOXM1 (Forkhead box M1) is a typical proliferation-associated transcription factor and is also intimately involved in tumorigenesis. FOXM1 stimulates cell proliferation and cell cycle progression by promoting the entry into S-phase and M-phase. Additionally, FOXM1 is required for proper execution of mitosis. In accordance with its role in stimulation of cell proliferation, FOXM1 exhibits a proliferation-specific expression pattern and its expression is regulated by proliferation and anti-proliferation signals as well as by proto-oncoproteins and tumor suppressors. Since these factors are often mutated, overexpressed, or lost in human cancer, the normal control of the foxm1 expression by them provides the basis for deregulated FOXM1 expression in tumors. Accordingly, FOXM1 is overexpressed in many types of human cancer. FOXM1 is intimately involved in tumorigenesis, because it contributes to oncogenic transformation and participates in tumor initiation, growth, and progression, including positive effects on angiogenesis, migration, invasion, epithelial-mesenchymal transition, metastasis, recruitment of tumor-associated macrophages, tumor-associated lung inflammation, self-renewal capacity of cancer cells, prevention of premature cellular senescence, and chemotherapeutic drug resistance. However, in the context of urethane-induced lung tumorigenesis, FOXM1 has an unexpected tumor suppressor role in endothelial cells because it limits pulmonary inflammation and canonical Wnt signaling in epithelial lung cells, thereby restricting carcinogenesis. Accordingly, FOXM1 plays a role in homologous recombination repair of DNA double-strand breaks and maintenance of genomic stability, that is, prevention of polyploidy and aneuploidy. The implication of FOXM1 in tumorigenesis makes it an attractive target for anticancer therapy, and several antitumor drugs have been reported to decrease FOXM1 expression.