The anterior gradient homolog 3 (AGR3) gene is associated with differentiation and survival in ovarian cancer

Bioinformatics Based Drug Repurposing Approach for Breast and Gynecological Cancers: RECQL4/FAM13C Genes Address Common Hub Genes and Drugs

Objective

The prevalence of breast cancer and gynaecological cancers is high, and these cancer types can occur consecutively as secondary cancers. The aim of our study is to determine the genes commonly expressed in these cancers and to identify the common hub genes and drug components.

Materials and Methods

Gene intensity values of breast cancer, gynaecological cancers such as cervical, ovarian and endometrial cancers were used from the Gene Expression Omnibus database Affymetrix Human Genome U133 Plus 2.0 Array project. Using the linear modelling method included in the R LIMMA package, genes that differ between healthy individuals and cancer patients were identified. Hub genes were determined using cytoHubba in Cytoscape program. "ShinyGo 0.80" tool was used to determine the disease-specific biological KEGG pathways. Drug.MATADOR from the ShinyGo 0.80 tool was used to predict drug-target relationships.

Results

The RecQ Like Helicase 4 and Family with Sequence Similarity 13 Member C genes were found to be similarly expressed in breast cancer and gynaecological cancers. Upon KEGG pathway analyses with hub genes, Drug.MATADOR analysis with hub genes related to cancer related pathways was performed. We have determined these gene/drug interactions: NBN (targeted by Hydroxyurea), EP300 (targeted by Acetylcarnitine) and MAPK14 (targeted by Salicylate and Dibutyryl cyclic AMP).

Conclusion

The drugs associated with hub genes determined in our study are not routinely used in cancer treatment. Our study offers the opportunity to identify the target genes of drugs used in breast and gynaecological cancers with the drug repurposing approach.

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.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression

Serous borderline tumors (SBT) are epithelial neoplastic lesions of the ovaries that commonly have a good prognosis. In 10-15% of cases, however, SBT will recur as low-grade serous cancer (LGSC), which is deeply invasive and responds poorly to current standard chemotherapy1,2,3. While genetic alterations suggest a common origin, the transition from SBT to LGSC remains poorly understood4. Here, we integrate spatial proteomics5 with spatial transcriptomics to elucidate the evolution from SBT to LGSC and its corresponding metastasis at the molecular level in both the stroma and the tumor. We show that the transition of SBT to LGSC occurs in the epithelial compartment through an intermediary stage with micropapillary features (SBT-MP), which involves a gradual increase in MAPK signaling. A distinct subset of proteins and transcripts was associated with the transition to invasive tumor growth, including the neuronal splicing factor NOVA2, which was limited to expression in LGSC and its corresponding metastasis. An integrative pathway analysis exposed aberrant molecular signaling of tumor cells supported by alterations in angiogenesis and inflammation in the tumor microenvironment. Integration of spatial transcriptomics and proteomics followed by knockdown of the most altered genes or pharmaceutical inhibition of the most relevant targets confirmed their functional significance in regulating key features of invasiveness. Combining cell-type resolved spatial proteomics and transcriptomics allowed us to elucidate the sequence of tumorigenesis from SBT to LGSC. The approach presented here is a blueprint to systematically elucidate mechanisms of tumorigenesis and find novel treatment strategies.

Prognostic Impact of AGR3 Protein Expression in Breast Cancer: A Systematic Review and Meta-analysis

OBJECTIVE

 To investigate the clinicopathological significance and prognosis of the expression of the anterior gradient 3 (AGR3) protein in women with breast cancer.

DATA SOURCES

 The PubMed, CINAHL, EMBASE, Scopus, and Web of Science databases were searched for studies published in English and without restrictions regarding the year of publication. The search terms were: breast cancer AND anterior gradient 3 OR AGR3 expression.

STUDY SELECTION

 We included observational or interventional studies, studies on AGR3 protein expression by immunohistochemistry, and studies on invasive breast cancer. Case reports, studies with animals, and reviews were excluded. In total, 4 studies were included, containing 713 cases of breast cancer.

DATA COLLECTION

 Data were extracted on clinicopathological characteristics and survival. A meta-analysis of the prevalence of AGR3 expression was performed according to the clinicopathological characteristics, hazard ratios (HRs), and overall survival and disease-free survival.

DATA SYNTHESIS

 The expression of AGR3 was found in 62% of the cases, and it was associated with histological grade II, positivity of estrogen and progesterone receptors, low expression of ki67, recurrence or distant metastasis, and lumen subtypes. In patients with low and intermediate histological grades, AGR3 expression was associated with worse overall survival (HR: 2.39; 95% confidence interval [95%CI]: 0.628-4.159; p = 0.008) and worse disease-free survival (HR: 3.856; 95%CI: 1.026-6.686; p = 0.008).

CONCLUSION

 The AGR3 protein may be a biomarker for the early detection of breast cancer and predict prognosis in luminal subtypes. In addition, in patients with low and intermediate histological grades, AGR3 protein expression may indicate an unfavorable prognosis in relation to survival.

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.

Low-grade serous ovarian cancer: expert consensus report on the state of the science

Compared with high-grade serous carcinoma, low-grade serous carcinoma of the ovary or peritoneum is a less frequent epithelial ovarian cancer type that is poorly sensitive to chemotherapy and affects younger women, many of whom endure years of ineffective treatments and poor quality of life. The pathogenesis of this disease and its management remain incompletely understood. However, recent advances in the molecular characterization of the disease and identification of novel targeted therapies with activity in low-grade serous carcinoma offer the promise of improved outcomes. To update clinicians regarding recent scientific and clinical trial advancements and discuss unanswered questions related to low-grade serous carcinoma diagnosis and treatment, a panel of experts convened for a workshop in October 2022 to develop a consensus document addressing pathology, translational research, epidemiology and risk, clinical management, and ongoing research. In addition, the patient perspective was discussed. The recommendations developed by this expert panel-presented in this consensus document-will guide practitioners in all settings regarding the clinical management of women with low-grade serous carcinoma and discuss future opportunities to improve research and patient care.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

Prognostic value of regulatory T cells and T helper 17 cells in high grade serous ovarian carcinoma

PURPOSE

In recent years the tumor microenvironment and its interaction with the tumor has emerged into research focus with increased attention to the composition of Tumor-infiltrating lymphocytes. We wanted to quantify the composition of Regulatory T cells (Tregs) and T helper 17 cells (Th17 cells) and their prognostic impact in high-grade serous tubo-ovarian carcinoma.

METHODS

Tregs and Th17 cells were determined by immunohistochemical analysis of CD25 FoxP3 and RORγt, respectively on tissue microarrays of a cohort of 222 patients with reviewed histology and available clinical data. Expression was analyzed with Qupath for quantification and integration with clinical data enabled calculation of prognostic impact. For validation FOXP3 and RORC mRNA expression levels from 502 patients with HGSC in publicly available datasets were evaluated.

RESULTS

An average percentage of 0.93 Tregs and of 0.06 Th17 cells was detected per cells in overall tissue. Optimal cut-offs were determined and higher Tregs were associated with a better overall survival in stroma (p = 0.006), tumor area (p = 0.0012) and overall tissue (p = 0.02). After accounting for well-known prognostic factors age at diagnosis, residual tumor and FIGO stage, this association remained significant for stromal Tregs with overall survival (p = 0.02). Survival analysis for Th17 cells revealed no significant association with survival rates. Moreover, lower Th17/Treg ratios had a positive impact on patient overall survival (p = 0.025 tumor, p = 0.049 stroma and p = 0.016 overall tissue).

CONCLUSION

Our results outline a positive prognostic effect for higher Tregs but not for Th17 in high grade serous tubo-ovarian carcinoma.

Modulatory Role of TPPP3 in Microtubule Organization and Its Impact on Alpha-Synuclein Pathology

Parkinson’s disease is characterized by locomotion deficits, dopaminergic neuronal loss and alpha-synuclein (SYN) aggregates; the Tubulin Polymerization Promoting Protein (TPPP/p25 or TPPP1) is also implicated in these processes. The moonlighting and chameleon TPPP1 modulates the dynamics/stability of the multifunctional microtubule network by promoting its acetylation and bundling. Previously, we identified the microtubule-associated TPPP3, a homologue of TPPP1 lacking its N-terminus; however, its involvement in physiological or pathological processes was not elucidated. In this work, we have shown the modulatory role of TPPP3, similarly to TPPP1, in microtubule organization, as well as its homo- and hetero-associations with TPPP1. TPPP3, in contrast to TPPP1, virtually does not bind to SYN; consequently, it does not promote SYN aggregation. Its anti-aggregative potency is achieved by counteracting the formation of the TPPP1–SYN pathological complex/aggregation leading to Parkinsonism. The interactions of TPPP3 have been determined and quantified in vitro with recombinant human proteins, cell extracts and in living human cells using different methods including bifunctional fluorescence complementation. The tight association of TPPP3 with TPPP1, but not with SYN, may ensure a unique mechanism for its inhibitory effect. TPPP3 or its selected fragments may become a leading agent for developing anti-Parkinson agents.

Functions and mechanisms of protein disulfide isomerase family in cancer emergence

The endoplasmic reticulum (ER) is a multi-layered organelle that is essential for the synthesis, folding, and structural maturation of almost one-third of the cellular proteome. It houses several resident proteins for these functions including the 21 members of the protein disulfide isomerase (PDI) family. The signature of proteins belonging to this family is the presence of the thioredoxin domain which mediates the formation, and rearrangement of disulfide bonds of substrate proteins in the ER. This process is crucial not only for the proper folding of ER substrates but also for maintaining a balanced ER proteostasis. The inclusion of new PDI members with a wide variety of structural determinants, size and enzymatic activity has brought additional epitomes of how PDI functions. Notably, some of them do not carry the thioredoxin domain and others have roles outside the ER. This also reflects that PDIs may have specialized functions and their functions are not limited within the ER. Large-scale expression datasets of human clinical samples have identified that the expression of PDI members is elevated in pathophysiological states like cancer. Subsequent functional interrogations using structural, molecular, cellular, and animal models suggest that some PDI members support the survival, progression, and metastasis of several cancer types. Herein, we review recent research advances on PDIs, vis-à-vis their expression, functions, and molecular mechanisms in supporting cancer growth with special emphasis on the anterior gradient (AGR) subfamily. Last, we posit the relevance and therapeutic strategies in targeting the PDIs in cancer.

CENPA is one of the potential key genes associated with the proliferation and prognosis of ovarian cancer based on integrated bioinformatics analysis and regulated by MYBL2

Background

Ovarian cancer (OV) is a highly lethal disease, and the fifth leading cause of all cancer-related deaths in women. The study aimed to identify potential key genes associated with the proliferation and prognosis of OV.

Methods

Differentially expressed genes (DEGs) between ovarian cancer and normal tissues were screened by the robust rank aggregation (RRA) method. The expression of CENPA and MYBL2 were examined in SKOV3 and A2780 ovarian cancer cell lines and tumor tissues by qRT-PCR and western blot. Small RNA interference assays, plasmid overexpression assays and EdU assays were used to validate the proliferative effect of the MYBL2-CENPA axis in ovarian cancer cell lines. The ChIP assay was used to verify the direct regulation of MYBL2 on CENPA.

Results

133 up-regulated genes and 158 down-regulated genes were identified, and the up-regulated genes mainly enrichment in cell cycle. The three up-regulated gene with DNA separation (CENPA, CENPF and CEP55) might be tightly correlated with proliferation and prognosis of OV. Knockdown CENPA expression inhibited the proliferation of A2780 and SKOV3 cells After the knockout of MYBL2, the expression of CENPA significantly decreased. MYBL2 directly binds to the promoter region of CENPA.

Conclusions

The MYBL2-CENPA pathway plays an important role in the proliferation of ovarian cancer cells, suggesting that this pathway may be a potential target for the treatment of ovarian cancer.

Estrogen-regulated AGR3 activates the estrogen receptor signaling pathway to promote tamoxifen resistance in breast cancer

PURPOSE

Anterior gradient 3 (AGR3) is associated with breast cancer progression, but its relationship with estrogen and tamoxifen resistance in breast cancer is still unclear. This study was designed to investigate the correlation of ARG3 and estrogen as well as the roles of ARG3 in tamoxifen resistance in breast cancer.

METHODS

Online database including GEPIA, UALCAN, and TCGA and rVista predictive tool were applied to analyze the expression patterns of AGR3 and its relationship with estrogen receptor 1. AGR3 knockdown and overexpression cell models were constructed. Luciferase reporter assay and ChIP were performed to investigate intermolecular interactions. Western blotting and qPCR were applied to assess targets at mRNA and protein levels, respectively. Cell counting and MTT assay were applied to determine the cell proliferation.

RESULTS

An elevation of AGR3 was observed in patients with breast cancer, especially in the patients with estrogen receptor (ER)-positive breast cancer. The TCGA dataset and in vitro data supported that AGR3 was positively correlated to ER. Further results demonstrated that ER protein bound to AGR3 promoter sites. AGR3 expression exhibited a positive correlation to cell viability. Besides, AGR3 promoted tamoxifen resistance in breast cancer.

CONCLUSION

AGR3 is associated with estrogen and promotes tamoxifen resistance in breast cancer.

The Anterior GRadient (AGR) family proteins in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the most common gynecologic disorder. Even with the recent progresses made towards the use of new therapeutics, it still represents the most lethal gynecologic malignancy in women from developed countries.

The discovery of the anterior gradient proteins AGR2 and AGR3, which are highly related members belonging to the protein disulfide isomerase (PDI) family, attracted researchers’ attention due to their putative involvement in adenocarcinoma development. This review compiles the current knowledge on the role of the AGR family and the expression of its members in EOC and discusses the potential clinical relevance of AGR2 and AGR3 for EOC diagnosis, prognosis, and therapeutics.

A better understanding of the role of the AGR family may thus provide new handling avenues for EOC patients.

Meta-analysis identifying epithelial-derived transcriptomes predicts poor clinical outcome and immune infiltrations in ovarian cancer

BACKGROUND

Previous studies revealed that the epithelial component is associated with the modulation of the ovarian tumor microenvironment (TME). However, the identification of key transcriptional signatures of laser capture microdissected human ovarian cancer epithelia remains lacking.

METHODS

We identified the differentially expressed transcriptional signatures of human ovarian cancer epithelia by meta-analysis of GSE14407, GSE2765, GSE38666, GSE40595, and GSE54388. Then we investigated the enrichment of KEGG pathways that are associated with epithelia-derived transcriptomes. Finally, we investigated the correlation of key epithelia-hub genes with the survival prognosis and immune infiltrations. Finally, we investigated the genetic alterations of key prognostic hub genes and their diagnostic efficacy in ovarian cancer epithelia.

RESULTS

We identified 1339 differentially expressed genes (DEGs) in ovarian cancer epithelia including 541upregulated and 798 downregulated genes. We identified 21 (such as E2F4, FOXM1, TFDP1, E2F1, and SIN3A) and 11 (such as JUN, DDX4, FOSL1, NOC2L, and HMGA1) master transcriptional regulators (MTRs) that are interacted with upregulated and the downregulated genes in ovarian tumor epithelium, respectively. The STRING-based analysis identified hub genes (such as CDK1, CCNB1, AURKA, CDC20, and CCNA2) in ovarian cancer epithelia. The significant clusters of identified hub genes are associated with the enrichment of KEGG pathways including cell cycle, DNA replication, cytokine-cytokine receptor interaction, pathways in cancer, and focal adhesion. The upregulation of SCNN1A and CDCA3 and the downregulation of SOX6 are correlated with a shorter survival prognosis in ovarian cancer (OV). The expression level of SOX6 is negatively correlated with immune score and positively correlated with tumor purity in OV. Moreover, SOX6 is negatively correlated with the infiltration of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages in OV. Also, SOX6 is negatively correlated with various immune markers including CD8A, PRF1, GZMA, GZMB, NKG7, CCL3, and CCL4, indicating the immune regulatory efficiency of SOX6 in the TME of OV. Furthermore, SCNN1A, CDCA3, and SOX6 genes are genetically altered in OV and the expression levels of SCNN1A and SOX6 genes showed diagnostic efficacy in ovarian cancer epithelia.

CONCLUSIONS

The identified ovarian cancer epithelial-derived key transcriptional signatures are significantly correlated with survival prognosis and immune infiltrations, and may provide new insight into the diagnosis and treatment of epithelial ovarian cancer.

AGR2 and AGR3 play an important role in the clinical characterization and prognosis of basal like breast cancer

INTRODUCTION

Basal-like Breast Cancer (BLBC) represents an important molecular subtype of breast cancer characterized by an aggressive behavior, molecular pathology poorly understood and a limited treatment.

OBJECTIVE

We aim to search for molecular differences between non-BLBC and BLBC tumors in order to propose possible diagnostic and prognostic biomarkers using databases. Metodology: Microarray processed data were downloaded from GEO database considering non-BLBC and BLBC. Enrichment analysis was evaluated using GO consortium and Ingenuity, protein-protein interaction, gene Ontology and co-expression analysis using STRING. Gene expression data was extracted using TCGA, METABRIC and Breast Cancer Gene-Expression Miner v4.2 databases. The Survival was evaluated using The Kaplan-Meier plotter.

RESULTS

Were identified 58 upregulated and 58 downregulated genes enriched in signaling pathways like PDGF, Angiogenesis, Integrin and WNT. AGR2 and AGR3 expression were reduced in BLBC in relation to non-BLBC tumors, patients aged ≤51 years, and with negativity of ER, PR and HER-2 and nodal status. Low expression of AGR2 and AGR3 were associated with worse OS and RFS for all breast cancer cases. But according to the molecular stratification, low AGR2 conferred worst OS in luminal A, worst RFS in BLBC and good OS and RFS in luminal B. High AGR3 conferred worse OS and RFS in BLBC, but low AGR3 attributed worse OS in luminal A.

CONCLUSION

AGR2 and AGR3 expression were able to differentiate non-BLBC from BLBC. Downregulation of AGR2 and AGR3 was associated with BLBC clinical phenotype. Furthermore, both genes behave different when considering prognosis and molecular stratification.

Profile of anterior gradient 3 (AGR3) mRNA expression and serum levels in benign and malignant breast tumors

BACKGROUND

Benign and malignant breast tumors are the most commonly diagnosed tumor in females. Early and accurate diagnosis of malignancy is essential for effective breast cancer treatment. Human anterior gradient 3 (AGR3) has been suggested as a potential biomarker for the early detection and prognostic determination of breast cancer.

OBJECTIVE

This study profiles AGR3 mRNA expression and serum protein levels in patients with benign and malignant breast tumors.

METHODS

A case-control study was conducted on 40 benign and 40 malignant breast tumor patients in Makassar, Indonesia. AGR3 mRNA and protein were detected using qRT-PCR and ELISA, respectively.

RESULTS

This study found significantly higher AGR3 mRNA expression in benign than malignant breast tumors using qRT-PCR (p < 0.001). In contrast, ELISA revealed no significant difference between AGR3 serum protein levels in benign and malignant breast tumors (p = 0.507).

CONCLUSIONS

AGR3 is associated with non-aggressive tumors and could be used as a marker for less aggressive breast tumors.

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.

Functional Interrogation of Enhancer Connectome Prioritizes Candidate Target Genes at Ovarian Cancer Susceptibility Loci

Identifying causal regulatory variants and their target genes from the majority of non-coding disease-associated genetic loci is the main challenge in post-Genome-Wide Association Studies (GWAS) functional studies. Although chromosome conformation capture (3C) and its derivative technologies have been successfully applied to nominate putative causal genes for non-coding variants, many GWAS target genes have not been identified yet. This study generated a high-resolution contact map from epithelial ovarian cancer (EOC) cells with two H3K27ac-HiChIP libraries and analyzed the underlying gene networks for 15 risk loci identified from the largest EOC GWAS. By combinatory analysis of 4,021 fine-mapped credible variants of EOC GWAS and high-resolution contact map, we obtained 162 target genes that mainly enriched in cancer related pathways. Compared with GTEx eQTL genes in ovarian tissue and annotated proximal genes, 132 HiChIP targets were first identified for EOC causal variants. More than half of the credible variants (CVs) involved interactions that were over 185 kb in distance, indicating that long-range transcriptional regulation is an important mechanism for the function of GWAS variants in EOC. We also found that many HiChIP gene targets showed significantly differential expressions between normal ovarian and EOC tumor samples. We validated one of these targets by manipulating the rs9303542 located region with CRISPR-Cas9 deletion and dCas9-VP64 activation experiments and found altered expression of HOXB7 and HOXB8 at 17q21.32. This study presents a systematic analysis to identify putative target genes for causal variants of EOC, providing an in-depth investigation of the mechanisms of non-coding regulatory variants in the etiology and pathogenesis of ovarian cancer.

PTOV1 promotes cisplatin-induced chemotherapy resistance by activating the nuclear factor kappa B pathway in ovarian cancer

Chemotherapy resistance is a bottleneck for ovarian cancer treatment; therefore, revealing its regulatory mechanism is critical. In the present study, we found that prostate tumor overexpressed-1 (PTOV1) was upregulated significantly in ovarian cancer cells and tissues. Patients with high PTOV1 levels had a poor outcome. In addition, PTOV1 overexpression increased CDDP (cisplatin) resistance, while PTOV1 knockdown inhibited CDDP resistance, as determined using cell viability assays, apoptosis assays, and an animal model. Mechanistic analysis showed that PTOV1 increased nuclear factor kappa B (NF-κB) pathway activity, reflected by increased nuclear translocation of its p65 subunit and the phosphorylation of inhibitor of nuclear factor kappa-B kinase subunits alpha and beta, which are markers of NF-κB pathway activation. Inhibition of the NF-κB pathway in PTOV1-overexpressing ovarian cancer cells increased CDDP-induced apoptosis, suggesting that PTOV1 promoted chemotherapy resistance by activating the NF-κB pathway. In summary, we identified PTOV1 as a prognostic factor for patients with ovarian cancer. PTOV1 might be a target for inhibition of chemotherapy resistance.

Molecular Subtyping and Outlier Detection in Human Disease Using the Paraclique Algorithm

Recent discoveries of distinct molecular subtypes have led to remarkable advances in treatment for a variety of diseases. While subtyping via unsupervised clustering has received a great deal of interest, most methods rely on basic statistical or machine learning methods. At the same time, techniques based on graph clustering, particularly clique-based strategies, have been successfully used to identify disease biomarkers and gene networks. A graph theoretical approach based on the paraclique algorithm is described that can easily be employed to identify putative disease subtypes and serve as an aid in outlier detection as well. The feasibility and potential effectiveness of this method is demonstrated on publicly available gene co-expression data derived from patient samples covering twelve different disease families.

High-throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype-specific serologically relevant biomarkers

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of BC subtypes. Five of the most representative proteomics databases currently available were also used to estimate the potential for these selected proteins to serve as putative serological markers.

AGR3 promotes estrogen receptor-positive breast cancer cell proliferation in an estrogen-dependent manner

Breast cancer is one of the most common malignancies and the leading cause of cancer-associated death among women. Anterior gradient 3 (AGR3) is a cancer-associated gene and is similar to its homologous oncogene AGR2. However, whether AGR3 participates in breast cancer progression remains unclear. The present study aimed to investigate the function of AGR3 in ER-positive breast cancer. In the present study, reverse transcription-quantitative PCR was used to detect AGR3 mRNA expression in breast cancer tissues and cell lines; linear correlation analysis was used to investigate the correlation between AGR3 and estrogen receptor 1 (ESR1) expression in breast cancer via GEO dataset analysis; western blotting was used to assess the levels of AGR3, ER and GAPDH; small interfering (si)RNA transfection was used to knock down AGR3 and ESR1 expression; and finally the Cell Counting Kit-8 assay was used to evaluate cell viability. In the present study, AGR3 expression was markedly increased in estrogen receptor (ER)-positive breast cancer tissues and cell lines compared with that in ER-negative breast cancer. AGR3 expression was upregulated in estrogen-treated T47D cells, whereas 4-hydroxytamoxifen, an inhibitor of estrogen-ER activity in breast cancer cells, downregulated AGR3 expression in T47D cells. Functional assays demonstrated that knockdown of AGR3 using siRNAs inhibited T47D cell proliferation compared with that of the negative control group. Additionally, AGR3 expression was decreased after knocking down ESR1. The present results suggested that AGR3 may serve an important role in estrogen-mediated cell proliferation in breast cancer and that AGR3 knockdown may be a potential therapeutic strategy for ER-positive breast cancer.

Elevated mRNA Expression Levels of NCAPG are Associated with Poor Prognosis in Ovarian Cancer

BACKGROUND

Ovarian cancer is a major gynecologic malignancy that is often detected at a late stage due to the lack of detailed studies on its pathogenesis and reliable biomarkers for predicting its prognosis.

MATERIALS AND METHODS

Four ovarian cancer data sets GSE18520, GSE27651, GSE40595, and GSE52037 were downloaded from the Gene Expression Omnibus (GEO) database and the robust rank aggregation approach was used to find common differentially expressed genes (DEGs). Cytoscape software was used to construct and detect key models of protein-protein interaction (PPI) network. While the expression, prognostic value and potential mechanism of the hub gene non-SMC condensin I complex subunit G (NCAPG) was carried out through Gene Expression Profiling Interactive Analysis, Kaplan-Meier plotter online dataset and gene set enrichment analysis. To further investigate the role of NCAPG in ovarian cancer, in vitro experiments were carried out.

RESULTS

A total of 232 DEGs were identified in the four GEO datasets; and we detected 32 hub genes from the PPI network and 21 of these genes were associated with ovarian cancer prognosis, one of which was NCAPG. NCAPG was significantly upregulated in most of the ovarian cancer samples. High NCAPG expression was mainly involved in homologous recombination, DNA replication, proteasome, and more correlated pathways. NCAPG knockdown arrested the cell cycle, inhibited the proliferation, and attenuated the migration ability of A2780 cells. Meanwhile, silencing of NCAPG significantly promoted cisplatin-induced apoptosis thus increased the sensitivity to cisplatin.

CONCLUSION

NCAPG together with the other 31 hub genes play a vital role in the tumorigenesis of ovarian, meanwhile, the cell cycle pathway may be a potential pathway contributing to progression in OC; and NCAPG expression can be used as a promising target for the treatment of OC.

An optimal prognostic model based on gene expression for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of RCC; however, prognostic prediction tools for ccRCC are scant. Developing mRNA or long non-coding RNA (lncRNA)-based risk assessment tools may improve the prognosis in patients with ccRCC. RNA-sequencing and prognostic data from patients with ccRCC were downloaded from The Cancer Genome Atlas and the European Bioinformatics Institute Array database at the National Center for Biotechnology Information. Differentially expressed (DE) RNAs (DERs) and prognostic DERs were screened between less favorable and favorable prognoses using the limma package in R 3.4.1, and analyzed using univariate and multivariate Cox regression analyses, respectively. Risk score models were constructed using optimal combinations of DEmRNAs and DElncRNAs identified using the Least Absolute Shrinkage And Selection Operator Cox regression model of the penalized package. Associations between risk score models and overall survival time were evaluated. Independent prognostic clinical factors were screened using univariate and multivariate Cox regression analyses, and nomogram models were constructed. Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the clusterProfiler package in R3.4.1. A total of 451 DERs were identified, including 404 mRNAs and 47 lncRNAs, between less favorable and favorable prognoses, and 269 DERs, including 233 mRNAs and 36 lncRNAs, were identified as independent prognostic factors. Optimal combinations including 10 DEmRNAs or 10 DElncRNAs were screened using four risk score models based on the status or expression levels of the 10 DEmRNAs or 10 DElncRNAs. The model based on the expression levels of the 10 DEmRNAs had the highest prognostic power. These prognostic DEmRNAs may be involved in biological processes associated with the inflammatory response, complement and coagulation cascades and neuroactive ligand-receptor interaction pathways. The present validated risk assessment tool based on the expression levels of these 10 DEmRNAs may help to identify patients with ccRCC at a high risk of mortality. These 10 DEmRNAs in optimal combinations may serve as prognostic biomarkers and help to elucidate the pathogenesis of ccRCC.

Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis

Background

High-grade serous ovarian cancer (HGSOC) is the most malignant gynecologic tumor. This study reveals biomarkers related to HGSOC incidence and progression using the bioinformatics method.

Material/Methods

Five gene expression profiles were downloaded from GEO. Differentially-expressed genes (DEGs) in HGSOC and normal ovarian tissue samples were screened using limma and the function of DEGs was annotated by KEGG and GO analysis using clusterProfiler. A co-expression network utilizing the WGCNA package was established to define several hub genes from the key module. Furthermore, survival analysis was performed, followed by expression validation with datasets from TCGA and GTEx. Finally, we used single-gene GSEA to detect the function of prognostic hub genes.

Results

Out of the 1874 DEGs detected from 114 HGSOC versus 49 normal tissue samples, 956 were upregulated and 919 were downregulated. The functional annotation indicated that upregulated DEGs were mostly enriched in cell cycle, whereas the downregulated DEGs were enriched in the MAPK or Ras signaling pathway. Two modules significantly associated with HGSOC were excavated through WGCNA. After survival analysis and expression validation of hub genes, we found that 2 upregulated genes (MAD2L1 and PKD2) and 3 downregulated genes (DOCK5, FANCD2 and TBRG1) were positively correlated with HGSOC prognosis. GSEA for single-hub genes revealed that MAD2L1 and PKD2 were associated with proliferation, while DOCK5, FANCD2, and TBRG1 were associated with immune response.

Conclusions

We found that FANCD2, PKD2, TBRG1, and DOCK5 had prognostic value and could be used as potential biomarkers for HGSOC treatment.

Microtubule-Associated Proteins with Regulatory Functions by Day and Pathological Potency at Night

The sensing, integrating, and coordinating features of the eukaryotic cells are achieved by the complex ultrastructural arrays and multifarious functions of the cytoskeleton, including the microtubule network. Microtubules play crucial roles achieved by their decoration with proteins/enzymes as well as by posttranslational modifications. This review focuses on the Tubulin Polymerization Promoting Protein (TPPP/p25), a new microtubule associated protein, on its "regulatory functions by day and pathological functions at night". Physiologically, the moonlighting TPPP/p25 modulates the dynamics and stability of the microtubule network by bundling microtubules and enhancing the tubulin acetylation due to the inhibition of tubulin deacetylases. The optimal endogenous TPPP/p25 level is crucial for its physiological functions, to the differentiation of oligodendrocytes, which are the major constituents of the myelin sheath. Pathologically, TPPP/p25 forms toxic oligomers/aggregates with α-synuclein in neurons and oligodendrocytes in Parkinson's disease and Multiple System Atrophy, respectively; and their complex is a potential therapeutic drug target. TPPP/p25-derived microtubule hyperacetylation counteracts uncontrolled cell division. All these issues reveal the anti-mitotic and α-synuclein aggregation-promoting potency of TPPP/p25, consistent with the finding that Parkinson's disease patients have reduced risk for certain cancers.

Integrated Analysis To Identify Molecular Biomarkers Of High-Grade Serous Ovarian Cancer

Purpose

Ovarian cancer is the leading cause of gynecologic cancer-related death worldwide. Early diagnosis of ovarian cancer can significantly improve patient prognosis. Hence, there is an urgent need to identify key diagnostic and prognostic biomarkers specific for ovarian cancer. Because high-grade serous ovarian cancer (HGSOC) is the most common type of ovarian cancer and accounts for the majority of deaths, we identified potential biomarkers for the early diagnosis and prognosis of HGSOC.

Methods

Six datasets (GSE14001, GSE18520, GSE26712, GSE27651, GSE40595, and GSE54388) were downloaded from the Gene Expression Omnibus database for analysis. Differentially expressed genes (DEGs) between HGSOC and normal ovarian surface epithelium samples were screened via integrated analysis. Hub genes were identified by analyzing protein-protein interaction (PPI) network data. The online Kaplan-Meier plotter was utilized to evaluate the prognostic roles of these hub genes. The expression of these hub genes was confirmed with Oncomine datasets and validated by quantitative real-time PCR and Western blotting.

Results

A total of 103 DEGs in patients with HGSOC-28 upregulated genes and 75 downregulated genes-were successfully screened. Enrichment analyses revealed that the upregulated genes were enriched in cell division and cell proliferation and that the downregulated genes mainly participated in the Wnt signaling pathway and various metabolic processes. Ten hub genes were associated with HGSOC pathogenesis. Seven overexpressed hub genes were partitioned into module 1 of the PPI network, which was enriched in the cell cycle and DNA replication pathways. Survival analysis revealed that MELK, CEP55 and KDR expression levels were significantly correlated with the overall survival of HGSOC patients (P < 0.05). The RNA and protein expression levels of these hub genes were validated experimentally.

Conclusion

Based on an integrated analysis, we propose the further investigation of MELK, CEP55 and KDR as promising diagnostic and prognostic biomarkers of HGSOC.

Identification of key biomarkers associated with development and prognosis in patients with ovarian carcinoma: evidence from bioinformatic analysis

BACKGROUND

Ovarian cancer (OC) is the deadliest cause in the gynecological malignancies. Most OC patients are diagnosed in advanced stages with less than 40% of women cured. However, the possible mechanism underlying tumorigenesis and candidate biomarkers remain to be further elucidated.

RESULTS

Gene expression profiles of GSE18520, GSE54388, and GSE27651 were available from Gene Expression Omnibus (GEO) database with a total of 91 OC samples and 22 normal ovarian (OV) tissues. Three hundred forty-nine differentially expressed genes (DEGs) were screened between OC tissues and OV tissues via GEO2R and online Venn software, followed by KEGG pathway and gene ontology (GO) enrichment analysis. The enriched functions and pathways of these DEGs contain male gonad development, cellular response to transforming growth factor beta stimulus, positive regulation of transcription from RNA polymerase II promoter, calcium independent cell-cell adhesion via plasma membrane cell adhesion molecules, extracellular matrix organization, pathways in cancer, cell cycle, cell adhesion molecules, PI3K-AKT signaling pathway, and progesterone mediated oocyte maturation. The protein-protein network (PPI) was established and module analysis was carried out using STRING and Cytoscape. Next, with PPI network analyzed by four topological methods in Cytohubba plugin of Cytoscape, 6 overlapping genes (DTL, DLGAP5, KIF15, NUSAP1, RRM2, and TOP2A) were eventually selected. GEPIA and Oncomine were implemented for validating the gene expression and all the six hub genes were highly expressed in OC specimens compared to normal OV tissues. Furthermore, 5 of 6 genes except for DTL were associated with worse prognosis using Kaplan Meier-plotter online tool and 3 of 6 genes were significantly related to clinical stages, including RRM2, DTL, and KIF15. Additionally, cBioPortal showed that TOP2A and RRM2 were the targets of cancer drugs in patients with OC, indicating the other four genes may also be potential drug targets.

CONCLUSION

Six hub genes (DTL, DLGAP5, KIF15, NUSAP1, RRM2, and TOP2A) present promising predictive value for the development and prognosis of OC and may be used as candidate targets for diagnosis and treatment of OC.

Increased expression of TET3 predicts unfavorable prognosis in patients with ovarian cancer-a bioinformatics integrative analysis

Ovarian carcinoma is a lethal gynecological malignancy. Women with ovarian cancer (OC) are highly recurrent and typically diagnosed at late stage. Ten-eleven translocation protein 3 (TET3) belongs to the family of ten-eleven translocations (TETs) which induce DNA demethylation and gene regulation in epigenetic level by converting 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). Previous studies indicated that TET3 is overexpressed in ovarian cancer tissues. However, the clinic-pathological functions and prognostic values of TET3 remain unclear. Here we performed an integrative study to identify the role of TET3 by bioinformatics analysis. The TET3 expression in ovarian cancer was assessed with Oncomine database, and validated with TCGA and GTEx database. The correlation of TET3 gene alteration and clinic-pathological functions was addressed by integrative analysis of GEO datasets. Then we showed mainly TET3 gain and diploid but less deletion in ovarian cancer by copy number alteration (CNA) or mutation analysis with cBioPortal. Furthermore, by using Kaplan-Meier plotter (K-M plotter), we evaluated that high TET3 level was associated with poor survival in ovarian cancer patients, which was validated with analysis by PrognoScan database and gene differential analyses with TCGA and GTEx. This is the first study demonstrated that elevated expression of TET3 is associated with poor clinic-pathological functions, poor prognosis, wherein TET3, which presents epigenetic changes or methylation changes, might be served as a diagnostic marker or therapeutic target for ovarian cancer.

The core genes involved in the promotion of depression in patients with ovarian cancer

The present study aimed to identify the core genes and pathways involved in depression in patients with ovarian cancer (OC) who suffer from high or low-grade depression. The dataset GSE9116 from Gene Expression Omnibus database was analyzed to identify differentially expressed genes (DEGs) in these patients. To elucidate how certain genes could promote depression in patients with OC, pathway crosstalk, protein-protein interaction (PPI) and comprehensive gene-pathway analyses were determined using WebGestalt, ToppGene and Search Tool for the Retrieval of Interacting Genes and gene ontology analysis. Key genes and pathways were extracted from the gene-pathway network, and gene expression and survival analysis were evaluated. A total of 93 DEGs were identified from GSE9116 dataset, including 84 upregulated genes and nine downregulated genes. The PPI, pathway crosstalk and comprehensive gene-pathway analyses highlighted C-C motif chemokine ligand 2 (CCL2), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), serpin family E member 1 (SERPINE1) and serpin family G member 1 (SERPING1) as core genes involved in the promotion of depression in patients with OC. These core genes were involved in the following four pathways 'Ensemble of genes encoding ECM-associated proteins including ECM-affiliated proteins', 'ECM regulators and secreted factors', 'Ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins' and 'MAPK signaling pathway and IL-17 signaling pathway'. The results from gene expression and survival analysis demonstrated that these four key genes were upregulated in patients with OC and high-grade depression and could worsen patients' survival. These results suggested that CCL2, FOS, SERPINE1 and SERPING1 may serve a crucial role in the promotion of depression in patients with OC. This finding may provide novel markers for predicting and treating depression in patients with OC; however, the underlying mechanisms remain unknown and require further investigation.

AGR3 promotes the stemness of colorectal cancer via modulating Wnt/β-catenin signalling

Cancer cells with stem cell properties have been acknowledged to be responsible for cancer initiation and progression. Wnt/β-catenin signalling is a major signal pathway promoting the stemness of cancer cells. Anterior gradient 3 (AGR3), a member of the protein disulfide isomerase (PDI) family, was found to be overexpressed in several cancers. However, the roles and mechanisms of AGR3 in colorectal cancer (CRC) have not been previously described. In our study, we find that AGR3 is highly expressed in CRC and associated with poor prognosis. Functional studies show that AGR3 promotes the stemness of CRC cells. Mechanically, AGR3 activates Wnt/β-catenin signalling and promotes the nuclear translocation of β-catenin to upregulate stemness related genes. Wnt/β-catenin signalling inhibition counteracts the promoting effect of AGR3 on cancer stemness. Moreover, the effect of AGR3 on Wnt/β-catenin signalling and cancer stemness depends on the presence of frizzled 4 (FZD4). Thus, our study first uncovers the stemness-promoting role and the oncogenic mechanism of AGR3 in CRC, which might provide a novel target for designing anti-CRC strategies.

Extracellular AGR3 regulates breast cancer cells migration via Src signaling

Human anterior gradient proteins AGR2 and AGR3 are overexpressed in a variety of adenocarcinomas and are often secreted in cancer patients' specimens, which suggests a role for AGR proteins in intra and extracellular compartments. Although these proteins exhibit high sequence homology, AGR2 is predominantly described as a pro-oncogene and a potential prognostic biomarker. However, little is known about the function of AGR3. Therefore, the aim of the present study was to investigate the role of AGR3 in breast cancer. The results demonstrated that breast cancer cells secrete AGR3. Furthermore, it was revealed that extracellular AGR3 (eAGR3) regulates tumor cell adhesion and migration. The current study indicated that the pharmacological and genetic perturbation of Src kinase signaling, through treatment with Dasatinib (protein kinase inhibitor) or investigating cells that express a dominant-negative form of Src, significantly abrogated eAGR3-mediated breast cancer cell migration. Therefore, the results indicated that eAGR3 may control tumor cell migration via activation of Src kinases. The results of the present study indicated that eAGR3 may serve as a microenvironmental signaling molecule in tumor-associated processes.

Anterior Gradient 3 Promotes Breast Cancer Development and Chemotherapy Response

PURPOSE

Anterior gradient 3 (AGR3) belongs to human anterior gradient (AGR) family. The function of AGR3 on cancer remains unknown. This research aimed to investigate if AGR3 had prognostic values in invasive ductal carcinoma (IDC) of breast cancer and could promote tumor progression.

Materials and Methods

AGR3 expression was detected in breast benign lesions, ductal carcinoma in situ and IDC by immunohistochemistry analysis. AGR3's correlations with clinicopathological features and prognosis of IDC patients were analyzed. By cell function experiments, collagen gel droplet-embedded culture drug sensitivity test and cytotoxic analysis, AGR3's impacts on proliferation, invasion ability, and chemotherapeutic drug sensitivity of breast cancer cells were also detected.

RESULTS

AGR3 was up-regulated in luminal subtype of histological grade I-II of IDC patients and positively correlated with high risks of recurrence and distant metastasis. AGR3 high expression could lead to bone or liver metastasis and predict poor prognosis of luminal B. In cell lines, AGR3 could promote proliferation and invasion ability of breast cancer cells which were consistent with clinical analysis. Besides, AGR3 could indicate poor prognosis of breast cancer patients treated with taxane but a favorable prognosis with 5-fluoropyrimidines. And breast cancer cells with AGR3 high expression were resistant to taxane but sensitive to 5-fluoropyrimidines.

CONCLUSION

AGR3 might be a potential prognostic indicator in luminal B subtype of IDC patients of histological grade I-II. And patients with AGR3 high expression should be treated with chemotherapy regimens consisting of 5-fluoropyrimidines but no taxane.

Comparing progression molecular mechanisms between lung adenocarcinoma and lung squamous cell carcinoma based on genetic and epigenetic networks: big data mining and genome-wide systems identification

Non-small-cell lung cancer (NSCLC) is the predominant type of lung cancer in the world. Lung adenocarcinoma (LADC) and lung squamous cell carcinoma (LSCC) are subtypes of NSCLC. We usually regard them as different disease due to their unique molecular characteristics, distinct cells of origin and dissimilar clinical response. However, the differences of genetic and epigenetic progression mechanism between LADC and LSCC are complicated to analyze. Therefore, we applied systems biology approaches and big databases mining to construct genetic and epigenetic networks (GENs) with next-generation sequencing data of LADC and LSCC. In order to obtain the real GENs, system identification and system order detection are conducted on gene regulatory networks (GRNs) and protein-protein interaction networks (PPINs) for each stage of LADC and LSCC. The core GENs were extracted via principal network projection (PNP). Based on the ranking of projection values, we got the core pathways in respect of KEGG pathway. Compared with the core pathways, we found significant differences between microenvironments, dysregulations of miRNAs, epigenetic modifications on certain signaling transduction proteins and target genes in each stage of LADC and LSCC. Finally, we proposed six genetic and epigenetic multiple-molecule drugs to target essential biomarkers in each progression stage of LADC and LSCC, respectively.

ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer

Background

Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable.

Materials and methods

Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples.

Results

We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA.

Conclusion

In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.

Database on spermatozoa transcriptogram of catagorised Frieswal crossbred (Holstein Friesian X Sahiwal) bulls

Bull spermatozoa contain different functional genes and many of them plays important roles in different stages of spermatogenesis, spermatozoa kinetics, fertilization as well as embryonic development. RNA deep sequencing is one of the preferred tools for absolute quantification of messenger RNA. The intention of the current study was to investigate the abundance of spermatozoal transcripts in categorized Frieswal (Holstein-Friesian X Sahiwal) crossbred bull semen through RNA deep sequencing. A total 1546561 and 1019308 numbers of reads were identified among good and poor quality bull spermatozoa based on their conception rate. Post mapping with Bos taurus reference genome identified 1,321,236 and 842,022 number of transcripts among good and poor quality RNA libraries, respectively. However, a total number of 3510 and 6759 functional transcripts were identified among good and poor quality bull spermatozoa, respectively. Most of the identified transcripts were related to spermatozoa functions, embryonic development and other functional aspects of fertilization. Wet laboratory validation of the top five selected transcripts (AKAP4, PRM1, ATP2B4, TRIM71 and SLC9B2) illustrated the significant (p < 0.01) level of expression in the good quality crossbred bull semen than the poor quality counterparts. The present study with comprehensive profiling of spermatozoal transcripts provides a useful non-invasive tool to understand the causes of as well as an effective way to predict male infertility in crossbred bulls.

Prognostic Characteristics of MACC1 Expression in Epithelial Ovarian Cancer

Recent studies have shown that overexpression of metastasis-associated in colon cancer 1 (MACC1) is significantly associated with adverse prognoses of patients with different kinds of cancer. However, the exact survival effect of MACC1 on epithelial ovarian cancer (EOC) patients has not yet been established. Thus, the objective of this study was to explore the prognostic role of MACC1 mRNA in EOC by using Kaplan-Meier (KM) plotter and ONCOMINE database. Our results indicated that MACC1 mRNA high expression was significantly associated with unfavorable overall survival (hazard ratio (HR) = 1.51 (95% confidence interval (CI): 1.21 - 1.88), P = 0.00025) and progression-free survival (HR = 1.53 (95% CI: 1.24 - 1.89), P = 5.8e-05) in EOC patients. We also found that the expression of MACC1 mRNA in EOC was 2.5 times higher than that in normal surface ovarian epithelium, which was statistically significant (P = 2.86e-7). Our results suggest that MACC1 expression might be a biomarker for poor prognosis in individual EOC patients.

Effect of lamin-A expression on migration and nuclear stability of ovarian cancer cells

OBJECTIVE

Nuclear lamina plays important roles in nuclear shape and mechanical stability. Many studies demonstrated that defects of lamin-A were associated with several diseases, but little research was found on its potential roles in ovarian cancer.

METHODS

GEPIA and GEO database were used to analyze lamin-A in ovarian tissues, followed by assessing lamin-A and prognosis of ovarian cancer patients with Kaplan-Meier plotter. Then, transient transfected HO-8910 cells with shRNA to knockdown lamin-A. Knockdown efficiency was determined by western blot, qRT-PCR and immunofluorescence. Meanwhile, lamin-A was overexpressed in HO-8910 PM cells. Then, 2D migration, 3D migration through 3 μm and 8 μm pores were carried out, followed by immunofluorescence and TEM observation.

RESULTS

Lamin-A tended to be lower in ovarian cancer, and higher expression of lamin-A was associated with better survival. After lamin-A knockdown, 2D and 3D migration (3 μm, 8 μm) abilities of HO-8910 cells were significantly increased (p < 0.001), while overexpression of lamin-A in HO-8910PM impeded migration. Meanwhile, when HO-8910 cells migrated through 3 μm pores, nuclei became strikingly elongated, and down-regulation of lamin-A promoted nuclear plasticity, making the circularity of nucleus increased. Besides, further knockdown group had the highest proportion of γ-H2AX, with micronuclei forming. Furthermore, western blot showed that the expression of BRCA1, Ku80 and Rad50 decreased significantly after further knockdown, suggesting impairment of DNA damage repair.

CONCLUSIONS

Lamin-A was down-regulated in ovarian cancer, and higher lamin-A was associated with better prognosis. Nuclei with high lamin-A were severely deformed through constricted pores. Moderate lamin-A enhanced nuclear plasticity, so as to strengthen migration ability. When lamin-A was further knockdown, ovarian cancer cells that migrated through restricted pores decreased, with DNA damage, genomic instability and impairment of DNA damage repair.

Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer

Purpose: Considerable evidence suggests that autophagy plays a crucial role in the biological processes of ovarian cancer. The aim of this study was to develop a novel autophagy-related prognostic signature for serous ovarian cancer.

Methods: A univariate Cox proportional regression model was used to analyze mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) for the purpose of selecting autophagy-related prognostic genes. A multivariate Cox proportional regression model and the survival analysis were used to develop an eight-gene prognostic signature. The multivariate Cox and stratification analysis suggested that this signature was an independent prognostic factor for serous ovarian cancer patients. Bioinformatics functions were investigated by a principal components analysis and gene set enrichment analysis (GSEA). Finally, the correlation between the prognostic signature and gene mutation status was further analyzed in serous ovarian cancer, and especially with regard to the mutation status of BRCA1 and BRCA2 (BRCA1/2) genes.

Results: Distinctly different autophagy-related gene expression profiles were identified in normal ovarian tissues and serous ovarian cancer tissues. We profiled an autophagy-related gene set and identified eight genes with significant prognostic values for serous ovarian cancer. Subsequently, an autophagy-related ovarian cancer risk signature was constructed, and patients at a high-risk or low-risk for poor prognosis were identified based on their signature. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) times than low-risk patients. GSEA results suggested an enhanced intensity of autophagy regulation in high-risk patients when compared with low-risk patients. When studied as an independent prognostic factor for serous ovarian cancer, the significant prognostic value of this signature could be seen in the stratified cohorts. For clinical use, we developed a nomogram that included the prognostic classifier and seven clinical risk factors. Additionally, we identified the 10 most frequently mutated genes found in serous ovarian cancer patients, and analyzed them for their differences in high-risk and low-risk patients. Among 293 patients, 62 had BRCA1/2 gene mutations, and this result was significantly correlated with the autophagy-related prognostic signature.

Conclusions: Our findings suggest that the eight-gene autophagy-related signature could serve as an independent prognostic indicator for cases of serous ovarian cancer.

Crystal structure of human anterior gradient protein 3

Oxidative protein folding in the endoplasmic reticulum is catalyzed by the protein disulfide isomerase family of proteins. Of the 20 recognized human family members, the structures of eight have been deposited in the PDB along with domains from six more. Three members of this family, ERp18, anterior gradient protein 2 (AGR2) and anterior gradient protein 3 (AGR3), are single-domain proteins which share sequence similarity. While ERp18 has a canonical active-site motif and is involved in native disulfide-bond formation, AGR2 and AGR3 lack elements of the active-site motif found in other family members and may both interact with mucins. In order to better define its function, the structure of AGR3 is required. Here, the recombinant expression, purification, crystallization and crystal structure of human AGR3 are described.

Nuclear receptor NR2F6 inhibition potentiates responses to PD-L1/PD-1 cancer immune checkpoint blockade

Analyzing mouse tumor models in vivo, human T cells ex vivo, and human lung cancer samples, we provide direct evidence that NR2F6 acts as an immune checkpoint. Genetic ablation of Nr2f6, particularly in combination with established cancer immune checkpoint blockade, efficiently delays tumor progression and improves survival in experimental mouse models. The target genes deregulated in intratumoral T lymphocytes upon genetic ablation of Nr2f6 alone or together with PD-L1 blockade reveal multiple advantageous transcriptional alterations. Acute Nr2f6 silencing in both mouse and human T cells induces hyper-responsiveness that establishes a non-redundant T-cell-inhibitory function of NR2F6. NR2F6 protein expression in T-cell-infiltrating human NSCLC is upregulated in 54% of the cases (n = 303) and significantly correlates with PD-1 and CTLA-4 expression. Our data define NR2F6 as an intracellular immune checkpoint that suppresses adaptive anti-cancer immune responses and set the stage for clinical validation of targeting NR2F6 for next-generation immuno-oncological regimens.

Immune checkpoints blockade (ICB) is a viable anti-cancer strategy. Here the authors show that nuclear receptor NR2F6 acts as an immune checkpoint in T cells and, using mouse models and human T cells, they show NR2F6 inhibition might improve current ICB therapy or work as an alternative therapeutic strategy.

Combination of TP53 and AGR3 to distinguish ovarian high-grade serous carcinoma from low-grade serous carcinoma

Ovarian high-grade serous carcinoma (HGSC) and low-grade serous carcinoma (LGSC) are distinct gynecologic neoplasms with diverse pathogenesis and characteristic features. They respond differently to same modalities of treatment protocol and have dissimilar prognosis. Thus, it is essential to obtain accurate differential diagnosis of HGSC and LGSC prior to clinical treatment. In the present study, mRNA expression profiles were generated from 5 HGSC and 6 LGSC specimen using HTSeq, and 699 differentially expressed genes (>2-fold difference) were identified using the DESeq R package. Dendrograms produced by unsupervised hierarchical clustering completely distinguished HGSC from LGSC. Among differentially expressed genes between HGSC and LGSC, anterior gradient homolog 3 (AGR3) was highly upregulated in LGSC compared to HGSC, which was validated by reverse transcription‑quantitative polymerase chain reaction and western blotting. Then, anti‑tumor protein 53 (TP53) and anti-AGR3 immunohistochemistry were performed on 145 HGSC and 30 LGSC samples. Consistent with previous studies, abnormal expression of TP53 (0 or ≥75% positive expression) was observed in 87.6% of HGSC and 13.3% of LGSC samples. Positive staining of AGR3 had a sensitivity of 80.0% and specificity of 89.7% for LGSC. TP53 and AGR3 were both efficient in distinguishing HGSC from LGSC (P<0.001). Receiver operating characteristic analysis revealed a similar area under the curve for AGR3 (0.848) and TP53 (0.871). Through combination of the two markers (TP53 wild‑type pattern and AGR3‑positive expression), the accuracy of differential diagnosis was up to 93.1%. These findings provide compelling evidence that differential diagnosis of HGSC and LGSC can be improved by combined application of these two markers on the basis of conventional histopathological diagnosis.

FGFRL1 Promotes Ovarian Cancer Progression by Crosstalk with Hedgehog Signaling

Fibroblast growth factor receptor-like-1 (FGFRL1) has been identified as the fifth fibroblast growth factor receptor. So far, little is known about its biological functions, particularly in cancer development. Here, for the first time, we demonstrated the roles of FGFRL1 in ovarian carcinoma (OC). An array and existing databases were used to investigate the expression profile of FGFRL1 and the relationship between FGFRL1 expression and clinicopathological parameters. FGFRL1 was significantly upregulated in OC patients, and high FGFRL1 expression was correlated with poor prognosis. In vitro cell proliferation, apoptosis and migration assays, and in vivo subcutaneous xenograft tumor models were used to determine the role of FGFRL1. Loss of function of FGFRL1 significantly influenced cell proliferation, apoptosis, and migration of OC cells in vitro and tumor growth in vivo. Chromatin immunoprecipitation PCR analysis and microarray hybridization were performed to uncover the mechanism. FGFRL1 expression could be induced by hypoxia through hypoxia-inducible factor 1α, which directly binds to the promoter elements of FGFRL1. FGFRL1 promoted tumor progression by crosstalk with Hedgehog (Hh) signaling. Taken together, FGFRL1 is a potential predictor and plays an important role in tumor growth and Hh signaling which could serve as potential therapeutic targets for the treatment of OC.

Low-grade serous carcinoma of the ovary or peritoneum

Over the past decade, the strategy for clinical trial design in making progress against epithelial cancers of the ovary/peritoneum/fallopian tube has changed dramatically. The NRG (GOG) Rare Tumor Committee has been a leader in this transformation. No longer does 'one size fit all'. Rather, separate clinical trials for rare subtypes have been developed and, in some cases, completed. An enhanced understanding of their pathologic diagnosis, molecular biology, and clinical behavior has galvanized this change. Low-grade serous carcinoma may occur de novo or following an initial diagnosis of serous tumor of low malignant potential. It is characterized by young age at diagnosis, relative chemoresistance, and prolonged survival compared with high-grade serous carcinoma. Historically, conventional chemotherapy has demonstrated very limited activity in this subtype. Hormonal therapy may provide benefit in this subtype. Preclinical studies have identified and elucidated genes and pathways-MAP kinase pathway, IGF1-R, the angiogenesis pathway, and possibly, the PI3K/AKT/mTOR pathway in low-grade serous carcinoma. To date, clinical evidence supports the activity of MEK and BRAF inhibitors and bevacizumab. Further pursuit of targeted therapy trials is clearly warranted.

RNA-Seq reveals seven promising candidate genes affecting the proportion of thick egg albumen in layer-type chickens

Eggs with a much higher proportion of thick albumen are preferred in the layer industry, as they are favoured by consumers. However, the genetic factors affecting the thick egg albumen trait have not been elucidated. Using RNA sequencing, we explored the magnum transcriptome in 9 Rhode Island white layers: four layers with phenotypes of extremely high ratios of thick to thin albumen (high thick albumen, HTA) and five with extremely low ratios (low thick albumen, LTA). A total of 220 genes were differentially expressed, among which 150 genes were up-regulated and 70 were down-regulated in the HTA group compared with the LTA group. Gene Ontology (GO) analysis revealed that the up-regulated genes in HTA were mainly involved in a wide range of regulatory functions. In addition, a large number of these genes were related to glycosphingolipid biosynthesis, focal adhesion, ECM-receptor interactions and cytokine-cytokine receptor interactions. Based on functional analysis, ST3GAL4, FUT4, ITGA2, SDC3, PRLR, CDH4 and GALNT9 were identified as promising candidate genes for thick albumen synthesis and metabolism during egg formation. These results provide new insights into the molecular mechanisms of egg albumen traits and may contribute to future breeding strategies that optimise the proportion of thick egg albumen.

Expression of protein disulfide isomerase family members correlates with tumor progression and patient survival in ovarian cancer

Objective

Protein disulfide isomerase (PDI) is an oxidoreductase that is overexpressed in several cancers. PDI family members (PDIs) play a role in various diseases including cancer. Select PDIs were reported as useful markers in other cancers but their expression in ovarian cancer has not been thoroughly assessed. We sought to evaluate the expression of PDI, PDIA6, PDIR, ERp57, ERp72 and AGR3 in ovarian cancer patient samples and examine their prognostic significance.

Methods

TMA samples from 415 tissues collected from three cancer centers (UM, USC, and KCCRI) were used to assess the expression levels of PDI family proteins using IHC.

Results

We observed significant increases in PDI (p = 9.16E-36), PDIA6 (p = 5.51E-33), PDIR (p = 1.81E-12), ERp57 (p = 9.13E-07), ERp72 (p = 3.65E-22), and AGR3 (p = 4.56E-24) expression in ovarian cancers compared to normal tissues. Expression of PDI family members also increases during disease progression (p <0.001). All PDI family members are overexpressed in serous ovarian cancer (p<0.001). However, PDI, PDIA6, PDIR, ERp72 and AGR3 are more significantly overexpressed (p<0.001) than ERp57 (p<0.05) in clear cell ovarian carcinoma. Importantly, overexpression of PDI family members is associated with poor survival in ovarian cancer (p = 0.045 for PDI, p = 0.047 for PDIR, p = 0.037 for ERp57, p = 0.046 for ERp72, p = 0.040 for AGR3) with the exception of PDIA6 (p = 0.381).

Conclusions

Our findings demonstrate that select PDI family members (PDI, PDIR, ERp72, ERp57 and AGR3) are potential prognostic markers for ovarian cancer.