scholarly journals Proteomic Profile Mapping and Differential Expression of Protein in Ovarian Cancer

2021 ◽  
Vol 50 (12) ◽  
pp. 3667-3681
Author(s):  
Ambreen Tauseef ◽  
Asima Karim ◽  
Gulfam Ahmad ◽  
Qurratulann Afza Gardner ◽  
Muhammad Waheed Akhtar
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Ovarian Tissue ◽  
Protein Profile ◽  
Protein Profiling ◽  
Differentially Expressed ◽  
P Value ◽  
Differentially Expressed Proteins ◽  
Tissue Samples ◽  
Novel Biomarkers

This study aimed to characterize differentially expressed proteins in malignant ovarian tissue to find out potential novel biomarkers in ovarian cancer (OC). We enrolled 20 ovarian cancer patients (40-65 years) and an equal number of age-matched healthy women to get malignant and healthy ovarian tissue samples for protein extraction and quantification after tissue lysis. The protein profile was analyzed using two-dimensional gel electrophoresis followed by MALDI-TOF mass spectrometry. Based on the information thus obtained, the proteins were identified using the relevant software and protein databank to analyze the malignant and non-malignant ovarian tissue samples (n = 20/group). In this proteomic analysis of the ovarian tissue, 112 proteins were detected. Based on a minimum of ≥ 1.5-fold expression difference (p-value ≤ 0.05; FDR ≤ 0.05 and PMF ≥ 79), 17 proteins were found to be upregulated while 27 were downregulated in the malignant ovarian tissue. Six of these proteins have not been previously reported in ovarian cancer. Out of these, three are upregulated while the other three are downregulated. The upregulated proteins are centrosomal protein of 290 kDa (Cep290), uncharacterized protein C1orf109 (C1orf109) and GTPase-activating Rap/Ran-GAP domain-like protein 3 (GARNL3), and the three downregulated proteins identified are actin-related protein 3 (ARP3), cytosolic carboxypeptidase 3 (AGBL3) and NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 10 (NDUFA10). This proteomic mapping not only provides data on protein profiling of ovarian cancer in Pakistani population for the first time but also reports six novel differentially expressed proteins, which have not been previously reported in ovarian cancer patients. They may serve as potential novel biomarkers after further validation for early diagnosis and prognosis of ovarian cancer. It also provides additional data to improve existing knowledge of already reported protein ovarian cancer biomarkers.

Using adjacent to tumor samples as normal controls in molecular investigations: Are we missing the earliest biomarkers?

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 80-80
Author(s):  
Williams Fernandes Barra ◽  
Fabiano Moreira ◽  
Arthur Ribeiro-dos-Santos ◽  
Sidney Santos ◽  
Andrea KCR Santos ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Illumina Miseq ◽  
Differentially Expressed ◽  
P Value ◽  
Tissue Samples ◽  
Tumor Sample ◽  
Molecular Alterations ◽  
Molecular Events ◽  
Mirna Sequencing ◽  
Normal Controls

80 Background: Recently, some papers aiming to warn the scientific community regarding possible misinterpretation due to using adjacent to tumor sample tissues as normal controls were published The main motivation for these alerts was the observation of important molecular alterations in tissues nearby a cancer, although been collected from non-tumor areas. Nevertheless the cancer field hypothesis has been recognized many years ago; this concept was not incorporated to current practice in molecular investigations. In this work we aim to investigate potential implications of using adjacent to tumor sample tissues as normal controls. Methods: Gastric Cancer (GC) and Adjacent Tissues (AT) paired fresh samples of 16 patients, and 16 samples from non-cancer patients (NC) were analyzed. miRNA sequencing was performed using Illumina Miseq platform. Statistical analysis was performed in R using DESeq2 tool to identify differential expressed miRNAs. Results: Comparing GC with NC tissue samples, we observed 21 miRNAs differentially expressed (p-value < 0.05 and |Log2(Fold-Change)| > 2), eight were down-regulated and 13 were up-regulated in GC. Comparing AT with NC tissue samples, 16 miRNAs were differentially expressed, two were down-regulated and 14 were up-regulated in AT. In both comparisons, GC and AT samples clustered together and clearly separated from NC samples. Comparing GC with AT samples, hsa-miR-196a-5p was significantly down-regulated in GC. Conclusions: AT tissues harbor molecular alterations distinguishing them from non-cancer patient’s samples. Combined analyses of the groups clearly demonstrated that AT resembles much more cancer profile than NC patient’s tissue prolife. Using AT samples as controls might impair the discovery of possible initial molecular events identifiable exclusively by using samples from non-cancer patients. The current strategy of molecular investigation must be revised.

Expression of CD24 in plasma, exosome and ovarian tissue samples of serous ovarian cancer patients

2019 ◽  
Vol 298 ◽  
pp. 16-20 ◽  
Author(s):  
Beáta Soltész ◽  
János Lukács ◽  
Edina Szilágyi ◽  
Éva Márton ◽  
Melinda Szilágyi Bónizs ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Ovarian Tissue ◽  
Serous Ovarian Cancer ◽  
Tissue Samples

scholarly journals MicroRNA-based signatures impacting clinical course and biology of ovarian cancer: a miRNOmics study

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
E. Krasniqi ◽  
A. Sacconi ◽  
D. Marinelli ◽  
L. Pizzuti ◽  
M. Mazzotta ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Ovarian Tissue ◽  
Gynecological Cancer ◽  
Enrichment Analysis ◽  
Platinum Resistance ◽  
Tissue Samples ◽  
Ribonucleic Acids ◽  
Survival Endpoints ◽  
Novel Biomarkers

Abstract Background In Western countries, ovarian cancer (OC) still represents the leading cause of gynecological cancer-related deaths, despite the remarkable gains in therapeutical options. Novel biomarkers of early diagnosis, prognosis definition and prediction of treatment outcomes are of pivotal importance. Prior studies have shown the potentials of micro-ribonucleic acids (miRNAs) as biomarkers for OC and other cancers. Methods We focused on the prognostic and/or predictive potential of miRNAs in OC by conducting a comprehensive array profiling of miRNA expression levels in ovarian tissue samples from 17 non-neoplastic controls, and 60 tumor samples from OC patients treated at the Regina Elena National Cancer Institute (IRE). A set of 54 miRNAs with differential expression in tumor versus normal samples (T/N-deregulated) was identified in the IRE cohort and validated against data from the Cancer Genoma Atlas (TCGA) related to 563 OC patients and 8 non-neoplastic controls. The prognostic/predictive role of the selected 54 biomarkers was tested in reference to survival endpoints and platinum resistance (P-res). Results In the IRE cohort, downregulation of the 2 miRNA-signature including miR-99a-5p and miR-320a held a negative prognostic relevance, while upregulation of miR-224-5p was predictive of less favorable event free survival (EFS) and P-res. Data from the TCGA showed that downregulation of 5 miRNAs, i.e., miR-150, miR-30d, miR-342, miR-424, and miR-502, was associated with more favorable EFS and overall survival outcomes, while miR-200a upregulation was predictive of P-res. The 9 miRNAs globally identified were all included into a single biologic signature, which was tested in enrichment analysis using predicted/validated miRNA target genes, followed by network representation of the miRNA-mRNA interactions. Conclusions Specific dysregulated microRNA sets in tumor tissue showed predictive/prognostic value in OC, and resulted in a promising biological signature for this disease.

Ovarian Cancer: Differentially Expressed microRNAs in Tumor Tissue and Cell-Free Ascitic Fluid as Potential Novel Biomarkers

2019 ◽  
Vol 37 (9) ◽  
pp. 440-452 ◽  
Author(s):  
Luděk Záveský ◽  
Eva Jandáková ◽  
Vít Weinberger ◽  
Luboš Minář ◽  
Veronika Hanzíková ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Ascitic Fluid ◽  
Tumor Tissue ◽  
Differentially Expressed ◽  
Novel Biomarkers

scholarly journals ITRAQ-based quantitative proteomic analysis of Fusarium moniliforme (Fusarium verticillioides) in response to Phloridzin inducers

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Rong Zhang ◽  
Weitao Jiang ◽  
Xin Liu ◽  
Yanan Duan ◽  
Li Xiang ◽  
...  
Keyword(s):  
Fusarium Moniliforme ◽  
Abiotic Factors ◽  
Fusarium Verticillioides ◽  
Protein Profile ◽  
Sugar Metabolism ◽  
Differentially Expressed ◽  
Pcr Analysis ◽  
Differentially Expressed Proteins ◽  
Apple Replant Disease ◽  
Qrt Pcr

Abstract Background Apple replant disease (ARD) has been reported from all major fruit-growing regions of the world, and is often caused by biotic factors (pathogen fungi) and abiotic factors (phenolic compounds). In order to clarify the proteomic differences of Fusarium moniliforme under the action of phloridzin, and to explore the potential mechanism of F. moniliforme as the pathogen of ARD, the role of Fusarium spp. in ARD was further clarified. Methods In this paper, the quantitative proteomics method iTRAQ analysis technology was used to analyze the proteomic differences of F. moniliforme before and after phloridzin treatment. The differentially expressed protein was validated by qRT-PCR analysis. Results A total of 4535 proteins were detected, and 293 proteins were found with more than 1.2 times (P< 0.05) differences. In-depth data analysis revealed that 59 proteins were found with more than 1.5 times (P< 0.05) differences, and most proteins were consistent with the result of qRT-PCR. Differentially expressed proteins were influenced a variety of cellular processes, particularly metabolic processes. Among these metabolic pathways, a total of 8 significantly enriched KEGG pathways were identified with at least 2 affiliated proteins with different abundance in conidia and mycelium. Functional pathway analysis indicated that up-regulated proteins were mainly distributed in amino sugar, nucleotide sugar metabolism, glycolysis/ gluconeogenesis and phagosome pathways. Conclusions This study is the first to perform quantitative proteomic investigation by iTRAQ labeling and LC-MS/MS to identify differentially expressed proteins in F. moniliforme under phloridzin conditions. The results confirmed that F. moniliforme presented a unique protein profile that indicated the adaptive mechanisms of this species to phloridzin environments. The results deepened our understanding of the proteome in F. moniliforme in response to phloridzin inducers and provide a basis for further exploration for improving the efficiency of the fungi as biocontrol agents to control ARD.

Epigenetic Mechanism and Survival Prognosis Analysis of Serum Exosomes from Ovarian Cancer Patients based on Sequencing Technology and Bioinformatics

2021 ◽  
Author(s):  
Li Xia ◽  
Huang He
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Target Genes ◽  
Epigenetic Modification ◽  
Gene Prediction ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Survival Prognosis ◽  
Signaling Axis ◽  
Serum Exosomes

Abstract Backguound: To screen the signaling axis of epigenetic modification in serum exosomes of ovarian cancer patients based on sequencing technology and raw signal analysis, in depth study of the potential mechanism of action of ovarian cancer, prediction of potential therapeutic targets and survival prognosis analysis of potential targets.Methods: Serum exosomes from three ovarian cancer patients were selected as the experimental group, and serum exosomes from three uterine fibroid patients as the control group, and whole transcriptome of serum exosomes was performed to obtain differentially expressed lncRNA and mRNA in ovarian cancer,The miRcode database and miRNA target gene prediction website were used to predict the target genes, Cytoscape software was used to draw a ceRNA network model of epigenetic modification of ovarian cancer serum exosomes, and the R language was used for GO and KEGG enrichment analysis of the target genes. Finally, the TCGA website was used to download clinical and expression data related to ovarian cancer, and the common potential target genes obtained in the previous period were analyzed for survival。Results: A total of 117 differentially expressed lncRNAs as well as 513 differentially expressed mRNAs (P < 0.05, |log2 FC|≥ 1.0) were obtained by combining sequencing data and raw signal analysis, and 841 predicted target genes were reciprocally mapped by combining mircode database and miRNA target gene prediction website, resulting in 11 potential target genes related to ovarian cancer (FGFR3, BMPR1B, TRIM29, FBN2, PAPPA, CCDC58, IGSF3, FBXO10, GPAM, HOXA10, LHFPL4), and survival prognosis analysis of the above 11 target genes revealed that the survival curve was statistically significant (P < 0.05) for HOXA10 only genes, but not for the other genes, and through enrichment analysis, we found that the above target genes were mainly involved in biological processes such as regulation of transmembrane receptor protein kinase activity, structural molecule activity with elasticity, transforming growth factor - activated receptor activity, and GABA receptor binding, and were mainly enriched in signaling pathways regulating stem cell pluripotency, bladder cancer, glycerolipid metabolism, central carbon metabolism of cancer, tyrosine stimulation to EGFR in signaling pathways such as resistance to enzyme inhibitors.Conclusions: The serum exosomal DIO3OS-hsa-miR-27a-3p-HOXA10 epigenetic modification signaling axis affects ovarian cancer development and disease survival prognosis by targeting transcriptional dysregulation pathways in cancer.

scholarly journals Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

2021 ◽  
Vol 8 ◽  
Author(s):  
Tingshan He ◽  
Liwen Huang ◽  
Jing Li ◽  
Peng Wang ◽  
Zhiqiao Zhang
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Transcription Factors ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Immune Genes ◽  
Immune Biomarkers

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

scholarly journals Correlation of Tumor Marker Cancer Antigen (CA-125) against Hemoglobin, Leucocytes, and Platelet Lymphocyte Ratio in Ovarian Cancer Patients at RSUD ULIN Banjarmasin

2018 ◽  
Vol 7 (1) ◽  
pp. 93
Author(s):  
Hermin Sabarudin
Keyword(s):  
Ovarian Cancer ◽  
Tumor Marker ◽  
Cancer Patients ◽  
Negative Relationship ◽  
Sampling Technique ◽  
Ca 125 ◽  
P Value ◽  
Cross Sectional ◽  
Normality Test ◽  
Limitation Method

CA-125 is the most commonly used tumor marker in ovarian cancer, its known as the "Gold Standard" for the diagnosis of ovarian cancer. CA-125 allegely related to the inflammatory mechanisms which associated with leukocytes and lymphocytes, as well as Lymphocyte Platelet Ratio. This study is to prove above hypothesis. The objective is to investigate CA-125 correlation as tumor marker of inflammatory hematology examination which consisting of Hemoglobin, leucocytes and PLR as indicator of inflammatory examination in ovarian cancer patient. The method is an observational analytic study with cross sectional retrospective descriptive. The sample is 42 patients medical record who are treated in Oncology Polyclinic RSUD ULIN in October 2014 - October 2017 period. Samples selected by time limitation method. Sampling technique using nonpropability method followed by purposive sampling method. After the data is taken and selected then processed into SPSS for normality test and continued by multiple linear regression test. The results showed that correlation analysis of CA-125 tumor marker on Hb, leukocyte and PLR levels was P = 858b; P value <0.05. The CA-125 relationship to Hb, leucocytes and PLR is a negative relationship (r Hb = -3,463, r leukocytes = -6,117, r PLR = -2.281), which means that more high the Hb, leukocyte and PLR value, conversely CA125 value become more low. The conclusions of this study is there no correlation of CA125 tumor marker against Hb, leucocytes and PLR in ovarian cancer patients. The results of this study indicate that the increasing of CA125 value is negative correlation because it is not followed by decreasing of Hb, increasing of leukocyte and PLR. 

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