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JOURNAL ARTICLE
Wenwen Wang, Hongjun Guo, Shengyu Wu, Shuyuan Xian, Weiwei Zhang, Ruitao Zhang, Zhihua Chen, Ke Su, Ying Zhang, Ying Zhu, Danxia Chu, Mengling Zhao, Zhihua Tang, Chunlan Zheng, Zongqiang Huang, Qian Ma, Ruixia Guo
WE aimed to reveal the correlation between ovarian cancer (OV) metastasis and cancer stemness in OV. RNA-seq data and clinical information of 591 OV samples (551 without metastasis and 40 with metastasis) were obtained from TCGA. The edgeR method was used to determine differentially expressed genes (DEGs) and transcription factors (DETFs). Then, mRNA expression-based stemness index was calculated using one-class logistic regression (OCLR). Weighted gene co-expression network analysis (WGCNA) was used to define stemness-related genes (SRGs)...
March 20, 2023: Reproductive Sciences
#22
JOURNAL ARTICLE
Zhe Zhang, Zhiyao Wei, Luyang Zhao, Chenglei Gu, Yuanguang Meng
Ovarian cancer (OC) is characterised by heterogeneity that complicates the prediction of patient survival and treatment outcomes. Here, we conducted analyses to predict the prognosis of patients from the Genomic Data Commons database and validated the predictions by fivefold cross-validation and by using an independent dataset in the International Cancer Genome Consortium database. We analysed the somatic DNA mutation, mRNA expression, DNA methylation, and microRNA expression data of 1203 samples from 599 serous ovarian cancer (SOC) patients...
December 2023: Journal of Obstetrics and Gynaecology: the Journal of the Institute of Obstetrics and Gynaecology
#23
JOURNAL ARTICLE
Zhu Zhou, Hang Jin, Jian Xu
BACKGROUND: Integrated omics analysis based on transcriptome and DNA methylation data combined with machine learning methods is very promising for the diagnosis, prognosis, and classification of cancer. In this study, the DNA methylation and gene expression data of ovarian cancer (OC) were analyzed to identify abnormally methylated differentially expressed genes (DEGs), screen potential therapeutic agents for OC, and construct a risk model based on the abnormally methylated DEGs to predict patient prognosis...
January 15, 2023: Annals of Translational Medicine
#24
JOURNAL ARTICLE
Kwong-Kwok Wong, Nicholas W Bateman, Chun Wai Ng, Yvonne T M Tsang, Charlotte S Sun, Joseph Celestino, Tri V Nguyen, Anais Malpica, R Tyler Hillman, Jianhua Zhang, P Andrew Futreal, Christine Rojas, Kelly A Conrads, Brian L Hood, Clifton L Dalgard, Matthew D Wilkerson, Neil T Phippen, Thomas P Conrads, George L Maxwell, Anil K Sood, David M Gershenson
BACKGROUND: Low-grade serous ovarian cancer (LGSOC) is a rare disease that occurs more frequently in younger women than those with high-grade disease. The current treatment is suboptimal and a better understanding of the molecular pathogenesis of this disease is required. In this study, we compared the proteogenomic analyses of LGSOCs from short- and long-term survivors (defined as < 40 and > 60 months, respectively). Our goal was to identify novel mutations, proteins, and mRNA transcripts that are dysregulated in LGSOC, particularly in short-term survivors...
December 17, 2022: Journal of Translational Medicine
#25
JOURNAL ARTICLE
Ignacio Vázquez-García, Florian Uhlitz, Nicholas Ceglia, Jamie L P Lim, Michelle Wu, Neeman Mohibullah, Juliana Niyazov, Arvin Eric B Ruiz, Kevin M Boehm, Viktoria Bojilova, Christopher J Fong, Tyler Funnell, Diljot Grewal, Eliyahu Havasov, Samantha Leung, Arfath Pasha, Druv M Patel, Maryam Pourmaleki, Nicole Rusk, Hongyu Shi, Rami Vanguri, Marc J Williams, Allen W Zhang, Vance Broach, Dennis S Chi, Arnaud Da Cruz Paula, Ginger J Gardner, Sarah H Kim, Matthew Lennon, Kara Long Roche, Yukio Sonoda, Oliver Zivanovic, Ritika Kundra, Agnes Viale, Fatemeh N Derakhshan, Luke Geneslaw, Shirin Issa Bhaloo, Ana Maroldi, Rahelly Nunez, Fresia Pareja, Anthe Stylianou, Mahsa Vahdatinia, Yonina Bykov, Rachel N Grisham, Ying L Liu, Yulia Lakhman, Ines Nikolovski, Daniel Kelly, Jianjiong Gao, Andrea Schietinger, Travis J Hollmann, Samuel F Bakhoum, Robert A Soslow, Lora H Ellenson, Nadeem R Abu-Rustum, Carol Aghajanian, Claire F Friedman, Andrew McPherson, Britta Weigelt, Dmitriy Zamarin, Sohrab P Shah
High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability1-4 patterned by distinct mutational processes5,6 , tumour heterogeneity7-9 and intraperitoneal spread7,8,10 . Immunotherapies have had limited efficacy in HGSOC11-13 , highlighting an unmet need to assess how mutational processes and the anatomical sites of tumour foci determine the immunological states of the tumour microenvironment. Here we carried out an integrative analysis of whole-genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumour sites from 42 treatment-naive patients with HGSOC...
December 2022: Nature
#26
JOURNAL ARTICLE
Anukriti Sharma, Ken B Johnson, Bihua Bie, Emily E Rhoades, Alper Sen, Yuri Kida, Jennifer Hockings, Alycia Gatta, Jacqueline Davenport, Connie Arcangelini, Jennifer Ritzu, Jennifer DeVecchio, Ron Hughen, Mei Wei, G Thomas Budd, N Lynn Henry, Charis Eng, Joseph Foss, Daniel M Rotroff
Introduction: Taxanes are a class of chemotherapeutics commonly used to treat various solid tumors, including breast and ovarian cancers. Taxane-induced peripheral neuropathy (TIPN) occurs in up to 70% of patients, impacting quality of life both during and after treatment. TIPN typically manifests as tingling and numbness in the hands and feet and can cause irreversible loss of function of peripheral nerves. TIPN can be dose-limiting, potentially impacting clinical outcomes. The mechanisms underlying TIPN are poorly understood...
January 2022: Technology in Cancer Research & Treatment
#27
JOURNAL ARTICLE
Katherine H Shutta, Roberta De Vito, Denise M Scholtens, Raji Balasubramanian
Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical foundations of GGMs are introduced with the goal of enabling the researcher to draw practical conclusions by interpreting model results. Background literature is presented, emphasizing methods recently developed for high-dimensional applications such as genomics, proteomics, or metabolomics...
September 26, 2022: Statistics in Medicine
#28
JOURNAL ARTICLE
Sina Tabakhi, Haiping Lu
Multiomics data integration is key for cancer prediction as it captures different aspects of molecular mechanisms. Nevertheless, the high-dimensionality of multi-omics data with a relatively small number of patients presents a challenge for the cancer prediction tasks. While feature selection techniques have been widely used to tackle the curse of dimensionality of multi-omics data, most existing methods have been applied to each type of omics data separately. In this paper, we propose a multi-agent architecture for feature selection, called MAgentOmics, to consider all omics data together...
July 2022: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
#29
JOURNAL ARTICLE
Can Xu, Wei Cao
DNA methylation is a widely researched epigenetic modification. It is associated with the occurrence and development of cancer and has helped evaluate patients' prognoses. However, most existing DNA methylation prognosis models have not simultaneously considered the changes of the downstream transcriptome. Methods . The RNA-Sequencing data and DNA methylation omics data of ovarian cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Consensus Cluster Plus algorithm was used to construct the methylated molecular subtypes of the ovary...
2022: Journal of Oncology
#30
JOURNAL ARTICLE
Ruoyao Zou, Qidi Jiang, Tianqiang Jin, Mo Chen, Liangqing Yao, Hongda Ding
Background: Cancer-associated fibroblasts (CAFs) are essential components of the tumor microenvironment (TME). These cells play a supportive role throughout cancer progression. Their ability to modulate the immune system has also been noted. However, there has been limited investigation of CAFs in the TME of epithelial ovarian cancer (EOC). Methods: We comprehensively evaluated the CAF landscape and its association with gene alterations, clinical features, prognostic value, and immune cell infiltration at the pan-cancer level using multi-omic data from The Cancer Genome Atlas (TCGA)...
2022: Frontiers in Immunology
#31
REVIEW
Sandra Orsulic, Joshi John, Ann E Walts, Arkadiusz Gertych
Histopathologic evaluations of tissue sections are key to diagnosing and managing ovarian cancer. Pathologists empirically assess and integrate visual information, such as cellular density, nuclear atypia, mitotic figures, architectural growth patterns, and higher-order patterns, to determine the tumor type and grade, which guides oncologists in selecting appropriate treatment options. Latent data embedded in pathology slides can be extracted using computational imaging. Computers can analyze digital slide images to simultaneously quantify thousands of features, some of which are visible with a manual microscope, such as nuclear size and shape, while others, such as entropy, eccentricity, and fractal dimensions, are quantitatively beyond the grasp of the human mind...
2022: Frontiers in Oncology
#32
JOURNAL ARTICLE
Lingyu Li, Wai-Ki Ching, Zhi-Ping Liu
Recently, identifying robust biomarkers or signatures from gene expression profiling data has attracted much attention in computational biomedicine. The successful discovery of biomarkers for complex diseases such as spontaneous preterm birth (SPTB) and high-grade serous ovarian cancer (HGSOC) will be beneficial to reduce the risk of preterm birth and ovarian cancer among women for early detection and intervention. In this paper, we propose a stable machine learning-recursive feature elimination (StabML-RFE for short) strategy for screening robust biomarkers from high-throughput gene expression data...
July 29, 2022: Computational Biology and Chemistry
#33
JOURNAL ARTICLE
Michael R Kelly, Kamila Wisniewska, Matthew J Regner, Michael W Lewis, Andrea A Perreault, Eric S Davis, Douglas H Phanstiel, Joel S Parker, Hector L Franco
The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we utilize an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We systematically probe the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells...
July 22, 2022: Nature Communications
#34
JOURNAL ARTICLE
Emilie Eliseussen, Thomas Fleischer, Valeria Vitelli
Variable selection is crucial in high-dimensional omics-based analyses, since it is biologically reasonable to assume only a subset of non-noisy features contributes to the data structures. However, the task is particularly hard in an unsupervised setting, and a priori ad hoc variable selection is still a very frequent approach, despite the evident drawbacks and lack of reproducibility. We propose a Bayesian variable selection approach for rank-based unsupervised transcriptomic analysis. Making use of data rankings instead of the actual continuous measurements increases the robustness of conclusions when compared to classical statistical methods, and embedding variable selection into the inferential tasks allows complete reproducibility...
July 18, 2022: Statistics in Medicine
#35
JOURNAL ARTICLE
Kevin Y X Wang, Gulietta M Pupo, Varsha Tembe, Ellis Patrick, Dario Strbenac, Sarah-Jane Schramm, John F Thompson, Richard A Scolyer, Samuel Muller, Garth Tarr, Graham J Mann, Jean Y H Yang
In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments...
July 4, 2022: NPJ Digital Medicine
#36
JOURNAL ARTICLE
Danian Dai, Qiang Li, Pengfei Zhou, Jianjiang Huang, Hongkai Zhuang, Hongmei Wu, Bo Chen
Most of the high-grade serous ovarian cancers (HGSOC) are accompanied by P53 mutations, which are related to the nucleotide excision repair (NER) pathway. This study aims to construct a risk signature based on NER-related genes that could effectively predict the prognosis for advanced patients with HGSOC. In our study, we found that two clusters of HGSOC with significantly different overall survival (OS) were identified by consensus clustering and principal component analysis (PCA). Then, a 7-gene risk signature (DDB2, POLR2D, CCNH, XPC, ERCC2, ERCC4, and RPA2) for OS prediction was developed subsequently based on TCGA cohort, and the risk score-based signature was identified as an independent prognostic indicator for HGSOC...
2022: Frontiers in Cell and Developmental Biology
#37
JOURNAL ARTICLE
Katia Pane, Mario Zanfardino, Anna Maria Grimaldi, Gustavo Baldassarre, Marco Salvatore, Mariarosaria Incoronato, Monica Franzese
Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family...
June 2, 2022: Biomedicines
#38
JOURNAL ARTICLE
Ke Feng Sun, Li Min Sun, Dong Zhou, Ying Ying Chen, Xi Wen Hao, Hong Ruo Liu, Xin Liu, Jing Jing Chen
Ovarian carcinomas (OCs) represent a heterogeneous group of neoplasms consisting of several entities with pathogenesis, molecular profiles, multiple risk factors, and outcomes. OC has been regarded as the most lethal cancer among women all around the world. There are at least five main types of OCs classified by the fifth edition of the World Health Organization of tumors: high-/low-grade serous carcinoma, mucinous carcinoma, clear cell carcinoma, and endometrioid carcinoma. With the improved knowledge of genome-wide association study (GWAS) and expression quantitative trait locus (eQTL) analyses, the knowledge of genomic landscape of complex diseases has been uncovered in large measure...
2022: Frontiers in Oncology
#39
REVIEW
Liang Shen, Xianquan Zhan
The mitochondrion is a very versatile organelle that participates in some important cancer-associated biological processes, including energy metabolism, oxidative stress, mitochondrial DNA (mtDNA) mutation, cell apoptosis, mitochondria-nuclear communication, dynamics, autophagy, calcium overload, immunity, and drug resistance in ovarian cancer. Multiomics studies have found that mitochondrial dysfunction, oxidative stress, and apoptosis signaling pathways act in human ovarian cancer, which demonstrates that mitochondria play critical roles in ovarian cancer...
2022: Oxidative Medicine and Cellular Longevity
#40
REVIEW
Yinan Xiao, Meiyu Bi, Hongyan Guo, Mo Li
Ovarian cancer (OC) is a heterogeneous disease with the highest mortality rate and the poorest prognosis among gynecological malignancies. Because of the absence of specific early symptoms, most OC patients are often diagnosed at late stages. Thus, improved biomarkers of OC for use in research and clinical practice are urgently needed. The last decade has seen increasingly rapid advances in sequencing and biotechnological methodologies. Consequently, multiple omics technologies, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra, have been widely applied to analyze tissue- and liquid-derived samples from OC patients...
May 2022: EBioMedicine
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