Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer

Immunological Roles of CCL18 in Pan‑Cancer and Its Potential Value in Endometrial Cancer

Endometrial cancer (EC) is one of the most prevalent malignancies in the female reproductive system. However, the potential functions and mechanisms of immune-related genes in the onset and progression of EC remain unclear. The immune-related gene CCL18 has been implicated in apoptosis, proliferation, invasion, metastasis, and drug resistance in various types of tumors. Nevertheless, its role in pan-cancer has been poorly investigated, and its expression value and prognostic significance in endometrial cancer (EC) have not been explored. Therefore, the objective of this study was to identify potential immune-related prognostic biomarkers for EC by utilizing the cancer genome atlas (TCGA), immunology database and analysis portal (ImmPort) database, and Gene Expression Omnibus (GEO). Immunohistochemistry staining results from EC tissue chips demonstrated elevated expression levels of inflammatory chemokine protein 18 (CCL18) in EC compared to normal endometrium. This study offers a potential therapeutic strategy for EC treatment by identifying regulatory targets through microRNA sequencing data. Additionally, drug prediction was based on CCL18 targets. Furthermore, an analysis of CCL18 expression in pan-cancer was conducted, and the results revealed its high expression in various types of cancer, including EC and bladder cancer. Through analysis of the ATAC-seq data, we found that SIX1, SOX3, and TWIST2 may regulate CCL18 transcription by binding to the gene promoter of CCL18 in EC. This study indicated that CCL18 could be a potential biomarker in pan-cancer and EC.

GBAP1 functions as a tumor promotor in hepatocellular carcinoma via the PI3K/AKT pathway

Hepatocellular carcinoma (HCC) is common worldwide, and novel therapeutic targets and biomarkers are needed to improve outcomes. In this study, bioinformatics analyses combined with in vitro and in vivo assays were used to identify the potential therapeutic targets. Differentially expressed genes (DEG) in HCC were identified by the intersection between The Cancer Genome Atlas and International Cancer Genome Consortium data. The DEGs were evaluated by a gene set enrichment analysis as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. A protein interaction network, univariate Cox regression, and Lasso regression were used to screen out hub genes correlated with survival. Increased expression of the long noncoding RNA GBAP1 in HCC was confirmed in additional datasets and its biological function was evaluated in HCC cell lines and nude mice. Among 121 DEGs, GBAP1 and PRC1 were identified as hub genes with significant prognostic value. Overexpression of GBAP1 in HCC was confirmed in 21 paired clinical tissues and liver cancer or normal cell lines. The inhibition of GBAP1 expression reduced HCC cell proliferation and promoted apoptosis by inactivating the PI3K/AKT pathway in vitro and in vivo. Therefore, GBAP1 has a pro-oncogenic function in HCC and is a candidate prognostic biomarker and therapeutic target.

A radiogenomics application for prognostic profiling of endometrial cancer

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

A multi-objective genetic algorithm to find active modules in multiplex biological networks

The identification of subnetworks of interest—or active modules—by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease.

Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html.

Contact:anais.baudot@univ-amu.fr

Integrating different sources of biological information is a powerful way to uncover the functioning of biological systems. In network biology, in particular, integrating interaction data with expression profiles helps contextualizing the networks and identifying subnetworks of interest, aka active modules. We here propose MOGAMUN, a multi-objective genetic algorithm that optimizes both the overall deregulation and the density to identify active modules, considering jointly multiple sources of biological interactions. We demonstrate the performance of MOGAMUN over state-of-the-art methods, and illustrate its usefulness in unveiling perturbed biological processes in Facio-Scapulo-Humeral muscular Dystrophy.

Comparative Genomic Analysis of the DUF34 Protein Family Suggests Role as a Metal Ion Chaperone or Insertase

Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as "GTP cyclohydrolase I type 2" through electronic propagation based on one study. Here, the annotation status of this protein family was examined through a comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox, and universal stress responses.

Patient-derived organoids reflect the genetic profile of endometrial tumors and predict patient prognosis

Background

A major hurdle in translational endometrial cancer (EC) research is the lack of robust preclinical models that capture both inter- and intra-tumor heterogeneity. This has hampered the development of new treatment strategies for people with EC.

Methods

EC organoids were derived from resected patient tumor tissue and expanded in a chemically defined medium. Established EC organoids were orthotopically implanted into female NSG mice. Patient tissue and corresponding models were characterized by morphological evaluation, biomarker and gene expression and by whole exome sequencing. A gene signature was defined and its prognostic value was assessed in multiple EC cohorts using Mantel-Cox (log-rank) test. Response to carboplatin and/or paclitaxel was measured in vitro and evaluated in vivo. Statistical difference between groups was calculated using paired t-test.

Results

We report EC organoids established from EC patient tissue, and orthotopic organoid-based patient-derived xenograft models (O-PDXs). The EC organoids and O-PDX models mimic the tissue architecture, protein biomarker expression and genetic profile of the original tissue. Organoids show heterogenous sensitivity to conventional chemotherapy, and drug response is reproduced in vivo. The relevance of these models is further supported by the identification of an organoid-derived prognostic gene signature. This signature is validated as prognostic both in our local patient cohorts and in the TCGA endometrial cancer cohort.

Conclusions

We establish robust model systems that capture both the diversity of endometrial tumors and intra-tumor heterogeneity. These models are highly relevant preclinical tools for the elucidation of the molecular pathogenesis of EC and identification of potential treatment strategies.

To study the biology of cancer and test new potential treatments, it is important to use models that mimic patients’ tumors. Such models have largely been lacking in endometrial cancer. We therefore aimed to developing miniature tumors, called “organoids”, directly from patient tumor tissue. Our organoids maintained the characteristics and genetic features of the tumors from which they were derived, would grow into endometrial tumors in mice, and exhibited patient-specific responses to chemotherapy drugs. In summary, we have developed models that will help us better understand the biology of endometrial tumors and can be used to potentially identify new effective drugs for endometrial cancer patients.

Berg et al. establish a panel of patient-derived endometrial cancer organoids and xenograft models. They show that their models recapitulate the genetic profile of the donor tumor and can be used for drug testing and development of a prognostic gene signature.

Y-Box Binding Protein-1 Promotes Epithelial-Mesenchymal Transition in Sorafenib-Resistant Hepatocellular Carcinoma Cells

Hepatocellular carcinoma is one of the most common cancer types worldwide. In cases of advanced-stage disease, sorafenib is considered the treatment of choice. However, resistance to sorafenib remains a major obstacle for effective clinical application. Based on integrated phosphoproteomic and The Cancer Genome Atlas (TCGA) data, we identified a transcription factor, Y-box binding protein-1 (YB-1), with elevated phosphorylation of Ser102 in sorafenib-resistant HuH-7^R cells. Phosphoinositide-3-kinase (PI3K) and protein kinase B (AKT) were activated by sorafenib, which, in turn, increased the phosphorylation level of YB-1. In functional analyses, knockdown of YB-1 led to decreased cell migration and invasion in vitro. At the molecular level, inhibition of YB-1 induced suppression of zinc-finger protein SNAI1 (Snail), twist-related protein 1 (Twist1), zinc-finger E-box-binding homeobox 1 (Zeb1), matrix metalloproteinase-2 (MMP-2) and vimentin levels, implying a role of YB-1 in the epithelial-mesenchymal transition (EMT) process in HuH-7^R cells. Additionally, YB-1 contributes to morphological alterations resulting from F-actin rearrangement through Cdc42 activation. Mutation analyses revealed that phosphorylation at S102 affects the migratory and invasive potential of HuH-7^R cells. Our collective findings suggest that sorafenib promotes YB-1 phosphorylation through effect from the EGFR/PI3K/AKT pathway, leading to significant enhancement of hepatocellular carcinoma (HCC) cell metastasis. Elucidation of the specific mechanisms of action of YB-1 may aid in the development of effective strategies to suppress metastasis and overcome resistance.

Integrating pathology, chromosomal instability and mutations for risk stratification in early-stage endometrioid endometrial carcinoma

Background

Risk stratifications for endometrial carcinoma (EC) depend on histopathology and molecular pathology. Histopathological risk stratification lacks reproducibility, neglects heterogeneity and contributes little to surgical procedures. Existing molecular stratification is useless in patients with specific pathological or molecular characteristics and cannot guide postoperative adjuvant radiotherapies. Chromosomal instability (CIN), the numerical and structural alterations of chromosomes resulting from ongoing errors of chromosome segregation, is an intrinsic biological mechanism for the evolution of different prognostic factors of histopathology and molecular pathology and may be applicable to the risk stratification of EC.

Results

By analyzing CIN25 and CIN70, two reliable gene expression signatures for CIN, we found that EC with unfavorable prognostic factors of histopathology or molecular pathology had serious CIN. However, the POLE mutant, as a favorable prognostic factor, had elevated CIN signatures, and the CTNNB1 mutant, as an unfavorable prognostic factor, had decreased CIN signatures. Only if these two mutations were excluded were CIN signatures strongly prognostic for outcomes in different adjuvant radiotherapy subgroups. Integrating pathology, CIN signatures and POLE/CTNNB1 mutation stratified stageIendometrioid EC into four groups with improved risk prognostication and treatment recommendations.

Conclusions

We revealed the possibility of integrating histopathology and molecular pathology by CIN for risk stratification in early-stage EC. Our integrated risk model deserves further improvement and validation.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.