Proteomics Profiling Reveals the Molecular Signatures and Potential Therapeutic Targets of Human Nasopharyngeal Carcinoma.

Nasopharyngeal carcinoma (NPC), a malignant tumor distinctly characterized by ethnic and geographic distribution, is highly prevalent in Southern China and Southeast Asia. However, the molecular mechanisms of NPC have not been fully revealed at the proteomic level. In this study, 30 primary NPC samples and 22 normal nasopharyngeal epithelial tissues were collected for proteomics analysis, and a relatively complete proteomics landscape of NPC was depicted for the first time. By combining differential expression analysis, differential co-expression analysis, and network analysis, potential biomarkers and therapeutic targets were identified. Some identified targets were verified by biological experiments. We found that 17-AAG, a specific inhibitor of the identified target heat shock protein 90 (HSP90), could be a potential therapeutic drug for NPC. Finally, consensus clustering identified two NPC subtypes with specific molecular features. The subtypes and the related molecules were verified by an independent data set and may have different progression-free survival. The results of this study provide a comprehensive understanding of the proteomics molecular signatures of NPC and provide new perspectives and inspiration for prognostic determination and treatment of NPC.

Novel model of pyroptosis-related molecular signatures for prognosis prediction of clear cell renal cell carcinoma patients.

Background: Pyroptosis is a programmed death mode of inflammatory cells, which is closely related to tumor progression and tumor immunity. Clear cell renal cell carcinoma (ccRCC) is the major pathological type of renal cell carcinoma (RCC) with poor prognosis. Many theories have tried to clarify the mechanism in the development of ccRCC, but the role of pyroptosis in ccRCC has not been well described. The main purpose of this study is to explore the role of pyroptosis in ccRCC and establish a novel prognosis prediction model of pyroptosis-related molecular signatures for ccRCC. Methods: In the present study, we made a systematical analysis of the association between ccRCC RNA transcriptome sequencing data from The Cancer Genome Atlas (TCGA) database [which included 529 ccRCC patients who were randomized in a training cohort (n=265) and an internal validation cohort (n=264)] and 40 pyroptosis-related genes (PRGs), from which four genes (CASP9, GSDME, IL1B and TIRAP) were selected to construct a molecular prediction model of PRGs for ccRCC. In addition, a cohort of 114 ccRCC patients from Shanghai Eastern Hepatobiliary Surgery Hospital (EHSH) was used as external data to verify the effectiveness of the model by immunohistochemistry. Moreover, the biological functions of the four PRGs were also verified in ccRCC 786-O and 769-P cells by Western blot (WB), CCK-8 cell proliferation, and Transwell invasion assays. Results: The model was able to differentiate high-risk patients from low-risk patients, and this differentiation was consistent with their clinical survival outcomes. In addition, the four PRGs also affected the ability of cell proliferation and invasion in ccRCC. Conclusion: The prediction model of pyroptosis-related molecular markers developed in this study may prove to be a novel understanding for ccRCC.

Single-Cell Transcriptome Analysis Reveals Development-Specific Networks at Distinct Synchronized Antral Follicle Sizes in Sheep Oocytes.

The development of the ovarian antral follicle is a complex, highly regulated process. Oocytes orchestrate and coordinate the development of mammalian ovarian follicles, and the rate of follicular development is governed by a developmental program intrinsic to the oocyte. Characterizing oocyte signatures during this dynamic process is critical for understanding oocyte maturation and follicular development. Although the transcriptional signature of sheep oocytes matured in vitro and preovulatory oocytes have been previously described, the transcriptional changes of oocytes in antral follicles have not. Here, we used single-cell transcriptomics (SmartSeq2) to characterize sheep oocytes from small, medium, and large antral follicles. We characterized the transcriptomic landscape of sheep oocytes during antral follicle development, identifying unique features in the transcriptional atlas, stage-specific molecular signatures, oocyte-secreted factors, and transcription factor networks. Notably, we identified the specific expression of 222 genes in the LO, 8 and 6 genes that were stage-specific in the MO and SO, respectively. We also elucidated signaling pathways in each antral follicle size that may reflect oocyte quality and in vitro maturation competency. Additionally, we discovered key biological processes that drive the transition from small to large antral follicles, revealing hub genes involved in follicle recruitment and selection. Thus, our work provides a comprehensive characterization of the single-oocyte transcriptome, filling a gap in the mapping of the molecular landscape of sheep oogenesis. We also provide key insights into the transcriptional regulation of the critical sizes of antral follicular development, which is essential for understanding how the oocyte orchestrates follicular development.

Deciphering molecular complexity of HCV-associated lymphoproliferation.

Recent clinical studies demonstrated the achievement of lymphoma responses in patients with Hepatitis C virus-associated indolent lymphoproliferative disorders (LPD) receiving direct-acting antivirals (DAAs) as their sole treatment. However, the molecular mechanisms underlying LPD responses to DAAs are still poorly understood. In their paper the authors provide new molecular insights on this issue, reporting intraclonal diversification and persistence of B-cell clones in most cases, despite viral eradication and beneficial clinical outcome. These provocative data suggest that the achievement of molecular response is probably not required for a 'functional cure' of these patients. Further comprehensive immunogenetic and mutational studies would be fundamental to dissect this biological puzzle and, ultimately, to refine improved treatment strategies in this setting. Commentary on: Mazzaro et al. Persistence of monoclonal B-cell expansion and intraclonal diversification despite virus eradication in patients affected by hepatitis C virus-associated lymphoproliferative disorders. Br J Haematol 2023;203:237-243.

Integrated clinical and metabolomic analysis of dengue infection shows molecular signatures associated with host-pathogen interaction in different phases of the disease.

PURPOSE: Dengue is a mosquito vector-borne disease caused by the dengue virus, which affects 125 million people globally. The disease causes considerable morbidity. The disease, based on symptoms, is classified into three characteristic phases, which can further lead to complications in the second phase. Molecular signatures that are associated with the three phases have not been well characterized. We performed an integrated clinical and metabolomic analysis of our patient cohort and compared it with omics data from the literature to identify signatures unique to the different phases. METHODS: The dengue patients are recruited by clinicians after standard-of-care diagnostic tests and evaluation of symptoms. Blood from the patients was collected. NS1 antigen, IgM, IgG antibodies, and cytokines in serum were analyzed using ELISA. Targeted metabolomics was performed using LC-MS triple quad. The results were compared with analyzed transcriptomic data from the GEO database and metabolomic data sets from the literature. RESULTS: The dengue patients displayed characteristic features of the disease, including elevated NS1 levels. TNF-α was found to be elevated in all three phases compared to healthy controls. The metabolic pathways were found to be deregulated compared to healthy controls only in phases I and II of dengue patients. The pathways represent viral replication and host response mediated pathways. The major pathways include nucleotide metabolism of various amino acids and fatty acids, biotin, etc. CONCLUSION: The results show elevated TNF-α and metabolites that are characteristic of viral infection and host response. IL10 and IFN-γ were not significant, consistent with the absence of any complications.

Molecular Determinants of Peritoneal Dissemination in Gastric Adenocarcinoma.

BACKGROUND: Peritoneal dissemination represents a poor prognostic indicator in gastric cancer. Despite a comprehensive molecular characterization of this disease, no peritoneal dissemination-specific signature has been identified, limiting the tailoring of the surgical and oncological treatments. In this review, we outline the available literature focusing on the role of the different molecular pathways involved in the acquisition of peritoneal metastatic dissemination. SUMMARY: According to our results, several molecular determinants are associated with peritoneal carcinomatosis and are involved in several cellular and molecular carcinogenetic processes. However, a comprehensive understanding of the complex molecular landscape of gastric carcinosis is still lacking. KEY MESSAGES: More efforts should be made toward the integration of molecular and histologic data to perform a risk prediction assessment of peritoneal dissemination based on molecular profiling and histological evaluation.

Metabolic reprogramming and immune regulation in viral diseases.

The recent outbreak and transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide and the ensuing coronavirus disease 2019 (COVID-19) pandemic has left us scrambling for ways to contain the disease and develop vaccines that are safe and effective. Equally important, understanding the impact of the virus on the host system in convalescent patients, healthy otherwise or with co-morbidities, is expected to aid in developing effective strategies in the management of patients afflicted with the disease. Viruses possess the uncanny ability to redirect host metabolism to serve their needs and also limit host immune response to ensure their survival. An ever-increasingly powerful approach uses metabolomics to uncover diverse molecular signatures that influence a wide array of host signalling networks in different viral infections. This would also help integrate experimental findings from individual studies to yield robust evidence. In addition, unravelling the molecular mechanisms harnessed by both viruses and tumours in their host metabolism will help broaden the repertoire of therapeutic tools available to combat viral disease.

Current Status of Predictive Biomarker Development in Metastatic Renal Cell Carcinoma.

PURPOSE OF REVIEW: In this review, we analyze the current state of research in development of new biomarkers that may be useful in managing metastatic renal cell carcinoma (mRCC) setting. RECENT FINDINGS: Combining tumor-based biomarkers (gene expression profile) and blood-based biomarkers (ctDNA, cytokines) would be helpful in acquiring information regarding RCC and might be significant in the decision-making process. Renal cell carcinoma (RCC) is the sixth most frequently diagnosed neoplasm in men and tithe in women, making it responsible for 5% and 3% of all diagnosed cancers respectively. Metastatic stage represents a non-negligible percentage at diagnosis and is characterized by poor prognosis. Despite clinical features and prognostic score could guide clinicians in therapeutic approach of this disease, biomarkers predictive of response to treatment remain an unmet need.

Phylogenomic and molecular markers based studies on Staphylococcaceae and Gemella species. Proposals for an emended family Staphylococcaceae and three new families (Abyssicoccaceae fam. nov., Salinicoccaceae fam. nov. and Gemellaceae fam. nov.) harboring four new genera, Lacicoccus gen. nov., Macrococcoides gen. nov., Gemelliphila gen. nov., and Phocicoccus gen. nov.

The family Staphylococcacae and genus Gemella contain several organisms of clinical or biotechnological importance. We report here comprehensive phylogenomic and comparative analyses on 112 available genomes from species in these taxa to clarify their evolutionary relationships and classification. In a phylogenomic tree based on 678 core proteins, Gemella species were separated from Staphylococcacae by a long branch indicating that they constitute a distinct family (Gemellaceae fam. nov.). In this tree, Staphylococcacae species formed two main clades, one encompassing the genera Aliicoccus, Jeotgalicoccus, Nosocomiicoccus and Salinicoccus (Family "Salinicoccaceae"), while the other clade consisted of the genera Macrococcus, Mammaliicoccus and Staphylococcus (Family Staphylococcaceae emend.). In this tree, species from the genera Gemella, Jeotgalicoccus, Macrococcus and Salinicoccus each formed two distinct clades. Two species clades for these genera are also observed in 16S rRNA gene trees and supported by average amino acid identity analysis. We also report here detailed analyses on protein sequences from Staphylococcaceae and Gemella genomes to identify conserved signature indels (CSIs) which are specific for different genus and family-level clades. These analyses have identified 120 novel CSIs robustly demarcating different proposed families and genera. The identified CSIs provide independent evidence that the genera Gemella, Jeotgalicoccus, Macrococcus and Salinicoccus consist of two distinct clades, which can be reliably distinguished based on multiple exclusively shared CSIs. We are proposing transfers of the species from the novel clades of the above four genera into the genera Gemelliphila gen. nov., Phocicoccus gen. nov., Macrococcoides gen. nov. and Lacicoccus gen. nov., respectively. The identified CSIs also provide strong evidence for division of Staphylococcaceae into an emended family Staphylococcaceae and two new families, Abyssicoccaceae fam. nov. and Salinicoccaceae fam. nov. All of these families can be reliably demarcated based on several exclusively shared CSIs.

Using a Network-Based Analysis Approach to Investigate the Involvement of S. aureus in the Pathogenesis of Granulomatosis with Polyangiitis.

Chronic nasal carriage of Staphylococcus aureus (SA) has been shown to be significantly higher in GPA patients when compared to healthy subjects, as well as being associated with increased endonasal activity and disease relapse. The aim of this study was to investigate SA involvement in GPA by applying a network-based analysis (NBA) approach to publicly available nasal transcriptomic data. Using these data, our NBA pipeline generated a proteinase 3 (PR3) positive ANCA associated vasculitis (AAV) disease network integrating differentially expressed genes, dysregulated transcription factors (TFs), disease-specific genes derived from GWAS studies, drug-target and protein-protein interactions. The PR3+ AAV disease network captured genes previously reported to be dysregulated in AAV associated. A subnetwork focussing on interactions between SA virulence factors and enriched biological processes revealed potential mechanisms for SA's involvement in PR3+ AAV. Immunosuppressant treatment reduced differential expression and absolute TF activities in this subnetwork for patients with inactive nasal disease but not active nasal disease symptoms at the time of sampling. The disease network generated identified the key molecular signatures and highlighted the associated biological processes in PR3+ AAV and revealed potential mechanisms for SA to affect these processes.

Transcriptome Sequencing Reveals the Mechanism behind Chemically Induced Oral Mucositis in a 3D Cell Culture Model.

Oral mucositis is a common side effect of cancer treatment, and in particular of treatment with the mTORC1 inhibitor everolimus. Current treatment methods are not efficient enough and a better understanding of the causes and mechanisms behind oral mucositis is necessary to find potential therapeutic targets. Here, we treated an organotypic 3D oral mucosal tissue model consisting of human keratinocytes grown on top of human fibroblasts with a high or low dose of everolimus for 40 or 60 h and investigated (1) the effect of everolimus on microscopic sections of the 3D cell culture for evidence of morphologic changes and (2) changes in the transcriptome by high throughput RNA-Seq analysis. We show that the most affected pathways are cornification, cytokine expression, glycolysis, and cell proliferation and we provide further details. This study provides a good resource towards a better understanding of the development of oral mucositis. It gives a detailed overview of the different molecular pathways that are involved in mucositis. This in turn provides information about potential therapeutic targets, which is an important step towards preventing or managing this common side effect of cancer treatment.

Identification of functional pathways and molecular signatures in neuroendocrine neoplasms by multi-omics analysis.

BACKGROUND: Neuroendocrine neoplasms (NENs) represent a heterogeneous class of rare tumors with increasing incidence. They are characterized by the ability to secrete peptide hormones and biogenic amines but other reliable biomarkers are lacking, making diagnosis and identification of the primary site very challenging. While in some NENs, such as the pancreatic ones, next generation sequencing technologies allowed the identification of new molecular hallmarks, our knowledge of the molecular profile of NENs from other anatomical sites is still poor. METHODS: Starting from the concept that NENs from different organs may be clinically and genetically correlated, we applied a multi-omics approach by combining multigene panel testing, CGH-array, transcriptome and miRNome profiling and computational analyses, with the aim to highlight common molecular and functional signatures of gastroenteropancreatic (GEP)-NENs and medullary thyroid carcinomas (MTCs) that could aid diagnosis, prognosis and therapy. RESULTS: By comparing genomic and transcriptional profiles, ATM-dependent signaling emerged among the most significant pathways at multiple levels, involving gene variations and miRNA-mediated regulation, thus representing a novel putative druggable pathway in these cancer types. Moreover, a set of circulating miRNAs was also selected as possible diagnostic/prognostic biomarkers useful for clinical management of NENs. CONCLUSIONS: These findings depict a complex molecular and functional landscape of NENs, shedding light on novel therapeutic targets and disease biomarkers to be exploited.

Magnetique: an interactive web application to explore transcriptome signatures of heart failure.

BACKGROUND: Despite a recent increase in the number of RNA-seq datasets investigating heart failure (HF), accessibility and usability remain critical issues for medical researchers. We address the need for an intuitive and interactive web application to explore the transcriptional signatures of heart failure with this work. METHODS: We reanalysed the Myocardial Applied Genomics Network RNA-seq dataset, one of the largest publicly available datasets of left ventricular RNA-seq samples from patients with dilated (DCM) or hypertrophic (HCM) cardiomyopathy, as well as unmatched non-failing hearts (NFD) from organ donors and patient characteristics that allowed us to model confounding factors. We analyse differential gene expression, associated pathway signatures and reconstruct signaling networks based on inferred transcription factor activities through integer linear programming. We additionally focus, for the first time, on differential RNA transcript isoform usage (DTU) changes and predict RNA-binding protein (RBP) to target transcript interactions using a Global test approach. We report results for all pairwise comparisons (DCM, HCM, NFD). RESULTS: Focusing on the DCM versus HCM contrast (DCMvsHCM), we identified 201 differentially expressed genes, some of which can be clearly associated with changes in ERK1 and ERK2 signaling. Interestingly, the signs of the predicted activity for these two kinases have been inferred to be opposite to each other: In the DCMvsHCM contrast, we predict ERK1 to be consistently less activated in DCM while ERK2 was more activated in DCM. In the DCMvsHCM contrast, we identified 149 differently used transcripts. One of the top candidates is the O-linked N-acetylglucosamine (GlcNAc) transferase (OGT), which catalyzes a common post-translational modification known for its role in heart arrhythmias and heart hypertrophy. Moreover, we reconstruct RBP - target interaction networks and showcase the examples of CPEB1, which is differentially expressed in the DCMvsHCM contrast. CONCLUSION: Magnetique ( https://shiny.dieterichlab.org/app/magnetique ) is the first online application to provide an interactive view of the HF transcriptome at the RNA isoform level and to include transcription factor signaling and RBP:RNA interaction networks. The source code for both the analyses ( https://github.com/dieterich-lab/magnetiqueCode2022 ) and the web application ( https://github.com/AnnekathrinSilvia/magnetique ) is available to the public. We hope that our application will help users to uncover the molecular basis of heart failure.

Investigation of Transcriptome Patterns in Endometrial Cancers from Obese and Lean Women.

Endometrial cancer is the most common gynaecological malignancy in developed countries. One of the largest risk factors for endometrial cancer is obesity. The aim of this study was to determine whether there are differences in the transcriptome of endometrial cancers from obese vs. lean women. Here we investigate the transcriptome of endometrial cancer between obese and lean postmenopausal women using rRNA-depleted RNA-Seq data from endometrial cancer tissues and matched adjacent non-cancerous endometrial tissues. Differential expression analysis identified 12,484 genes (6370 up-regulated and 6114 down-regulated) in endometrial cancer tissues from obese women, and 6219 genes (3196 up-regulated and 3023 down-regulated) in endometrial cancer tissues from lean women (adjusted p-value < 0.1). A gene ontology enrichment analysis revealed that the top 1000 up-regulated genes (by adjusted p-value) were enriched for growth and proliferation pathways while the top 1000 down-regulated genes were enriched for cytoskeleton restructure networks in both obese and lean endometrial cancer tissues. In this study, we also show perturbations in the expression of protein coding genes (HIST1H2BL, HIST1H3F, HIST1H2BH, HIST1H1B, TTK, PTCHD1, ASPN, PRELP, and CDH13) and the lncRNA MBNL1-AS1 in endometrial cancer tissues. Overall, this study has identified gene expression changes that are similar and also unique to endometrial cancers from obese vs. lean women. Furthermore, some of these genes may serve as prognostic biomarkers or, possibly, therapeutic targets for endometrial cancer.

Genomic molecular signatures determined characterization of Mycolicibacterium gossypii sp. nov., a fast-growing mycobacterial species isolated from cotton field soil.

A Gram-positive, acid-fast and rapidly growing rod, designated S2-37 T, that could form yellowish colonies was isolated from one soil sample collected from cotton cropping field located in the Xinjiang region of China. Genomic analyses indicated that strain S2-37 T harbored T7SS secretion system and was very likely able to produce mycolic acid, which were typical features of pathogenetic mycobacterial species. 16S rRNA-directed phylogenetic analysis referred that strain S2-37 T was closely related to bacterial species belonging to the genus Mycolicibacterium, which was further confirmed by pan-genome phylogenetic analysis. Digital DNA-DNA hybridization and the average nucleotide identity presented that strain S2-37 T displayed the highest values of 39.1% (35.7-42.6%) and 81.28% with M. litorale CGMCC 4.5724 T, respectively. And characterization of conserved molecular signatures further supported the taxonomic position of strain S2-37 T belonging to the genus Mycolicibacterium. The main fatty acids were identified as C16:0, C18:0, C20:3ω3 and C22:6ω3. In addition, polar lipids profile was mainly composed of diphosphatidylglycerol, phosphatidylethanolamine and phosphatidylinositol. Phylogenetic analyses, distinct fatty aids and antimicrobial resistance profiles indicated that strain S2-37 T represented genetically and phenotypically distinct from its closest phylogenetic neighbour, M. litorale CGMCC 4.5724 T. Here, we propose a novel species of the genus Mycolicibacterium: Mycolicibacterium gossypii sp. nov. with the type strain S2-37 T (= JCM 34327 T = CGMCC 1.18817 T).

Molecular Landscape and Therapeutic Strategies in Cholangiocarcinoma: An Integrated Translational Approach towards Precision Medicine.

Cholangiocarcinomas (CCAs) are heterogeneous biliary tract malignancies with dismal prognosis, mainly due to tumor aggressiveness, late diagnosis, and poor response to current therapeutic options. High-throughput technologies have been used as a fundamental tool in unveiling CCA molecular landscape, and several molecular classifications have been proposed, leading to various targeted therapy trials. In this review, we aim to analyze the critical issues concerning the status of precision medicine in CCA, discussing molecular signatures and clusters, related to both anatomical classification and different etiopathogenesis, and the latest therapeutic strategies. Furthermore, we propose an integrated approach comprising the CCA molecular mechanism, pathobiology, clinical and histological findings, and treatment perspectives for the ultimate purpose of improving the methods of patient allocations in clinical trials and the response to personalized therapies.

Identifying temporal molecular signatures underlying cardiovascular diseases: A data science platform.

OBJECTIVE: During cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes. Dynamic, temporally-regulated alterations of individual molecules underlie the collective response of the heart to pathological drivers and the ultimate development of pathogenesis. Advances in high-throughput omics technologies have enabled cost-effective, temporal profiling of targeted systems in animal models of human diseases. However, computational analysis of temporal patterns from omics data remains challenging. In particular, bioinformatic pipelines involving unsupervised statistical approaches to support cardiovascular investigations are lacking, which hinders one's ability to extract biomedical insights from these complex datasets. APPROACH AND RESULTS: We developed a non-parametric data analysis platform to resolve computational challenges unique to temporal omics datasets. Our platform consists of three modules. Module I preprocesses the temporal data using either cubic splines or principal component analysis (PCA), and it simultaneously accomplishes the tasks on missing data imputation and denoising. Module II performs an unsupervised classification by K-means or hierarchical clustering. Module III evaluates and identifies biological entities (e.g., molecular events) that exhibit strong associations to specific temporal patterns. The jackstraw method for cluster membership has been applied to estimate p-values and posterior inclusion probabilities (PIPs), both of which guided feature selection. To demonstrate the utility of the analysis platform, we employed a temporal proteomics dataset that captured the proteome-wide dynamics of oxidative stress induced post-translational modifications (O-PTMs) in mouse hearts undergoing isoproterenol (ISO)-induced hypertrophy. CONCLUSION: We have created a platform, CV.Signature.TCP, to identify distinct temporal clusters in omics datasets. We presented a cardiovascular use case to demonstrate its utility in unveiling biological insights underlying O-PTM regulations in cardiac remodeling. This platform is implemented in an open source R package (https://github.com/UCLA-BD2K/CV.Signature.TCP).

Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review.

Over the past few decades, the advent and development of genomic assessment methods and computational approaches have raised the hopes for identifying therapeutic targets that may aid in the treatment of glioblastoma. However, the targeted therapies have barely been successful in their effort to cure glioblastoma patients, leaving them with a grim prognosis. Glioblastoma exhibits high heterogeneity, both spatially and temporally. The existence of different genetic subpopulations in glioblastoma allows this tumor to adapt itself to environmental forces. Therefore, patients with glioblastoma respond poorly to the prescribed therapies, as treatments are directed towards the whole tumor and not to the specific genetic subregions. Genomic alterations within the tumor develop distinct radiographic phenotypes. In this regard, MRI plays a key role in characterizing molecular signatures of glioblastoma, based on regional variations and phenotypic presentation of the tumor. Radiogenomics has emerged as a (relatively) new field of research to explore the connections between genetic alterations and imaging features. Radiogenomics offers numerous advantages, including noninvasive and global assessment of the tumor and its response to therapies. In this review, we summarize the potential role of radiogenomic techniques to stratify patients according to their specific tumor characteristics with the goal of designing patient-specific therapies. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:54-69.

Biomarkers in ovarian cancer: To be or not to be.

Biomarkers are becoming increasingly important in the treatment of epithelial ovarian cancer. Recent work from many laboratories has begun to provide clinically meaningful biomarkers. This review summarizes the state of the science regarding biomarkers for stratifying early-stage patients into those who benefit from adjuvant treatment, primary debulking versus interval debulking, and specific targeted therapy. In addition, new molecular imaging technologies have been developed to allow the surgeon to resect subvisible tumor deposits. These efforts should increase clinical effectiveness while minimizing toxicities for patients.

System-based approaches as prognostic tools for glioblastoma.

BACKGROUND: The evasion of apoptosis is a hallmark of cancer. Understanding this process holistically and overcoming apoptosis resistance is a goal of many research teams in order to develop better treatment options for cancer patients. Efforts are also ongoing to personalize the treatment of patients. Strategies to confirm the therapeutic efficacy of current treatments or indeed to identify potential novel additional options would be extremely beneficial to both clinicians and patients. In the past few years, system medicine approaches have been developed that model the biochemical pathways of apoptosis. These systems tools incorporate and analyse the complex biological networks involved. For their successful integration into clinical practice, it is mandatory to integrate systems approaches with routine clinical and histopathological practice to deliver personalized care for patients. RESULTS: We review here the development of system medicine approaches that model apoptosis for the treatment of cancer with a specific emphasis on the aggressive brain cancer, glioblastoma. CONCLUSIONS: We discuss the current understanding in the field and present new approaches that highlight the potential of system medicine approaches to influence how glioblastoma is diagnosed and treated in the future.