Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review.

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

Discovery and systematic assessment of early biomarkers that predict progression to severe COVID-19 disease.

BACKGROUND: The clinical course of COVID-19 patients ranges from asymptomatic infection, via mild and moderate illness, to severe disease and even fatal outcome. Biomarkers which enable an early prediction of the severity of COVID-19 progression, would be enormously beneficial to guide patient care and early intervention prior to hospitalization. METHODS: Here we describe the identification of plasma protein biomarkers using an antibody microarray-based approach in order to predict a severe cause of a COVID-19 disease already in an early phase of SARS-CoV-2 infection. To this end, plasma samples from two independent cohorts were analyzed by antibody microarrays targeting up to 998 different proteins. RESULTS: In total, we identified 11 promising protein biomarker candidates to predict disease severity during an early phase of COVID-19 infection coherently in both analyzed cohorts. A set of four (S100A8/A9, TSP1, FINC, IFNL1), and two sets of three proteins (S100A8/A9, TSP1, ERBB2 and S100A8/A9, TSP1, IFNL1) were selected using machine learning as multimarker panels with sufficient accuracy for the implementation in a prognostic test. CONCLUSIONS: Using these biomarkers, patients at high risk of developing a severe or critical disease may be selected for treatment with specialized therapeutic options such as neutralizing antibodies or antivirals. Early therapy through early stratification may not only have a positive impact on the outcome of individual COVID-19 patients but could additionally prevent hospitals from being overwhelmed in potential future pandemic situations.

We aimed to identify components of the blood present during the early phase of SARS-CoV-2 infection that distinguish people who are likely to develop severe symptoms of COVID-19. Blood from people who later developed a mild or moderate course of disease were compared to blood from people who later had a severe or critical course of disease. Here, we identified a combination of three proteins that were present in the blood of patients with COVID-19 who later developed a severe or critical disease. Identifying the presence of these proteins in patients at an early stage of infection could enable physicians to treat these patients early on to avoid progression of the disease.

Stress signaling in breast cancer cells induces matrix components that promote chemoresistant metastasis.

Metastatic progression remains a major burden for cancer patients and is associated with eventual resistance to prevailing therapies such as chemotherapy. Here, we reveal how chemotherapy induces an extracellular matrix (ECM), wound healing, and stem cell network in cancer cells via the c-Jun N-terminal kinase (JNK) pathway, leading to reduced therapeutic efficacy. We find that elevated JNK activity in cancer cells is linked to poor clinical outcome in breast cancer patients and is critical for tumor initiation and metastasis in xenograft mouse models of breast cancer. We show that JNK signaling enhances expression of the ECM and stem cell niche components osteopontin, also called secreted phosphoprotein 1 (SPP1), and tenascin C (TNC), that promote lung metastasis. We demonstrate that both SPP1 and TNC are direct targets of the c-Jun transcription factor. Exposure to multiple chemotherapies further exploits this JNK-mediated axis to confer treatment resistance. Importantly, JNK inhibition or disruption of SPP1 or TNC expression sensitizes experimental mammary tumors and metastases to chemotherapy, thus providing insights to consider for future treatment strategies against metastatic breast cancer.

Tenascin C in metastasis: A view from the invasive front.

The extracellular matrix protein tenascin C (TNC) is a large glycoprotein expressed in connective tissues and stem cell niches. TNC over-expression is repeatedly observed in cancer, often at the invasive tumor front, and is associated with poor clinical outcome in several malignancies. The link between TNC expression and poor survival in cancer patients suggests a role for TNC in metastatic progression, which is responsible for the majority of cancer related deaths. Indeed, functional studies using mouse models are revealing new roles of TNC in cancer progression and underscore its important contribution to the development of metastasis. TNC has a pleiotropic role in advancing metastasis by promoting migratory and invasive cell behavior, angiogenesis and cancer cell viability under stress. TNC is an essential component of the metastatic niche and modulates stem cell signaling within the niche. This may be crucial for the fitness of disseminated cancer cells confronted with a foreign environment in secondary organs, that can exert a strong selective pressure on invading cells. TNC is a compelling example of how an extracellular matrix protein can provide a molecular context that is imperative to cancer cell fitness in metastasis.

Cis-regulatory properties of medaka synexpression groups.

During embryogenesis, tissue specification is triggered by the expression of a unique combination of developmental genes and their expression in time and space is crucial for successful development. Synexpression groups are batteries of spatiotemporally co-expressed genes that act in shared biological processes through their coordinated expression. Although several synexpression groups have been described in numerous vertebrate species, the regulatory mechanisms that orchestrate their common complex expression pattern remain to be elucidated. Here we performed a pilot screen on 560 genes of the vertebrate model system medaka (Oryzias latipes) to systematically identify synexpression groups and investigate their regulatory properties by searching for common regulatory cues. We find that synexpression groups share DNA motifs that are arranged in various combinations into cis-regulatory modules that drive co-expression. In contrast to previous assumptions that these genes are located randomly in the genome, we discovered that genes belonging to the same synexpression group frequently occur in synexpression clusters in the genome. This work presents a first repertoire of synexpression group common signatures, a resource that will contribute to deciphering developmental gene regulatory networks.