Diagnosis and Prognosis

Research Goal

Since 2005 the UdS research groups of Prof. Keller, Prof. Lenhof, and Prof. Meese developed and published more than 30 novel approaches for the diagnosis and prognosis of diseases, e.g., for Alzheimer disease, different types of cancers, COPD, Multiples sclerosis, etc. Most of the approaches are based on antigen or miRNA pattern and apply machine learning methods (ML, AI).

General Approaches

Large-scale validation of miRNAs by disease association, evolutionary conservation and pathway activity

Is there a general autoantibody signature for cancer?

Toward the blood-borne miRNome of human diseases

Immunogenicity of autoantigens

Large-scale antibody profiling of human blood sera: The future of molecular diagnosis

Alzheimer Disease

Machine Learning to Detect Alzheimer’s Disease from Circulating Non-coding RNAs

Cancer

Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients

Genome-wide MicroRNA Expression Profiles in COPD: Early Predictors for Cancer Development

Combining miRNA and mRNA Expression Profiles in Wilms Tumor Subtypes

Paired proteomics, transcriptomics and miRNomics in non-small cell lung cancers: known and novel signaling cascades

Integrated quantitative proteomic and transcriptomic analysis of lung tumor and control tissue: a lung cancer showcase

Circulating serum miRNAs as potential biomarkers for nephroblastoma

Is there a general autoantibody signature for cancer?

Multicenter study identified molecular blood-born protein signatures for Wilms Tumor

Treatment-independent miRNA signature in blood of wilms tumor patients.

Stable serum miRNA profiles as potential tool for non-invasive lung cancer diagnosis

Autoantibody signature differentiates wilms tumor patients from neuroblastoma patients

Specific peripheral miRNA profiles for distinguishing lung cancer from COPD

Novel immunogenic antigens increase classification accuracy in meningioma to 93.84%

High-throughput miRNA profiling of human melanoma blood samples

Identification of lung cancer with high sensitivity and specificity by blood testing

Improving Seroreactivity-Based Detection of Glioma

miRNAs in lung cancer – Studying complex fingerprints in patient’s blood cells by microrarray experiments

Pattern of Serum Autoantibodies Allows Accurate Distinctionbetween aTumor and Pathologies of the Same Organ

Toward an early diagnosis of lung cancer: An autoantibody signature for squamous cell lung carcinoma

Increased Seroreactivity to Glioma-Expressed Antigen 2 in Brain Tumor Patients under Radiation

A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors

COPD

Low miR-150-5p and miR-320b Expression Predicts Reduced Survival of COPD Patients

Genome-wide MicroRNA Expression Profiles in COPD: Early Predictors for Cancer Development

Specific peripheral miRNA profiles for distinguishing lung cancer from COPD

Novel autoantigens immunogenic in COPD patients

Multiples Sclerosis

Multiple sclerosis: microRNA expression profiles accurately differentiate patients with relapsing-remitting disease from healthy controls