Prognostic markers articles from across Nature Portfolio

Our analysis of the spatiotemporal transcriptional features of human ovarian aging at the single-cell level identified the DNA damage response as a fundamental attribute in oocyte senescence. FOXP1, a gatekeeper both in granulosa and in theca and stroma cellular senescence, can be activated by quercetin treatment to delay ovarian aging.

Staging Alzheimer’s disease on the basis of the disease’s biological underpinnings might help with stratification and prognostication, both in the clinical setting and in clinical trials. We propose a staging model based on only five biomarkers, which are related to amyloid-β and tau pathologies in different ways and can be measured with a single sample of cerebrospinal fluid.

Iwadare et al. investigate the value of serum autotaxin levels in predicting liver-related events within a retrospective cohort of 309 biopsy-proven NAFLD patients. Multivariate Cox proportional hazards models identify autotaxin levels and advanced fibrosis as independent predictive factors.

Early identification of osteoarthritis (OA) prior to the development of symptoms is challenging. Post-traumatic OA provides a model for the development of OA following a defined event. In this Review, the authors describe the existing knowledge relating to the biomarkers of post-traumatic OA, which might also be applicable to the identification of early non-traumatic OA.

Predicting recurrence risk in non small cell lung cancer can help to guide treatment decisions. Here, the authors use CT and PET imaging to develop predictive imaging subtypes, which can be integrated with existing ctDNA methods to predict recurrence.

Our analysis of the spatiotemporal transcriptional features of human ovarian aging at the single-cell level identified the DNA damage response as a fundamental attribute in oocyte senescence. FOXP1, a gatekeeper both in granulosa and in theca and stroma cellular senescence, can be activated by quercetin treatment to delay ovarian aging.

Staging Alzheimer’s disease on the basis of the disease’s biological underpinnings might help with stratification and prognostication, both in the clinical setting and in clinical trials. We propose a staging model based on only five biomarkers, which are related to amyloid-β and tau pathologies in different ways and can be measured with a single sample of cerebrospinal fluid.

We applied an artificial intelligence (AI) approach to a dataset of clinical and advanced multi-omic molecular features from patients with pancreatic adenocarcinoma to predict survival. The results reveal a tumor-type-agnostic platform that can identify parsimonious and robust clinical prediction biomarkers, catalyzing the vision to democratize precision oncology worldwide.