Evidence map›Paper›PMID 40004464›Full record

ReviewGenes2025

Genomic and Transcriptomic Approaches Advance the Diagnosis and Prognosis of Neurodegenerative Diseases.

Zheng Liu, Si-Yuan Song

Abstract readReview
In one paragraph

Review in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. International journal of microbiology · 2026
    Review
  8. Review
  9. Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Zheng LiuPathology Department, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0003-3264-7119
Si-Yuan SongDepartment of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS), represent a growing societal challenge due to their irreversible progression and significant impact on patients, caregivers, and healthcare systems. Despite advances in clinical and imaging-based diagnostics, these diseases are often detected at advanced stages, limiting the effectiveness of therapeutic interventions. Recent breakthroughs in genomic and transcriptomic technologies, including whole-genome sequencing, single-cell RNA sequencing (scRNA-seq), and CRISPR-based screens, have revolutionized the field, offering new avenues for early diagnosis and personalized prognosis. Genomic approaches have elucidated disease-specific genetic risk factors and molecular pathways, while transcriptomic studies have identified stage-specific biomarkers that correlate with disease progression and severity. Furthermore, genome-wide association studies (GWAS), polygenic risk scores (PRS), and spatial transcriptomics are enabling the stratification of patients based on their risk profiles and prognostic trajectories. Advances in functional genomics have uncovered actionable targets, such as ATXN2 in ALS and TREM2 in AD, paving the way for tailored therapeutic strategies. Despite these achievements, challenges remain in translating genomic discoveries into clinical practice due to disease heterogeneity and the complexity of neurodegenerative pathophysiology. Future integration of genetic technologies holds promise for transforming diagnostic and prognostic paradigms, offering hope for improved patient outcomes and precision medicine approaches.

Indexed as

GenomicsNeurodegenerative DiseasesTranscriptomeGene Expression ProfilingGenome-Wide Association StudyHumansParkinson DiseasePrognosisbiomarkersdiagnosisgenomics approachesneurodegenerative diseasesprognostic trajectoriestranscriptomics

Identifiers

PMID40004464
PMCPMC11855287

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.