ArticleOncology letters2019
Role of APLP2 in the prognosis and clinicopathology of renal cell carcinoma.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Integrated single-cell and spatial mapping coupled with machine learning unveils core stemness landscapes and regulatory drivers in triple-negative breast cancer.Discover oncology · 2026Article
- Immunohistochemical profile of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) versus other thyroid follicular lesions.Diagnostic pathology · 2025Article
- Epigenetic modulation of human neurobiological disorders: Lesch-Nyhan disease as a model disorder.AIMS neuroscience · 2025Review
- Amyloid precursor-like protein 2 expression in macrophages: differentiation and M1/M2 macrophage dynamics.Frontiers in oncology · 2025Article
- Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy.Frontiers in immunology · 2023Article
- Potential benefit of lymph node dissection during radical nephrectomy for kidney cancer: A review and critical analysis of current literature.Asian journal of urology · 2022Review
- MMP25 Regulates Immune Infiltration Level and Survival Outcome in Head and Neck Cancer Patients.Frontiers in oncology · 2020Article
- Implication of Amyloid Precursor-like Protein 2 Expression in Cutaneous Squamous Cell Carcinoma Pathogenesis.In vivo (Athens, Greece)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying diagnostic and prognostic biomarkers is crucial for improved guidance of the treatment of renal cell carcinoma (RCC). Amyloid β precursor-like protein 2 (APLP2) has been determined to serve an important role in the progression of a number of cancer types. However, the expression and significance of APLP2 in RCC remains unknown. In the present study, it was determined that the expression of APLP2 protein (n=10) and mRNA (n=8) expression was significantly decreased in clear cell RCC (CCRCC) tissues compared with that in matched normal renal tissues. The expression level of APLP2 was significantly associated with high Fuhrman grade, high pT stage, and presence of distant metastasis and lymph node metastasis (P<0.05). Multivariate analysis demonstrated that the expression of APLP2 was a significant independent predictor of disease-specific survival in renal cell carcinoma (P=0.026). Notably, APLP2 expression was significantly associated with disease-specific survival (P<0.001). APLP2 may be used to potentially predict patient prognosis, and to guide clinical diagnosis and treatment in CCRCC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.