ArticleBMC cancer2026
Screening and identification of biomarkers in tumor-educated platelets for non-small cell lung cancer.
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundPlatelets, a crucial source of liquid biopsy samples, play a significant role in the progression of various malignancies, including non-small cell lung cancer (NSCLC). However, specific biomarkers for early-stage NSCLC have not yet been identified. In this study, we employed bioinformatics approaches to identify differentially expressed genes (DEGs) in tumor-educated platelets (TEPs) from NSCLC. Subsequently, we validated these genes using specimens from patients with early-stage lung cancer, highlighting their potential as biomarkers for NSCLC.
methodsWe analyzed the platelet mRNA expression profile microarray dataset GSE89843, which comprises samples from NSCLC patients and healthy controls. Utilizing R software, we eliminated batch effects and identified DEGs. NSCLC-associated module genes were identified via weighted gene co-expression network analysis (WGCNA). Enrichment analysis was performed using R-based tools, and protein-protein interaction (PPI) analysis was conducted to identify key DEGs. Finally, RT-qPCR validation was performed to assess the differential expression of selected genes in platelet total RNA between early-stage NSCLC patients and healthy controls.
resultsInitially, we analyzed the RNA sequencing data from dataset GSE89843, which contains 779 platelet samples, and identified 532 DEGs (72 upregulated and 460 downregulated). Among these DEGs, seven key genes were identified: HSP90AB1, NPM1, CD44, HSPA4, CD74, SEC31A, and EIF2S1. Notably, CD44 served as the interaction starting point in gene association networks. Meanwhile, HSP90AB1, NPM1, and CD74 exhibited low expression levels in NSCLC. RT-qPCR validation confirmed that HSP90AB1, NPM1, CD44, and CD74 exhibited significantly downregulated expression in platelets from early-stage (I and II) NSCLC patients.
conclusionSpecific DEGs for NSCLC are present in platelets, including HSP90AB1, NPM1, CD44, and CD74, which may play a role in the early stages of NSCLC development. These genes exhibit a specific expression pattern, characterized by downregulation in early-stage (I and II) NSCLC, indicating their potential as biomarkers for early screening and diagnosis of NSCLC.
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.