Screening and identification of biomarkers in tumor-educated platelets for non-small cell lung cancer

BACKGROUND: Platelets, 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. METHODS: We 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. RESULTS: Initially, 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. CONCLUSION: Specific 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.

Authors

Institutions

Publication Details

Journal
BMC Cancer
Published
2026-06-08
DOI
https://doi.org/10.1186/s12885-026-16307-5
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Screening and identification of biomarkers in tumor-educated platelets for non-small cell lung cancer

Wenhua Li, HuiFang Wu, Zhen Xu, Lin Zhang et al.
BMC Cancer
Inflammatory Biomarkers in Disease Prognosis
article

Screening and identification of biomarkers in tumor-educated platelets for non-small cell lung cancer

Wenhua Li, HuiFang Wu, Zhen Xu, Lin Zhang, Yueneng Yu
article en

Abstract

BACKGROUND: Platelets, 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. METHODS: We 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. RESULTS: Initially, 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. CONCLUSION: Specific 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.

BMC Cancer
Wenzhou Medical University (CN), Jining Traditional Chinese Medicine Hospital (CN), Dongyang People's Hospital (CN)
Good health and well-being
Openalex Percentile: Top 7%
Inflammatory Biomarkers in Disease Prognosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.