Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer

Abstract

BackgroundLung cancer is a severe cancer with a high death rate. The 5-year survival rate for stage III lung cancer is much lower than stage I. Early detection and intervention of lung cancer patients can significantly increase their survival time. However, conventional lung cancer-screening methods, such as chest X-rays, sputum cytology, positron-emission tomography (PET), low-dose computed tomography (CT), magnetic resonance imaging, and gene-mutation, -methylation, and -expression biomarkers of lung tissue, are invasive, radiational, or expensive. Liquid biopsy is non-invasive and does little harm to the body. It can reflect early-stage dysfunctions of tumorigenesis and enable early detection and intervention.MethodsIn this study, we analyzed RNA-sequencing data of tumor-educated platelets (TEPs) in 402 non-small-cell lung cancer (NSCLC) patients and 231 healthy controls. A total of 48 biomarker genes were selected with advanced minimal-redundancy, maximal-relevance, and incremental feature-selection (IFS) methods.ResultsA support vector-machine (SVM) classifier based on the 48 biomarker genes accurately predicted NSCLC with leave-one-out cross-validation (LOOCV) sensitivity, specificity, accuracy, and Matthews correlation coefficients of 0.925, 0.827, 0.889, and 0.760, respectively. Network analysis of the 48 genes revealed that the WASF1 actin cytoskeleton module, PRKAB2 kinase module, RSRC1 ribosomal protein module, PDHB carbohydrate-metabolism module, and three intermodule hubs (TPM2, MYL9, and PPP1R12C) may play important roles in NSCLC tumorigenesis and progression.ConclusionThe 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients.

Liquid Biopsy in Non-Small Cell Lung Cancer: Highlights and Challenges

“…Sheng and colleagues analyzed RNA-sequencing data of TEPs in 402 NSCLC patients and 231 healthy controls. The authors identified 48 genes having a key role in tumorigenesis and cancer progression that can accurately predict NSCLC, suggesting that TEPs might be useful for the early detection of NSCLC [93]. A recent study showed that particle-swarm optimization-enhanced algorithms enable efficient selection of gene panels from platelet RNA-sequencing libraries, obtaining an accurate TEP-based detection of early stage or advanced NSCLC regardless of smoking history, age, inflammatory conditions, and whole-blood storage time [94].…”

Development of Thrombocytopenia is Associated with Improved Survival in Patients Treated with Immunotherapy

“…Moreover, it is theorized that platelets may undergo mRNA splice events in response to signals released by tumor cells [ 23 ]. Another suggested mechanism includes sequestration of tumor-associated molecules into the platelets, leading to what is known as tumor-educated platelets [ 24 ]. Based on these findings, it has become clear that interactions between cancer cells and activated platelets occur at different cellular levels; it may be a potential target for future therapeutic drugs.…”

Circulating platelets as liquid biopsy sources for cancer detection

“…Using RNA-seq data of TEPs from patients with NSCLC and healthy controls, a total of 48-biomarker panel was selected (Sheng et al, 2018). A support vector machine (SVM) classifier based on the 48-biomarker panel accurately predicted NSCLC with leave-oneout cross-validation (LOOCV), with 0.925 sensitivity, 0.827 specificity, and 0.889 accuracy.…”

“…A support vector machine (SVM) classifier based on the 48-biomarker panel accurately predicted NSCLC with leave-oneout cross-validation (LOOCV), with 0.925 sensitivity, 0.827 specificity, and 0.889 accuracy. Network analysis of the 48 transcripts revealed that the WASF1 actin cytoskeleton module, the PRKAB2 kinase module, the RSRC1 ribosomal protein module, the PDHB carbohydrate-metabolism module, and three inter-module hubs (TPM2, MYL9, and PPP1R12C) might play important roles in NSCLC tumorigenesis and progression (Sheng et al, 2018).…”