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This study investigated the effectiveness of acoustic separation for platelet analysis in patients with non–small‐cell lung cancer (NSCLC), comparing it with traditional centrifugation methods. In total, 10 patients with NSCLC and 10 healthy volunteers provided peripheral blood samples, which were processed using either acoustic separation or centrifugation to isolate platelets. The study included whole transcriptome analysis of platelets, peripheral blood mononuclear cells, and tumor tissue samples, employing hierarchical clustering and Gene Ontology analysis to explore gene expression differences. Acoustic separation proved more efficient than centrifugation in terms of platelet yield, recovery rate, and RNA yield. Gene expression profiles of platelets from patients with NSCLC showed distinct patterns compared with healthy volunteers, indicating tumor‐influenced alterations. Gene Ontology analysis revealed enrichment in pathways associated with platelet activation and the tumor microenvironment. This finding indicates the potential of acoustic isolation in platelet separation and its relevance in understanding the unique gene expression profile of platelets in patients with NSCLC. The findings of this study suggested that platelets from cancer patients separated by acoustic techniques exhibited tumor‐specific alterations and provided new insights into the diagnosis of cancer in platelet analysis systems in clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: Head and neck squamous cell carcinoma (HNSCC) remains a global health concern due to the lack of precise early diagnostic biomarkers and often-delayed diagnosis. This study employs machine learning, weighted gene co-expression network analysis, and network biology to identify transcriptomic markers for HNSCC detection. We identified nine genes with significantly differentially expressed activity in samples from HNSCC patients. These gene signatures could greatly improve early HNSCC identification and warrant further investigation to confirm their predictive and therapeutic significance. The transcriptional landscape of platelets in head and neck cancer patients revealed distinct gene expression profiles compared to healthy controls, underscoring the systemic impact of the tumor on blood platelets. Additionally, the study emphasizes the role of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Although there has been a reduction in head and neck squamous cell carcinoma occurrence, it continues to be a serious global health concern. The lack of precise early diagnostic biomarkers and postponed diagnosis in the later stages are notable constraints that contribute to poor survival rates and emphasize the need for innovative diagnostic methods. In this study, we employed machine learning alongside weighted gene co-expression network analysis (WGCNA) and network biology to investigate the gene expression patterns of blood platelets, identifying transcriptomic markers for HNSCC diagnosis. Our comprehensive examination of publicly available gene expression datasets revealed nine genes with significantly elevated expression in samples from individuals diagnosed with HNSCC. These potential diagnostic markers were further assessed using TCGA and GTEx datasets, demonstrating high accuracy in distinguishing between HNSCC and non-cancerous samples. The findings indicate that these gene signatures could revolutionize early HNSCC identification. Additionally, the study highlights the significance of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Despite using platelet and tumor samples from different individuals, our results suggest that TEPs reflect the transcriptomic and epigenetic landscape of tumors. Future research should aim to directly correlate tumor and platelet samples from the same patients to further elucidate this relationship. This study underscores the potential of these biomarkers in transforming early diagnosis and personalized treatment strategies for HNSCC, advocating for further research to validate their predictive and therapeutic potential. [ABSTRACT FROM AUTHOR]

There is no cost-effective, accurate, and non-invasive method for the detection of esophageal squamous cell carcinoma (ESCC) in clinical practice. We aimed to investigate the diagnostic potential of tumor-educated platelets in ESCC. In this study, seventy-one ESCC patients and eighty healthy individuals were enrolled and divided into a training cohort (23 patients and 27 healthy individuals) and a validation cohort (48 patients and 53 healthy individuals). Next-generation RNA sequencing was performed on platelets isolated from peripheral blood of all participants, and a support vector machine/leave-one-out cross validation (SVM/LOOCV) approach was used for binary classification. A diagnostic signature composed of ARID1A, GTF2H2 , and PRKRIR discriminated ESCC patients from healthy individuals with 91.3% sensitivity and 85.2% specificity in the training cohort and 87.5% sensitivity and 81.1% specificity in the validation cohort. The AUC was 0.924 (95% CI, 0.845–0.956) and 0.893 (95% CI, 0.821–0.966), respectively, in the training cohort and validation cohort. This 3-gene platelet RNA signature could effectively discriminate ESCC from healthy control. Our data highlighted the potential of tumor-educated platelets for the noninvasive diagnosis of ESCC. Moreover, we found that keratin and collagen protein families and ECM-related pathways might be involved in tumor progression and metastasis of ESCC, which might provide insights to understand ESCC pathobiology and advance novel therapeutics. [ABSTRACT FROM AUTHOR]

Aims: To evaluate the value of tumor-educated platelet (TEP) miR-18a-3p in the early diagnosis and chemotherapy efficacy monitoring of nasopharyngeal carcinoma (NPC). Methods: Expression levels of miR-18a-3p in platelets and plasma were detected by relative quantitative real-time PCR in NPC patients (n=54) and normal subjects (n=36). Diagnostic values of TEP miR-18a-3p for NPC was assessed by receiver operating characteristic (ROC) curve analysis. Follow up study was carried out to observe the dynamic changes of TEP miR-18a-3p with chemotherapy on 3 NPC patients. Results: The expression levels of TEP miR-18a-3p in NPC patients were significantly higher than that in healthy controls (p < 0.0001). ROC curve analysis showed that the area under the curve (AUC) value was 0.841, the sensitivity and specificity for the diagnosis of NPC were 87% and 72.7%. No correlation was found between expression levels of TEP miR-18a-3p and patients' clinical parameters and their NPC tumor-node-metastasis (TNM) stage. The positive rate of TEP miR-18a-3p and EBV DNA for NPC diagnosis were 85.4% and 66.7%. TEP miR-18a-3p expression were down-regulated after 77.8% (7 of 9) of chemotherapy, and in 66.7% (2 of 3) patients, TEP miR-18a-3p levels decreased after 3 cycles of chemotherapy. Conclusion: The expression levels of TEP miR-18a-3p are upregulated in NPC and have a high probability to downregulated after chemotherapy, indicating a significant clinical value. TEP miR-18a-3p might serve as a novel type of liquid-biopsy biomarker for early diagnosis and chemotherapy efficacy monitoring in NPC. [ABSTRACT FROM AUTHOR]

Background: Lung 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. Methods: In 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. Results: A 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. Conclusion: The 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients. [ABSTRACT FROM AUTHOR]