PurposeVenous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, is a leading cause of morbidity and mortality in cancer patients. Prostate cancer is associated with an elevated risk of VTE, yet the molecular drivers remain poorly defined.MethodsIn this study, we employed high-throughput proteomic profiling using the SomaLogic platform to analyze plasma from 85 prostate cancer patients, including 43 with and 42 without VTE. Samples were collected at cancer diagnosis, with VTE diagnosed at a mean of 96.8 months later.ResultsPrincipal component analysis showed modest proteomic separation between groups. Differential expression analysis identified enriched pathways in VTE patients, including hemostasis (TIMP1, JAM2, TMX3, F3, and ESAM), cell adhesion (CXCL12, CCL11, CCN5, COL18A1, and ADGRB1) and cell proliferation (TIMP1, REG1B, REG1A, CRLF2, and ALDH1A2). Receiver Operating Characteristic analysis using top fifteen proteins achieved an area under the curve of 0.859, indicating strong predictive value for VTE in this cohort.ConclusionsWe identified a specific cluster of circulating proteins associated with development of VTE in patients with prostate cancer. This work deepens understanding of systemic mediators of cancer-associated VTE and, pending validation in other cohorts, paves the way for improved risk stratification and long-term monitoring in this population.

BackgroundHepatocellular carcinoma (HCC) is a major global health burden, with limited tools for early diagnosis and prognosis. This study explores serum-derived exosomal miR-1275 as a potential non-invasive biomarker for HCC.MethodsExosomes were isolated from serum samples of 50 HCC patients and 50 matched healthy controls. miR-1275 expression was quantified by qRT-PCR and compared with traditional biomarkers (AFP, CEA, CA199, DCP, AFP-L3%). Diagnostic performance was evaluated using ROC curves. Bioinformatic analyses, including TCGA pan-cancer data, target gene prediction, and pathway enrichment, were performed to explore regulatory mechanisms.ResultsExosomal miR-1275 levels were significantly reduced in HCC patients and correlated with advanced clinical stage, tumor burden, and metastasis. miR-1275 showed strong diagnostic value (AUC = 0.869), outperforming CEA and CA199, and improved significantly when combined with other biomarkers (AUC = 0.982). UBE2V1 was identified as a key miR-1275 target involved in cancer-related pathways, including ubiquitination and mTOR signaling.ConclusionSerum exosomal miR-1275 is a promising biomarker for early diagnosis and prognosis of HCC. Its integration into multimarker panels could enhance clinical decision-making and patient management.

BackgroundCD137L plays a substantial role in immune regulation and has been associated with tumor progression. However, its expression pattern and clinical significance in colorectal cancer remain unclear. The current study was planned to evaluate the expression levels of CD137L in colorectal cancer tissues and investigate its association with clinicopathological characteristics and patient survival.Methodology36 tissue samples were collected from colorectal cancer patients followed by RNA extracted. Following the cDNA synthesis, qRT-PCR was conducted to evaluate the CD137L expression, normalized against GAPDH and the comparative expression was determined using the ΔΔCt method. Chi-square test was applied to evaluate the relation of CD137L expression with clinical parameters. Meanwhile, the TCGA database was explored to find the relationship between the prognosis of colorectal cancer patients and different levels of CD137L expression and to analyze the distribution characteristics of differentially expressed genes.ResultsCD137L expression was significantly correlated with patient survival (P < 0.05), with higher expression observed in patients who were alive at the time of analysis. Clinical parameters such as age and gender were insignificantly associated (P > 0.05) with CD137L expression. However, a significant correlation (P = 0.03) was noted between CD137L expression and tumor staging, suggesting its potential involvement in CRC progression. Furthermore, Analysis of TCGA data showed that patients with elevated CD137L expression exhibited improved overall survival compared to those with lower expression levels. Enrichment analysis revealed that CD137 was primarily enriched with immune cell proliferation, T cell activation and Th1/Th2 balance-related signaling pathways.ConclusionCD137L may serve as a reliable indicator for forecasting the outcome of colorectal cancer patients, providing guidance for colorectal cancer prognosis.

Diagnosing papillary thyroid carcinoma (PTC) remains challenging, particularly due to limitations in fine-needle aspiration biopsy (FNAB), which yields up to 10% nondiagnostic results. The objective of this study was to evaluate the diagnostic and prognostic potential of four candidate microRNAs (miR-21, miR-31, miR-187-3p, and miR-200a-5p) in PTC from multinodular goiter (MNG) and normal. Fresh tissue samples from PTC and MNG patients were analyzed using quantitative RT-PCR, followed by ROC analysis to assess diagnostic accuracy and correlation with clinical, histopathological, and hormonal parameters. Compared to normal tissue, miR-21 and miR-187-3p were significantly upregulated in PTC, while miR-31 and miR-200a-5p were downregulated. MNG samples showed similar but less pronounced trends. All four miRNAs differed significantly between PTC and MNG. ROC analysis revealed strong diagnostic performance, particularly for miR-187-3p (AUC = 0.937) and miR-21 (AUC = 0.914), with their combination achieving an AUC of 0.968. Expression levels correlated with age, tumor stage, surgical status, and thyroid hormones (TSH, ATG, TG), highlighting novel regulatory patterns. This miRNA panel offers promising diagnostic value and insight into PTC pathogenesis, suggesting potential for non-invasive diagnostics and targeted therapies.

BackgroundThe CD155-TIGIT axis, a breast cancer progression biomarker, underscored neoadjuvant chemotherapy (NAC) response variability in triple-negative breast cancer (TNBC), urging biomarker-based patient stratification for timely therapy.MethodsThirty-nine TNBC patients who received NAC were recruited. The expression of TIGIT, CD155, CD226, and CD96 on tumoral and stromal cells in the tumor microenvironment was detected by immunohistochemistry, and their relationships with NAC response were explored.Results10.3% patients exhibited grade 1 (G1) response to NAC, and 20.5% achieved a complete pathological response. Notably, CD155 and CD96 were predominantly detected on tumor cells, whereas CD226 and TIGIT were predominantly detected on stromal cells. The expression of these markers did not significantly correlate with response to NAC (p > 0.05), and each individual marker lacked predictive power for determining NAC therapeutic efficacy (p > 0.05). However, a specific combination of tumoral cells expression of CD226(≥4%), CD155(≥40%), and CD96(≥35%), coupled with TIGIT expression on tumoral (<35%) and stromal cells (<12.5%), was able to identify patients with G1 response to NAC.ConclusionExpression levels of TIGIT/CD155/CD226/CD96 on tumoral and stromal cells might collectively serve as predictive biomarkers for NAC response in TNBC. This implied that CD155-TIGIT axis could be prospectively applied clinically to identify NAC-resistant TNBC patients.

BackgroundHomeobox C6 (HOXC6) shows abnormal expression in various tumors, but its pattern in lung squamous cell carcinoma (LUSC) remains unclear.ObjectiveTo investigate HOXC6's expression and function in LUSC.MethodsHOXC6 expression was analyzed using single-cell RNA sequencing (scRNA-seq) in LUSC, cervical squamous cell carcinoma (CESC), esophageal squamous cell carcinoma (ESCC), laryngeal squamous cell carcinoma (LSCC), and oral squamous cell carcinoma (OSCC), verified by RNA-seq and immunohistochemistry (IHC). CRISPR knockdown assessed proliferation impact. Immune infiltration analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses explored immune microenvironment relationships.ResultsHOXC6 was highly expressed in LUSC (standard mean difference (SMD) = 1.17, confidence interval (CI) = 0.75-1.59, area under the curve (AUC) = 0.88), confirmed by IHC (P = 1.6e-10, AUC = 0.99). HOXC6 silencing inhibited proliferation. High expression negatively correlated with immune infiltration and decreased StromalScore, ImmuneScore, and ESTIMATEScore. HOXC6 was elevated in other squamous carcinomas.ConclusionHigh HOXC6 expression may promote LUSC and pan-squamous carcinoma development through mitosis regulation.

BackgroundAutoantibodies against tumor-associated antigens (TAAs) are promising noninvasive cancer biomarkers due to their specificity and stability. Gastric cancer (GC) diagnosis often requires invasive procedures, emphasizing the need for reliable blood-based biomarkers.ObjectiveThis study assessed whether serum IgG and IgA autoantibodies, individually or combined, could serve as noninvasive biomarkers for gastric cancer.Experimental designWe analyzed 27 autoantibodies in serum from 265 healthy controls, 296 GC patients, and 195 gastritis patients using protein microarray. Autoantibody levels and the IgG/IgA ratio were calculated. Diagnostic accuracy was evaluated using receiver operating characteristic (ROC) curves.ResultsWe identified 24 differentially expressed autoantibodies (DEAs) for IgA and 17 for IgG between GC patients and controls. In distinguishing GC from gastritis, 20 DEAs for IgA and 23 for IgG were significant. The IgG/IgA ratio of MIP1 beta had the highest diagnostic performance between atrophic gastritis and GC, while MMP7 was the most effective between chronic gastritis and GC. The gbm model with 14 autoantibodies had the highest Youden's index for GC versus controls, and a 13-autoantibody model performed best for GC versus all gastritis.ConclusionsSpecific panels of autoantibodies could serve as noninvasive diagnostic tools for distinguishing GC from controls and gastritis.

BackgroundGastric cancer is the fifth most common malignancy and third leading cause of cancer death in China, with advanced-stage five-year survival below 20%. βIII-tubulin (TUBB3) is overexpressed in cancers but its role in gastric cancer remains unclear.MethodsTUBB3 expression was analyzed using TCGA data and clinical samples. Knockdown models assessed its effects on proliferation, migration, and invasion in vitro and in vivo.ResultsTUBB3 was significantly upregulated in gastric cancer tissues versus normal mucosa. High TUBB3 correlated with poorer disease-free and overall survival but not other clinicopathological features. Functionally, TUBB3 knockdown inhibited proliferation via G2/M arrest and reduced migration/invasion by disrupting invadopodia, without affecting apoptosis, EMT, or ECM degradation. In vivo, TUBB3 depletion suppressed tumor growth and metastasis. Mechanistically, TUBB3 promoted G2/M transition via p21/Cyclin B1 and enhanced invasiveness through Cortactin/JNK activation.ConclusionTUBB3 overexpression predicts poor prognosis in gastric cancer. It drives proliferation via cell cycle regulation and metastasis through invadopodia formation, suggesting its potential as a therapeutic target.

BackgroundMelanoma represents one of the most aggressive skin cancers, responsible for over 75% of skin cancer-related deaths despite comprising only 5% of cases. Despite therapeutic advances, patient responses remain variable and unpredicv. The Signaling Lymphocytic Activation Molecule (SLAM) family regulates immune cell communication, with SLAMF8 being predominantly expressed on myeloid cells. However, SLAMF8's specific role in melanoma pathogenesis remains largely unexplored.MethodsIn this retrospective integrative study, we systematically investigated SLAMF8's role in melanoma through multi-omics analyses using TIMER, GEPIA, and UALCAN databases, following the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational data reporting. Functional studies were conducted in A-375 and SK-MEL-28 melanoma cell lines using siRNA-mediated knockdown, followed by migration, invasion, and proliferation assays. DNA methylation patterns were analyzed via the SMART database, while mutation profiles were examined using cBioPortal and COSMIC. Immune infiltration analysis was performed through TIMER, and pathway associations were investigated using Gene Set Enrichment Analysis and protein-protein interaction networks. Finally, two-sample Mendelian Randomization analysis assessed the causal relationship between SLAMF8 expression and melanoma susceptibility.ResultsSLAMF8 expression was significantly higher in metastatic melanoma compared to primary tumors, with expression patterns varying across disease stages and between sexes. Higher SLAMF8 expression correlated with improved disease-free and overall survival. Functional studies demonstrated that SLAMF8 knockdown significantly enhanced melanoma cell proliferation, migration, and invasion. DNA methylation analysis revealed significant negative correlations between methylation at specific CpG sites and SLAMF8 expression, with hypermethylation associated with worse survival outcomes. Mutation analysis identified alterations in 10.21% of melanoma patients. Immune infiltration studies demonstrated strong correlations between SLAMF8 expression and enhanced immune cell presence. GSEA linked SLAMF8 to critical immune pathways including allograft rejection, inflammatory response, and interferon signaling. Mendelian Randomization analysis established a protective causal relationship between SLAMF8 and melanoma risk (OR = 0.39, 95% CI = 0.21-0.74, p = 3.34e-03).ConclusionOur study demonstrates that SLAMF8 plays a critical role in melanoma by suppressing tumor progression and modulating the immune microenvironment. Elevated SLAMF8 expression in metastatic melanoma is associated with improved patient survival, suggesting its utility as a prognostic biomarker. Furthermore, its tumor-suppressive effects and immune-regulatory functions highlight SLAMF8 as a promising therapeutic target for melanoma treatment strategies.

BackgroundLung cancer remains a leading cause of cancer-related mortality, with accurate staging essential for guiding treatment. Advances in next-generation sequencing (NGS) and machine learning (ML) enable more precise classification, improving on traditional imaging-based methods.ObjectiveThis retrospective study applies XGBoost with cross-validation (CV) to classify early vs. late-stage lung cancer using RNA-Seq data from 993 patients in The Cancer Genome Atlas (TCGA) cohort.MethodsGene selection was conducted using the Wilcoxon rank-sum test on training data, and the XGBoost model was optimized via cross-validation. Model performance was assessed using the Area Under the Curve (AUC), with sensitivity-specificity analysis across classification thresholds.ResultsThe XGBoost model achieved a test AUC of 0.6534, identifying 40 key genes that optimize predictive accuracy while minimizing overfitting. Thresholds of 0.3 and 0.4 were optimal, balancing sensitivity and specificity for clinical application.ConclusionsIntegrating RNA-Seq data with machine learning improves lung cancer staging accuracy. Future research should focus on dataset expansion, model benchmarking, and multi-omics integration to enhance clinical applicability.