Cancer Biomarkers最新文献

BackgroundAmong head and neck squamous cell carcinomas (HNSCCs), the incidence of oropharyngeal cancer (OPC) has been increasing in recent decades. Human papillomavirus (HPV) type 16 is associated with the majority of OPC. Circulating antibodies (Abs) to multiple HPV16 early antigens, including E2, E6, and E7, have been detected in patient sera, and are strongly associated with risk for OPC. However, HPV serology currently requires laboratory-based tests that are difficult to implement for large-scale cancer screening.ObjectiveThe goal of this study was to develop and validate a point-of-care assay for rapid detection of circulating IgG to HPV16 early antigens.MethodsWe measured Abs to HPV16 E2, E6, and E7 proteins using a lateral flow assay (LFA) in sera from 119 newly diagnosed OPC cases, 41 partners, and 81 healthy volunteers. The 119 patients with HPV-OPC were classified as HPV-positive based on in situ hybridization (ISH) or institutional p16 immunohistochemistry. The sensitivity and specificity of the LFA were determined by comparing to clinical diagnosis.ResultsThe specificity for each individual HPV16 E2, E6, and E7 antibodies was 95.1% (77/81), 96.3% (78/81), and 98.7% (80/81), respectively. The sensitivities of the individual HPV16 antibodies were as follows: E2, 47.9% (57/119), E6, 31.9% (38/119), and E7, 57.1% (68/119). The 3-biomarker panel (at least one positive for E2, E6, and E7 Abs) demonstrated a sensitivity of 76.5% (91/119) and a specificity of 92.6% (75/81).ConclusionsWe developed a multiplexed lateral flow assay for the rapid detection of serologic responses to HPV16. Further research is required to determine the utility of these tests for HPV + HNSCC cancer screening, as higher specificity, and an assessment of the benefits of positive test results have yet to be evaluated in this context.

BackgroundThis study aimed to identify hub genes linked to hepatocellular carcinoma (LIHC) pathogenesis using bioinformatics analysis.MethodA total of 3865 samples from 12 datasets in the HCCDB database were analyzed to identify prognostic expression genes (PDGs). Enrichment analysis using DAVID and GSCA databases unveiled biological processes and signaling pathways associated with PDGs. Cytohubba app was utilized to identify 6 hub genes from the PDGs. Verification of hub genes was conducted using three GEO datasets and Western blot. Histopathological staining data of hub genes in LIHC patients were retrieved from the Human Protein Atlas database. Comprehensive analyses of hub genes were performed, including immune infiltration, prognosis, survival, methylation, gene mutation, related miRNA, and single-cell type. Potential therapeutic drugs were predicted using GDSC and CTRP databases.ResultA total of 1259 differential genes were screened, yielding 82 PDGs (36 up-regulated and 46 down-regulated genes). Hub genes identified included CDC20, TOP2A, CDK1 (up-regulated), and CAT, TAT, FTCD (down-regulated). These hub genes exhibited strong associations with immune cells and showed promising prognostic value based on AUC analysis. Reduced promoter methylation levels of TOP2A, CDK1, and FTCD in LIHC were observed. Single nucleotide polymorphisms analysis highlighted prevalent variants and miRNA expression associations impacting patient survival. Hub genes were enriched in various cell types. Trametinib, selumetinib, RDEA119, and teniposide were identified as potential drugs for LIHC treatment.ConclusionCDC20, TOP2A, CDK1, CAT, TAT, and FTCD may contribute to LIHC development and serve as novel prognostic biomarkers.

Background: Lung cancer (LC) is the most common malignancy and the leading cause of cancer death. LC-derived exosomes have been found to play a critical role in tumor initiation, progression, metastasis and drug resistance. Therefore, the objective of this study is to identify prognostic markers based on lung cancer-derived exosomes in patients with different subtypes of lung cancer, including small cell lung cancer (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large cell carcinoma (LCC). Additionally, we aim to develop corresponding prognostic models to predict the outcomes of these patients. Methods: In this study, the mRNAs information about LC-derived exosomes was collected from Vesiclependia database, and the mRNAs data of LCC, LUAD, LUSC and LCC tumors and paracancerous tissues was obtained from the GEO database and UCSC database. The prognostic models based on exosomes-related differential expression genes (ExoDEGs) by univariate Cox, LASSO, and multivariate Cox regression analyses. The independent prognostic value of the risk model was systematically analyzed. Results: A LUAD prognostic risk model of 12 ExoDEGs (CDH17, DAAM2, FKBP3, FLNC, GSTM2, PGAM4, HPCAL1, FERMT2, LYPD1, SNRNP70, KIR3DL2 and GPX3) and a LUSC prognostic risk model of 7 ExoDEGs (FGA, ERH, HID1, CSNK2A1, SLC7A5, ACOT7 and FUNDC1) were constructed. Kaplan-Meier curve, ROC curve and stratification survival analysis confirmed that the LUAD and LUSC risk models both possessed reliable predictive value for the prognosis of LUAD and LUSC patients. The expression level of ExoDEGs for building the LUAD and LUSC risk models is significantly correlated with immunosuppressive activity of patients, and the immunosuppressive activity is lower in the high-risk groups. Conclusions: We established a LUAD prognostic model with 12 ExoDEGs and a LUSC prognostic model with 7 ExoDEGs, which can be used as independent prognostic indicators for patients LUAD and LUSC. The identified ExoDEGs have the potential to be as prognostic markers and may also serve as novel candidate targets for the treatment of LUAD and LUSC.

This study aims to identify and validate potential endogenous biomarkers for triple-negative breast cancer (TNBC). TNBC microarray data (GSE38959, GSE53752) were retrieved from the Gene Expression Omnibus (GEO) database, and principal component analysis (PCA) was performed to evaluate the reliability of the data. The microarray datasets were merged, and differentially expressed genes (DEGs) were identified using R software. Functional enrichment analysis of the DEGs was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The most disease-relevant module was identified through Weighted Gene Co-expression Network Analysis (WGCNA), and genes within this module were intersected with the DEGs. The intersecting genes underwent Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis to minimize errors and identify TNBC-specific genes. Sensitivity and survival analyses were performed on the identified specific genes. There were 10 TNBC-specific genes identified: RRM2, DEPDC1, FIGF, TACC3, E2F1, CDO1, DST, MCM4, CHEK1, and PLSCR4. RT-qPCR analysis showed significant upregulation of CDO1, MCM4, DEPDC1, RRM2, and E2F1 in MDA-MB-231, CAL-148, and MFM-223 compared to MCF-10A. Our findings provide new insights into TNBC pathogenesis and potential therapeutic strategies, with important clinical implications for further understanding TNBC mechanisms and developing innovative treatments.

BackgroundPrecise recognition of neck lymph node metastasis (LNM) is essential for choosing the suitable scope of operation for papillary thyroid cancer(PTC) patients.ObjectiveThe purpose of our study was to establish an effective nomogram integrating both gene biomarkers and clinicopathologic features for preoperatively predicting LNM in PTC patients.MethodsWe gathered clinical information and gene expression data for PTC samples from The Cancer Genome Atlas database (TCGA). WGCNA and differential analysis were applied to identify LNM-related differentially expressed genes in PTC patients. We developed a risk score based on the 3-gene signature predicting LNM using the LASSO regression analysis. Furthermore, multivariate logistic regression analysis was performed to establish a nomogram. We evaluated the discriminative ability of the nomogram by calculating the area under the ROC curve. Besides, we applied the decision curve analyses and calibration curve to assess the nomogram's actual benefits and accuracy.ResultsSignificant predictors of LNM in PTC patients were eventually screened to develop a nomogram, which included age, histological type, focus type, T stage, and risk score calculated based on IQGAP2, BTBD11 and MT1G expression levels. The AUC value of the nomogram for training and validation set was 0.802 (95% CI 0.750-0.855) and 0.718 (95% CI 0.624-0.811). Moreover, the nomogram has outstanding calibration and actual clinical patient benefits.ConclusionsWe identified a nomogram based on the 3-gene signature and clinical characteristics that effectively predicted LNM in PTC patients, which offers guidance for the preoperative assessment the appropriate scope of operation in PTC patients.

Background: Head and neck squamous cell carcinoma (HNSC) is a globally prevalent malignancy with high mortality rates. RNA-binding proteins (RBPs) are crucial regulators of gene expression and play significant roles in cancer development. However, a comprehensive understanding of RBPs at the single-cell level in HNSC remains limited.ObjectiveThis study aims to investigate the role of RBPs in the stepwise progression of HNSC at the single-cell level, focusing on their expression patterns, prognostic potential, and involvement in key signaling pathways.MethodsWe analyzed single-cell RNA-sequencing data from HNSC samples across four stages, from normal tissue to precancerous leukoplakia, then to primary cancer and finally to metastatic tumors, examining the expression of 2141 previously reported RBPs. We identified RBP-based cell clusters and explored their associations with disease stages, cell types, and cancer progression. A prognostic risk model was developed based on RBPs with significant relevance to patient outcomes.ResultsRBPs displayed distinct cell type-specific expression patterns across different stages of HNSC. We found a significant correlation between RBP-based cell clusters and cancer progression. Notably, a prognostic model was constructed using RBPs such as CELF2, which showed downregulation from early leukoplakia to advanced cancer stages. Fibroblast RBPs were dynamically regulated, particularly in extracellular matrix remodeling, with key proteins like CFL1 and PFN1 linked to improved prognosis. Furthermore, we identified heterogeneity in RBP regulation of the Macrophage Migration Inhibitory Factor (MIF) signaling pathway across cell types during the precancerous stage.ConclusionsOur findings highlight the crucial roles of RBPs in HNSC progression and suggest their potential as therapeutic targets and prognostic markers, offering insights into personalized treatment strategies.

Guideline-recommended screening programs exist for only a few single-cancer types, and these cancers represent less than one-half of all new cancer cases diagnosed each year in the U.S. In addition, these "single-cancer" standard of care (SoC) screening tests vary in accuracy, adherence, and effectiveness, though all are generally understood to lead to reductions in cancer-related mortality. Recent advances in high-throughput technologies and machine learning have facilitated the development of blood-based multi-cancer early detection (MCED) tests. The opportunity for early detection of multiple cancers with a single blood test holds promise in addressing the current unmet need in cancer screening. By complementing existing SoC screening, MCED tests have the potential to detect a wide range of cancers at earlier stages when patients are asymptomatic, enabling more effective treatment options and improved cancer outcomes. MCED tests are positioned to be utilized as a complementary screening tool to improve screening adherence at the population level, to broaden screening availability for individuals who are not adherent with SoC screening programs, as well as for those who may harbor cancers that do not have SoC testing available. Published work to date has primarily focused on test performance relating to sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). MCED tests will require approval through the pre-market approval pathway from the United States Food and Drug Administration. Additional studies will be needed to demonstrate clinical utility (i.e., improvements in health outcomes) and establish optimal implementation strategies, (i.e., testing intervals), follow-up and logistics of shared decision making. Here, we propose core attributes of MCED testing for which clinical data are needed to ideally position MCED testing for widespread use in clinical practice.

BackgroundDoublecortin-like kinase 1 (DCLK1) isoforms play distinct roles in the progression of gastrointestinal cancers. For the first time ever, the current study aimed to generate DCLK1-S-specific monoclonal antibodies (mAbs) to evaluate the clinical value of DCLK1-S (short isoform) in gastric cancer (GC).Materials and methodsMice were immunized with a unique 7-mer synthetic peptide of DCLK1-S conjugated with keyhole limpet hemocyanin (KLH). Immunoreactivity of hybridomas and mAbs was determined by ELISA assays and immunohistochemistry (IHC). DCLK1-S expression in two GC cell lines was assessed by flow cytometry. After characterization, the expression pattern of DCLK1-S was investigated in different histological subtypes of GC (n=217) and adjacent normal tissues (n=28) using IHC on tissue microarrays. The association of clinical prognostic values with DCLK1-S expression was also investigated.ResultsELISA findings demonstrated that the generated monoclonal antibody (mAb) exhibited strong immunoreactivity towards the immunizing peptide. Positive control tissues, including GC and colorectal cancer, showed strong positive immunoreactivity with anti-DCLK1-S mAb whereas negative reagent control sections represented no staining, demonstrating the specificity of produced mAb. Flow cytometry analysis confirmed that the newly developed mAbs effectively recognized DCLK1-S on the cell surface. A mixture pattern of membranous, cytoplasmic, and nuclear DCLK1-S expression in the GC cells was observed. A significant and inverse association was identified between the expression DCLK1-S in the cell membrane and cytoplasm and PT stage, muscolarispropia, subserosa, and perineural invasion in intestinal subtype, respectively. In signet ring cell type, however, nuclear DCLK1-S expression was adversely associated with tumor size and PT stage. Furthermore, patients with low DCLK1-S expression had a shorter survival than patients with high expression, however, without a statistically significant association.ConclusionAn efficient and precise tool for detecting DCLK1-S in cancer tissues has been developed. Moreover, DCLK1-S overexpression might be considered a favorable clinical factor in GC patients.

BACKGROUNDN6-methyladenosine (m⁶A) modification has been associated with non-small cell lung cancer (NSCLC) tumorigenesis.OBJECTIVESThis study aimed to determine the functions of Vir-like m⁶A methyltransferase-associated (KIAA1429) and relaxin family peptide receptor 1 (RXFP1) in NSCLC.METHODSA quantitative real-time polymerase chain reaction was used to analyze the mRNA levels of KIAA1429 and RXFP1 in NSCLC. After silencing KIAA1429 or RXFP1 in NSCLC cells, changes in the malignant phenotypes of NSCLC cells were assessed using cell counting kit-8, colony formation, and transwell assays. Finally, the m⁶A modification of RXFP1 mediated by KIAA1429 was confirmed using luciferase, methylated RNA immunoprecipitation, and western blot assays.RESULTSKIAA1429 and RXFP1 were upregulated and downregulated in NSCLC, respectively. Silencing of KIAA1429 attenuated the viability, migration, and invasion of NSCLC cells, whereas silencing of RXFP1 showed the opposite function in NSCLC cells. Moreover, RXFP1 expression was inhibited by KIAA1429 via m⁶A-modification. Therefore, silencing RXFP1 reversed the inhibitory effect of KIAA1429 knockdown in NSCLC cells.CONCLUSIONOur findings confirmed that the KIAA1429/RXFP1 axis promotes NSCLC tumorigenesis. This is the first study to reveal the inhibitory function of RXFP1 in NSCLC via KIAA1429-mediated m⁶A-modification. These findings may help identify new biomarkers for targeted NSCLC therapy.

BackgroundHistologic grading of lung adenocarcinoma (LUAD) is predictive of outcome but is only possible after surgical resection. A radiomic biomarker predictive of grade has the potential to improve preoperative management of early-stage LUAD.ObjectiveValidate a prognostic radiomic score indicative of lung cancer aggression (SILA) in surgically resected stage I LUAD (n =  161) histologically graded as indolent low malignant potential (LMP), intermediate, or aggressive vascular invasive (VI) subtypes.MethodsThe SILA scores were generated from preoperative CT-scans using the previously validated Computer-Aided Nodule Assessment and Risk Yield (CANARY) software.ResultsCox proportional regression showed significant association between the SILA and 7-year recurrence-free survival (RFS) in a univariate (P <  0.05) and multivariate (P <  0.05) model incorporating age, gender, smoking status, pack years, and extent of resection. The SILA was positively correlated with invasive size (spearman r =  0.54, P =  8.0 × 10^-14) and negatively correlated with percentage of lepidic histology (spearman $r=-0.46$, P =  7.1 × 10^-10). The SILA predicted indolent LMP with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.74 and aggressive VI with an AUC of 0.71, the latter remaining significant when invasive size was included as a covariate in a logistic regression model (P <  0.01).ConclusionsThe SILA scoring of preoperative CT scans was prognostic and predictive of resected pathologic grade.